diff --git a/Dockerfile b/Dockerfile
index f407289..ad66173 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -24,7 +24,7 @@ RUN cd /tmp && \
     echo "e1045ee415162f944b6aebfe560b8fee *Miniconda3-${MINICONDA_VERSION}-Linux-x86_64.sh" | md5sum -c - && \
     /bin/bash Miniconda3-${MINICONDA_VERSION}-Linux-x86_64.sh -f -b -p $CONDA_DIR && \
     rm Miniconda3-${MINICONDA_VERSION}-Linux-x86_64.sh && \
-    $CONDA_DIR/bin/conda config --prepend channels conda-forge/label/dev && \
+    $CONDA_DIR/bin/conda config --system --prepend channels conda-forge/label/dev && \
     $CONDA_DIR/bin/conda config --system --prepend channels conda-forge && \
     $CONDA_DIR/bin/conda config --system --set auto_update_conda false && \
     $CONDA_DIR/bin/conda config --system --set show_channel_urls true && \
diff --git a/Pipfile b/Pipfile
index 064e1d1..3d8c5c3 100644
--- a/Pipfile
+++ b/Pipfile
@@ -5,7 +5,9 @@ name = "pypi"
 
 [packages]
 black = "==18.9b0"
+chainer = "==6.0.0b1"
 comet-ml = "==1.0.42"
+cupy-cuda92 = "==6.0.0b1"
 cython = "==0.29.2"
 descartes = "==1.1.0"
 geopandas = {editable = true, ref = "0.4.0-26-g9e584cc", git = "https://github.com/geopandas/geopandas.git"}
@@ -13,11 +15,11 @@ gmt = {editable = true, ref = "0.1a3-131-g9772fa3", git = "https://github.com/we
 ipython = "==7.2.0"
 jupyterlab = "==0.35.4"
 jupytext = "==0.8.6"
-keras = "==2.2.4"
 livelossplot = "==0.2.3"
 matplotlib = "==3.0.2"
 netcdf4 = "==1.4.1"
 numpy = "==1.14.5"
+onnx_chainer = "==1.3.0a1"
 packaging = "==18.0"
 pandas = "==0.23.4"
 pyproj = "==1.9.6"
@@ -25,11 +27,9 @@ quilt = "==2.9.14"
 rasterio = "==1.0.13"
 requests = "==2.21.0"
 scikit-image = "==0.14.1"
-scikit-learn = "==0.20.2"
 shapely = "==1.7a1"
-tensorflow = "==1.10.1"
-tensorflow-gpu = "==1.10.1"
 toolz = "==0.9.0"
+tornado = "==5.1.1"
 tqdm = "==4.28.1"
 
 [dev-packages]
diff --git a/Pipfile.lock b/Pipfile.lock
index 4b0b2c9..913ed5c 100644
--- a/Pipfile.lock
+++ b/Pipfile.lock
@@ -1,7 +1,7 @@
 {
     "_meta": {
         "hash": {
-            "sha256": "3bfc490703949f2d8d6118fc724c7adc2e76ddde1d0f28ae9bde2aa15559846f"
+            "sha256": "4f82cf471c151a352d5f20bdcd0effb20258671be37d72def7312877cd106fba"
         },
         "pipfile-spec": 6,
         "requires": {
@@ -16,12 +16,6 @@
         ]
     },
     "default": {
-        "absl-py": {
-            "hashes": [
-                "sha256:87519e3b91a3d573664c6e2ee33df582bb68dca6642ae3cf3a4361b1c0a4e9d6"
-            ],
-            "version": "==0.6.1"
-        },
         "affine": {
             "hashes": [
                 "sha256:e5970e2e53edd75fee60eb2550df365a1c3a58d78755e9e5164e345ac36df322",
@@ -36,13 +30,6 @@
             ],
             "version": "==1.4.3"
         },
-        "astor": {
-            "hashes": [
-                "sha256:95c30d87a6c2cf89aa628b87398466840f0ad8652f88eb173125a6df8533fb8d",
-                "sha256:fb503b9e2fdd05609fbf557b916b4a7824171203701660f0c55bbf5a7a68713e"
-            ],
-            "version": "==0.7.1"
-        },
         "attrs": {
             "hashes": [
                 "sha256:10cbf6e27dbce8c30807caf056c8eb50917e0eaafe86347671b57254006c3e69",
@@ -67,10 +54,10 @@
         },
         "bleach": {
             "hashes": [
-                "sha256:48d39675b80a75f6d1c3bdbffec791cf0bbbab665cf01e20da701c77de278718",
-                "sha256:73d26f018af5d5adcdabf5c1c974add4361a9c76af215fe32fdec8a6fc5fb9b9"
+                "sha256:213336e49e102af26d9cde77dd2d0397afabc5a6bf2fed985dc35b5d1e285a16",
+                "sha256:3fdf7f77adcf649c9911387df51254b813185e32b2c6619f690b593a617e19fa"
             ],
-            "version": "==3.0.2"
+            "version": "==3.1.0"
         },
         "certifi": {
             "hashes": [
@@ -110,6 +97,13 @@
             ],
             "version": "==1.0.3.4"
         },
+        "chainer": {
+            "hashes": [
+                "sha256:7d21fbd78d897ffb08f3c7bc9b4a2bfb720fc26b671454e664dd9ef36b10316c"
+            ],
+            "index": "pypi",
+            "version": "==6.0.0b1"
+        },
         "chardet": {
             "hashes": [
                 "sha256:84ab92ed1c4d4f16916e05906b6b75a6c0fb5db821cc65e70cbd64a3e2a5eaae",
@@ -161,11 +155,18 @@
             "index": "pypi",
             "version": "==1.0.42"
         },
-        "configobj": {
+        "cupy-cuda92": {
             "hashes": [
-                "sha256:a2f5650770e1c87fb335af19a9b7eb73fc05ccf22144eb68db7d00cd2bcb0902"
+                "sha256:02c3fdfcb757a923fbc01215ade695974c30ff08b2d8974fd1cecae0c55bba4c",
+                "sha256:14c74c2648aa9eccd2be36b0b8a56cace48f8f146a8ce43e7a98ec62ca7fd4b1",
+                "sha256:41f99af7f22d38cd047db2678a0c23a3789e7b9aa97d156f7e037a336938d1c9",
+                "sha256:6eb750762b24475b1421e1d9419ef9ac1be2a5c92827aa091972b78101467638",
+                "sha256:7e26f14660318f44bb8e8b75cd81c8d5bec9b96c1dda223f3cab1f563cbcadd9",
+                "sha256:87fba3d508057920d9cdbc6c7bf1922e195afdf97d58041075128287caad011e",
+                "sha256:e4e206200ee69b8274883308d3c635c19a474ef8dd5e9155aacd21fcc19a81ca"
             ],
-            "version": "==5.0.6"
+            "index": "pypi",
+            "version": "==6.0.0b1"
         },
         "cycler": {
             "hashes": [
@@ -213,10 +214,10 @@
                 "array"
             ],
             "hashes": [
-                "sha256:8a2c151d5862627c71fdc725760d710b7c037ec57730f453f392b896febfd0d5",
-                "sha256:a1fa4a3b2d7ce4dd0c68db4b68dadf2c283ff54d98bd72c556fc462000449ff7"
+                "sha256:21838b1144830ddf9d1f1acd59784bcdb944c315f0d000fff58d7b6a9a6c3317",
+                "sha256:e76088e8931b326c05a92d2658e07b94a6852b42c13a7560505a8b2354871454"
             ],
-            "version": "==1.0.0"
+            "version": "==1.1.0"
         },
         "decorator": {
             "hashes": [
@@ -243,17 +244,42 @@
         },
         "entrypoints": {
             "hashes": [
-                "sha256:10ad569bb245e7e2ba425285b9fa3e8178a0dc92fc53b1e1c553805e15a8825b",
-                "sha256:d2d587dde06f99545fb13a383d2cd336a8ff1f359c5839ce3a64c917d10c029f"
+                "sha256:589f874b313739ad35be6e0cd7efde2a4e9b6fea91edcc34e58ecbb8dbe56d19",
+                "sha256:c70dd71abe5a8c85e55e12c19bd91ccfeec11a6e99044204511f9ed547d48451"
             ],
-            "version": "==0.2.3"
+            "version": "==0.3"
         },
         "everett": {
             "hashes": [
-                "sha256:02a43a2b4194e6ed40757851b37f5acf3c086f37e7e8109a385a7198cfbbc51b",
-                "sha256:05b0d0bae138a3b1a7c365bdd991c0a5b06f20c2e01d6721896098ac93cf7ee9"
+                "sha256:35f69f6d8e45b2250a3d4b06b8e7f537d3cb296dae9a3ec4a4791258fe4de6eb",
+                "sha256:860011cc71520fe27c7b9e2539b72cc6df2e235705489ad47935b8da83c9b855"
+            ],
+            "version": "==1.0.1"
+        },
+        "fastrlock": {
+            "hashes": [
+                "sha256:0888072e2c6da8d72c774ecdcaa96395f354b2eb4d20409284aaffe9c1b83002",
+                "sha256:24dc4ade9d4fd410feb37748357b973be39703ca421faaf1851dcaaedf7f2045",
+                "sha256:2fcd6964340e1d0250f4e252febf495230fb948f8b8ed6e7010f816cd0d8fad8",
+                "sha256:3a8010de71a3ef18e31b282e08d2313c9a458e786d0d9773a2364605448e2691",
+                "sha256:5f4274edcc46e2b4464825a9e0ff7dd76c56245883d123413e6d54f1d862e08c",
+                "sha256:6a7e9cbc69a31502b00a436e5ee8255b5f9071656f99dca792170c523152df37",
+                "sha256:6abdbb35205792e2d2a8c441aaa41a613d43ee2d88b3af4fd9735ae7a5f7db6b",
+                "sha256:7aff59c126da8665648f9f5da70f4244aee46be1376b5ee07e0beaaf6ce9e721",
+                "sha256:905f6ae55e89b76f0e0353e8ad4440700ae3ebf89128c6b2ed406a4c2365827a",
+                "sha256:99408074357e1c5c6da68bfabe7c2fb61d1e7938da991ea0ba741fb3f20a5763",
+                "sha256:b96c177fc17be6f826ffac291ceb9b4326e048f930d99f27c76b13828fd12e78",
+                "sha256:e3c2348f215e0a1e5922e62df504f6f0b6d19a68abaffa6e9a92e2a7446155be",
+                "sha256:f89ae944157050242a9b1badbdcae449c8426cb0351cc567f8b9ef3298d6d7c6"
             ],
-            "version": "==0.9"
+            "version": "==0.4"
+        },
+        "filelock": {
+            "hashes": [
+                "sha256:b8d5ca5ca1c815e1574aee746650ea7301de63d87935b3463d26368b76e31633",
+                "sha256:d610c1bb404daf85976d7a82eb2ada120f04671007266b708606565dd03b5be6"
+            ],
+            "version": "==3.0.10"
         },
         "fiona": {
             "hashes": [
@@ -277,12 +303,6 @@
             ],
             "version": "==0.17.1"
         },
-        "gast": {
-            "hashes": [
-                "sha256:7068908321ecd2774f145193c4b34a11305bd104b4551b09273dfd1d6a374930"
-            ],
-            "version": "==0.2.0"
-        },
         "geopandas": {
             "editable": true,
             "git": "https://github.com/geopandas/geopandas.git",
@@ -293,76 +313,6 @@
             "git": "https://github.com/weiji14/gmt-python.git",
             "ref": "9772fa3d5825175a8760e57f1d6c39afeee20e4f"
         },
-        "grpcio": {
-            "hashes": [
-                "sha256:082bc981d6aabfdb26bfdeab63f5626df3d2c5ac3a9ae8533dfa5ce73432f4fe",
-                "sha256:0e8ff79b12b8b07198dd847974fc32a4ed8c0d52d5224fabb9d28bf4c2e3f4a9",
-                "sha256:11c8026a3d35e8b9ad6cda7bf4f5e51b9b82e7f29a590ad194f63957657fa808",
-                "sha256:145e82aec0a643d7569499b1aa0d5167c99d9d26a2b8c4e4b3f5cd51b99a8cdc",
-                "sha256:1a820ebf0c924cbfa299cb59e4bc9582a24abfec89d9a36c281d78fa941115ae",
-                "sha256:284bee4657c4dd7d48835128b31975e8b0ea3a2eeb084c5d46de215b31d1f8f5",
-                "sha256:2a8b6b569fd23f4d9f2c8201fd8995519dfbddc60ceeffa8bf5bea2a8e9cb72c",
-                "sha256:38b93080df498656aea1dbab632e32013c580c2d00bd8c30d0f1d2c9513b0469",
-                "sha256:4837ad8fdcf99df0e89214ba42001469cab807851f30481db41fd84fc9358ce7",
-                "sha256:5447336edd6fea8ab35eca34ff5289e369e22c375bc2ac8156a419fa467949ac",
-                "sha256:57705e31f76db45b51f3a98bcfd362c89d58e99f846337a25fed957b4d43ae4f",
-                "sha256:612e742c748df51c921a7eefd76195d76467e3cc00e084e089af5b111d8210b7",
-                "sha256:62c777f801aee22100d8ea5fa057020e37b65541a8000091879a8560b089da9d",
-                "sha256:8317d351ab1e80cf20676ef3d4929d3e760df10e6e5c289283c36c4c92ca61f7",
-                "sha256:8703efaf03396123426fdea08b369712df1248fa5fdfdbee3f87a410f52e9bac",
-                "sha256:8b72721e64becd4a3e9580f12dbdf618d41e80d3ae7585dc8a921dbf76c979bb",
-                "sha256:8bb7dbe20fe883ee22a6cb2c1317ea228b75a3ef60f3749584ee2634192e3452",
-                "sha256:9a7ed6160e6c14058b4676aac68a8bf268f171f4c371ff0a0c0ab81b90803f70",
-                "sha256:a46c34768f292fa0d97e929591e51ec20dc857321d83b198de1dad9c8183e8cb",
-                "sha256:a7f21a7b48fcd9f51029419b22a9bfea097973cca5d1529b8578f1d2919e6b23",
-                "sha256:adfee9c9099cae92c2a4948bc95cc2cc3185cdf59b371e056b8dd19ed434247e",
-                "sha256:b3bbeadc6b99e4a42bf23803f5e9b292f23f3e37cc7f75a9f5efbfa9b812abc1",
-                "sha256:b51d49d89758ea45841130c5c7be79c68612d8834bd600994b8a2672c59dc9b9",
-                "sha256:cbb95a586fdf3e795eba28b4acc75fdfdb59a14df62e747fe8bc4572ef37b647",
-                "sha256:cdea5595b30f027e6603887b71f343ca5b209da74b910fe04fc25e1dfe6df263",
-                "sha256:d64350156dc4b21914409e0c93ffeeb4ceba193716fb1ae570df699383c4cd63",
-                "sha256:e10bbef59706a90672b295c0f82dcb6329d829643b8dd7c3bd120f89a093d740",
-                "sha256:e68e6afbbae2cbfadaabd33ee40314963cd83500feff733c07edb172674a7f8b",
-                "sha256:f0c0e48c255a63fec78be2f240ff5a3bd4291b1f83976895f6ee0085362568d0",
-                "sha256:f7bb6617bae5e7333e66ec1e7aac1fe419b59e0e34a8717f97e1ce2791ab9d3a",
-                "sha256:fa6e14bce7ad5de2363abb644191489ddfffcdb2751337251f7ef962ab7e3293",
-                "sha256:fd6774bbb6c717f725b39394757445ead4f69c471118364933aadb81a4f16961"
-            ],
-            "version": "==1.17.1"
-        },
-        "h5py": {
-            "hashes": [
-                "sha256:05750b91640273c69989c657eaac34b091abdd75efc8c4824c82aaf898a2da0a",
-                "sha256:082a27208aa3a2286e7272e998e7e225b2a7d4b7821bd840aebf96d50977abbb",
-                "sha256:08e2e8297195f9e813e894b6c63f79372582787795bba2014a2db6a2de95f713",
-                "sha256:0dd2adeb2e9de5081eb8dcec88874e7fd35dae9a21557be3a55a3c7d491842a4",
-                "sha256:0f94de7a10562b991967a66bbe6dda9808e18088676834c0a4dcec3fdd3bcc6f",
-                "sha256:106e42e2e01e486a3d32eeb9ba0e3a7f65c12fa8998d63625fa41fb8bdc44cdb",
-                "sha256:1606c66015f04719c41a9863c156fc0e6b992150de21c067444bcb82e7d75579",
-                "sha256:1854c4beff9961e477e133143c5e5e355dac0b3ebf19c52cf7cc1b1ef757703c",
-                "sha256:1e9fb6f1746500ea91a00193ce2361803c70c6b13f10aae9a33ad7b5bd28e800",
-                "sha256:2cca17e80ddb151894333377675db90cd0279fa454776e0a4f74308376afd050",
-                "sha256:30e365e8408759db3778c361f1e4e0fe8e98a875185ae46c795a85e9bafb9cdf",
-                "sha256:3206bac900e16eda81687d787086f4ffd4f3854980d798e191a9868a6510c3ae",
-                "sha256:3c23d72058647cee19b30452acc7895621e2de0a0bd5b8a1e34204b9ea9ed43c",
-                "sha256:407b5f911a83daa285bbf1ef78a9909ee5957f257d3524b8606be37e8643c5f0",
-                "sha256:4162953714a9212d373ac953c10e3329f1e830d3c7473f2a2e4f25dd6241eef0",
-                "sha256:5fc7aba72a51b2c80605eba1c50dbf84224dcd206279d30a75c154e5652e1fe4",
-                "sha256:713ac19307e11de4d9833af0c4bd6778bde0a3d967cafd2f0f347223711c1e31",
-                "sha256:71b946d80ef3c3f12db157d7778b1fe74a517ca85e94809358b15580983c2ce2",
-                "sha256:8cc4aed71e20d87e0a6f02094d718a95252f11f8ed143bc112d22167f08d4040",
-                "sha256:9d41ca62daf36d6b6515ab8765e4c8c4388ee18e2a665701fef2b41563821002",
-                "sha256:a744e13b000f234cd5a5b2a1f95816b819027c57f385da54ad2b7da1adace2f3",
-                "sha256:b087ee01396c4b34e9dc41e3a6a0442158206d383c19c7d0396d52067b17c1cb",
-                "sha256:b0f03af381d33306ce67d18275b61acb4ca111ced645381387a02c8a5ee1b796",
-                "sha256:b9e4b8dfd587365bdd719ae178fa1b6c1231f81280b1375eef8626dfd8761bf3",
-                "sha256:c5dd4ec75985b99166c045909e10f0534704d102848b1d9f0992720e908928e7",
-                "sha256:d2b82f23cd862a9d05108fe99967e9edfa95c136f532a71cb3d28dc252771f50",
-                "sha256:e58a25764472af07b7e1c4b10b0179c8ea726446c7141076286e41891bf3a563",
-                "sha256:f3b49107fbfc77333fc2b1ef4d5de2abcd57e7ea3a1482455229494cf2da56ce"
-            ],
-            "version": "==2.9.0"
-        },
         "idna": {
             "hashes": [
                 "sha256:c357b3f628cf53ae2c4c05627ecc484553142ca23264e593d327bcde5e9c3407",
@@ -408,10 +358,10 @@
         },
         "jsonschema": {
             "hashes": [
-                "sha256:3ae8afd6f4ca6417f14bf43ef61341311598f14234cdb4174fe43d42b236a3c8",
-                "sha256:dfd8426040892c8d0ef6da574085f282569f189cb24b70091a66c21c12d6705e"
+                "sha256:3eae63135c4a2cd15ecfd1424494494be77bd8a27014c44c8c2343e61d908770",
+                "sha256:8ba4f6c03b9db02e51f4a21579b7b0364b7c174361998888fb5d18fab4ed73f1"
             ],
-            "version": "==3.0.0a3"
+            "version": "==3.0.0b1"
         },
         "jupyter-client": {
             "hashes": [
@@ -449,28 +399,6 @@
             "index": "pypi",
             "version": "==0.8.6"
         },
-        "keras": {
-            "hashes": [
-                "sha256:794d0c92c6c4122f1f0fcf3a7bc2f49054c6a54ddbef8d8ffafca62795d760b6",
-                "sha256:90b610a3dbbf6d257b20a079eba3fdf2eed2158f64066a7c6f7227023fd60bc9"
-            ],
-            "index": "pypi",
-            "version": "==2.2.4"
-        },
-        "keras-applications": {
-            "hashes": [
-                "sha256:721dda4fa4e043e5bbd6f52a2996885c4639a7130ae478059b3798d0706f5ae7",
-                "sha256:a03af60ddc9c5afdae4d5c9a8dd4ca857550e0b793733a5072e0725829b87017"
-            ],
-            "version": "==1.0.6"
-        },
-        "keras-preprocessing": {
-            "hashes": [
-                "sha256:90d04c1750bccceef88ac09475c291b4b5f6aa1eaf0603167061b1aa8b043c61",
-                "sha256:ef2e482c4336fcf7180244d06f4374939099daa3183816e82aee7755af35b754"
-            ],
-            "version": "==1.0.5"
-        },
         "kiwisolver": {
             "hashes": [
                 "sha256:0ee4ed8b3ae8f5f712b0aa9ebd2858b5b232f1b9a96b0943dceb34df2a223bc3",
@@ -511,13 +439,6 @@
             "index": "pypi",
             "version": "==0.2.3"
         },
-        "markdown": {
-            "hashes": [
-                "sha256:c00429bd503a47ec88d5e30a751e147dcb4c6889663cd3e2ba0afe858e009baa",
-                "sha256:d02e0f9b04c500cde6637c11ad7c72671f359b87b9fe924b2383649d8841db7c"
-            ],
-            "version": "==3.0.1"
-        },
         "markupsafe": {
             "hashes": [
                 "sha256:048ef924c1623740e70204aa7143ec592504045ae4429b59c30054cb31e3c432",
@@ -637,27 +558,30 @@
         },
         "netifaces": {
             "hashes": [
-                "sha256:0083ff8d89c559d0da0811c4930cf36e4945da0f03749e0f108678098d7d1607",
-                "sha256:179f2463469fe69c829c96c7b332c7fd3f01652311e36ae11e409e5b34eb9dad",
-                "sha256:19df6feff2af7a9179e42afdd01d79616d85b7ff4401b55ffce2df29d512a017",
-                "sha256:1a4082a52f521ceeaf3d0ff25c61a06d46444f3578f487935652ecc93becf538",
-                "sha256:1edeea7d739b1d716d15214039386e999f2e374aaeac0703092132b4e55ba461",
-                "sha256:2acb23ca092cc53b2b1f374132bbef5dd843767f6b10d31024f958474a1dfe96",
-                "sha256:38969c101f1e61c2a53af6a7b635f63e81085ae87413f1f5551a4d7057f5f773",
-                "sha256:4817871b226082600b64578549b9932bb07c1a42e9311ddd7c9dad08ff1fb22f",
-                "sha256:4bb6b02b7c485a595a9d75346df3a77fcaa12d2352437c49c2d73ed968572d72",
-                "sha256:674498dad41dacd86ec82e9e1793f9d8716755085c3776f051a266b1634a0b60",
-                "sha256:7ea8eb1e824f74c161396f0d6d76fa3943462ee9a4629c387c10399d2aee058c",
-                "sha256:8a69dc2743dcbb9b87fa3453820852f0feabc17b03d3841619e8e63f5d3902d5",
-                "sha256:9cf8cb2de7524c34808e6111dfb9f89e3b7c568e6953b3e02b8397447a6d8303",
-                "sha256:a77263e046636a761a2c3eeb0a56b5f8fa64f865efec91a9be008a46412b4ddd",
-                "sha256:aea569ce1a5a75b010758097199f84d9a3a109a696473c635bcf82f8a43cc551",
-                "sha256:bd590fcb75421537d4149825e1e63cca225fd47dad861710c46bd1cb329d8cbd",
-                "sha256:e1037cfad0e99a23fb4829f40302f3696395358950ba9f0315363a0e1eb04af6",
-                "sha256:e6d52aee254f9cf6192b54c156c67d54dcf451bec6781580844af892e4bf36bb",
-                "sha256:e76d38d9cff51ecf9fd5b8d0adf63f7b8875e1ac8548ccb52264939e308b771e"
-            ],
-            "version": "==0.10.7"
+                "sha256:078986caf4d6a602a4257d3686afe4544ea74362b8928e9f4389b5cd262bc215",
+                "sha256:0c4304c6d5b33fbd9b20fdc369f3a2fef1a8bbacfb6fd05b9708db01333e9e7b",
+                "sha256:2dee9ffdd16292878336a58d04a20f0ffe95555465fee7c9bd23b3490ef2abf3",
+                "sha256:3095218b66d359092b82f07c5422293c2f6559cf8d36b96b379cc4cdc26eeffa",
+                "sha256:30ed89ab8aff715caf9a9d827aa69cd02ad9f6b1896fd3fb4beb998466ed9a3c",
+                "sha256:4921ed406386246b84465950d15a4f63480c1458b0979c272364054b29d73084",
+                "sha256:563a1a366ee0fb3d96caab79b7ac7abd2c0a0577b157cc5a40301373a0501f89",
+                "sha256:5b3167f923f67924b356c1338eb9ba275b2ba8d64c7c2c47cf5b5db49d574994",
+                "sha256:6d84e50ec28e5d766c9911dce945412dc5b1ce760757c224c71e1a9759fa80c2",
+                "sha256:755050799b5d5aedb1396046f270abfc4befca9ccba3074f3dbbb3cb34f13aae",
+                "sha256:75d3a4ec5035db7478520ac547f7c176e9fd438269e795819b67223c486e5cbe",
+                "sha256:7a25a8e28281504f0e23e181d7a9ed699c72f061ca6bdfcd96c423c2a89e75fc",
+                "sha256:7cc6fd1eca65be588f001005446a47981cbe0b2909f5be8feafef3bf351a4e24",
+                "sha256:86b8a140e891bb23c8b9cb1804f1475eb13eea3dbbebef01fcbbf10fbafbee42",
+                "sha256:ad10acab2ef691eb29a1cc52c3be5ad1423700e993cc035066049fa72999d0dc",
+                "sha256:b2ff3a0a4f991d2da5376efd3365064a43909877e9fabfa801df970771161d29",
+                "sha256:b47e8f9ff6846756be3dc3fb242ca8e86752cd35a08e06d54ffc2e2a2aca70ea",
+                "sha256:da298241d87bcf468aa0f0705ba14572ad296f24c4fda5055d6988701d6fd8e1",
+                "sha256:db881478f1170c6dd524175ba1c83b99d3a6f992a35eca756de0ddc4690a1940",
+                "sha256:f0427755c68571df37dc58835e53a4307884a48dec76f3c01e33eb0d4a3a81d7",
+                "sha256:f8885cc48c8c7ad51f36c175e462840f163cb4687eeb6c6d7dfaf7197308e36b",
+                "sha256:f911b7f0083d445c8d24cfa5b42ad4996e33250400492080f5018a28c026db2b"
+            ],
+            "version": "==0.10.9"
         },
         "networkx": {
             "hashes": [
@@ -712,6 +636,30 @@
             ],
             "version": "==7.352.0"
         },
+        "onnx": {
+            "hashes": [
+                "sha256:18256e0099bffa3da7422ff3dd6663e201a875338f31bafa0f4148c5b5c938dd",
+                "sha256:1e0159865d6ebe3f3e7b14d349e9f395b2bb67373cd8b577cd79127245006050",
+                "sha256:3ff4fff42c4088fae401ad51dd1ac8db6b9f06495ed24ce81aad7ebb07db7d1f",
+                "sha256:4072a9234b0fa7ab6c0b9b78d9a5e1d61fb822fbc393da09fa4936c6ff0f690c",
+                "sha256:644c8c4173f1659715d773bf79ea6a870ef691beacd3bae3c0b85dfa00d4e2a9",
+                "sha256:65900d994c4a859e40f262c949a27331499773fd885a73ec8768548f6fd78d1b",
+                "sha256:6610f59ad9ddeded250a33071b5fff6632b127193adf6a00687b5f7ce4e4936d",
+                "sha256:6da872c38e4b414640670730371256c9ac1d270317d07f60d6826123cac32b74",
+                "sha256:9315d8fe8551883a96a815ac1cdcf8b029b2998e620c83b0f35754d5faf2c609",
+                "sha256:d1d1f7ea0cfc6890648343533377af5b31c4c797333976550a48bfde9b88672e",
+                "sha256:fa7d4fe66164303e94e2bf5169f751abdba9f9a565bbeb22f54b5497d1388ebe",
+                "sha256:fd1669a59e319079b50053c149f2a6a1493249619c7cfde77273f61dc71c6744"
+            ],
+            "version": "==1.3.0"
+        },
+        "onnx-chainer": {
+            "hashes": [
+                "sha256:3f73fa0a446621d95bc234236e65485908dba7bfd9f0557ed5b3595341ced268"
+            ],
+            "index": "pypi",
+            "version": "==1.3.0a1"
+        },
         "packaging": {
             "hashes": [
                 "sha256:0886227f54515e592aaa2e5a553332c73962917f2831f1b0f9b9f4380a4b9807",
@@ -783,38 +731,38 @@
         },
         "pillow": {
             "hashes": [
-                "sha256:00203f406818c3f45d47bb8fe7e67d3feddb8dcbbd45a289a1de7dd789226360",
-                "sha256:0616f800f348664e694dddb0b0c88d26761dd5e9f34e1ed7b7a7d2da14b40cb7",
-                "sha256:1f7908aab90c92ad85af9d2fec5fc79456a89b3adcc26314d2cde0e238bd789e",
-                "sha256:2ea3517cd5779843de8a759c2349a3cd8d3893e03ab47053b66d5ec6f8bc4f93",
-                "sha256:48a9f0538c91fc136b3a576bee0e7cd174773dc9920b310c21dcb5519722e82c",
-                "sha256:5280ebc42641a1283b7b1f2c20e5b936692198b9dd9995527c18b794850be1a8",
-                "sha256:5e34e4b5764af65551647f5cc67cf5198c1d05621781d5173b342e5e55bf023b",
-                "sha256:63b120421ab85cad909792583f83b6ca3584610c2fe70751e23f606a3c2e87f0",
-                "sha256:696b5e0109fe368d0057f484e2e91717b49a03f1e310f857f133a4acec9f91dd",
-                "sha256:870ed021a42b1b02b5fe4a739ea735f671a84128c0a666c705db2cb9abd528eb",
-                "sha256:916da1c19e4012d06a372127d7140dae894806fad67ef44330e5600d77833581",
-                "sha256:9303a289fa0811e1c6abd9ddebfc770556d7c3311cb2b32eff72164ddc49bc64",
-                "sha256:9577888ecc0ad7d06c3746afaba339c94d62b59da16f7a5d1cff9e491f23dace",
-                "sha256:987e1c94a33c93d9b209315bfda9faa54b8edfce6438a1e93ae866ba20de5956",
-                "sha256:99a3bbdbb844f4fb5d6dd59fac836a40749781c1fa63c563bc216c27aef63f60",
-                "sha256:99db8dc3097ceafbcff9cb2bff384b974795edeb11d167d391a02c7bfeeb6e16",
-                "sha256:a5a96cf49eb580756a44ecf12949e52f211e20bffbf5a95760ac14b1e499cd37",
-                "sha256:aa6ca3eb56704cdc0d876fc6047ffd5ee960caad52452fbee0f99908a141a0ae",
-                "sha256:aade5e66795c94e4a2b2624affeea8979648d1b0ae3fcee17e74e2c647fc4a8a",
-                "sha256:b78905860336c1d292409e3df6ad39cc1f1c7f0964e66844bbc2ebfca434d073",
-                "sha256:b92f521cdc4e4a3041cc343625b699f20b0b5f976793fb45681aac1efda565f8",
-                "sha256:bfde84bbd6ae5f782206d454b67b7ee8f7f818c29b99fd02bf022fd33bab14cb",
-                "sha256:c2b62d3df80e694c0e4a0ed47754c9480521e25642251b3ab1dff050a4e60409",
-                "sha256:c5e2be6c263b64f6f7656e23e18a4a9980cffc671442795682e8c4e4f815dd9f",
-                "sha256:c99aa3c63104e0818ec566f8ff3942fb7c7a8f35f9912cb63fd8e12318b214b2",
-                "sha256:dae06620d3978da346375ebf88b9e2dd7d151335ba668c995aea9ed07af7add4",
-                "sha256:db5499d0710823fa4fb88206050d46544e8f0e0136a9a5f5570b026584c8fd74",
-                "sha256:f36baafd82119c4a114b9518202f2a983819101dcc14b26e43fc12cbefdce00e",
-                "sha256:f52b79c8796d81391ab295b04e520bda6feed54d54931708872e8f9ae9db0ea1",
-                "sha256:ff8cff01582fa1a7e533cb97f628531c4014af4b5f38e33cdcfe5eec29b6d888"
-            ],
-            "version": "==5.3.0"
+                "sha256:051de330a06c99d6f84bcf582960487835bcae3fc99365185dc2d4f65a390c0e",
+                "sha256:0ae5289948c5e0a16574750021bd8be921c27d4e3527800dc9c2c1d2abc81bf7",
+                "sha256:0b1efce03619cdbf8bcc61cfae81fcda59249a469f31c6735ea59badd4a6f58a",
+                "sha256:163136e09bd1d6c6c6026b0a662976e86c58b932b964f255ff384ecc8c3cefa3",
+                "sha256:18e912a6ccddf28defa196bd2021fe33600cbe5da1aa2f2e2c6df15f720b73d1",
+                "sha256:24ec3dea52339a610d34401d2d53d0fb3c7fd08e34b20c95d2ad3973193591f1",
+                "sha256:267f8e4c0a1d7e36e97c6a604f5b03ef58e2b81c1becb4fccecddcb37e063cc7",
+                "sha256:3273a28734175feebbe4d0a4cde04d4ed20f620b9b506d26f44379d3c72304e1",
+                "sha256:4c678e23006798fc8b6f4cef2eaad267d53ff4c1779bd1af8725cc11b72a63f3",
+                "sha256:4d4bc2e6bb6861103ea4655d6b6f67af8e5336e7216e20fff3e18ffa95d7a055",
+                "sha256:505738076350a337c1740a31646e1de09a164c62c07db3b996abdc0f9d2e50cf",
+                "sha256:5233664eadfa342c639b9b9977190d64ad7aca4edc51a966394d7e08e7f38a9f",
+                "sha256:5d95cb9f6cced2628f3e4de7e795e98b2659dfcc7176ab4a01a8b48c2c2f488f",
+                "sha256:7eda4c737637af74bac4b23aa82ea6fbb19002552be85f0b89bc27e3a762d239",
+                "sha256:801ddaa69659b36abf4694fed5aa9f61d1ecf2daaa6c92541bbbbb775d97b9fe",
+                "sha256:825aa6d222ce2c2b90d34a0ea31914e141a85edefc07e17342f1d2fdf121c07c",
+                "sha256:9c215442ff8249d41ff58700e91ef61d74f47dfd431a50253e1a1ca9436b0697",
+                "sha256:a3d90022f2202bbb14da991f26ca7a30b7e4c62bf0f8bf9825603b22d7e87494",
+                "sha256:a631fd36a9823638fe700d9225f9698fb59d049c942d322d4c09544dc2115356",
+                "sha256:a6523a23a205be0fe664b6b8747a5c86d55da960d9586db039eec9f5c269c0e6",
+                "sha256:a756ecf9f4b9b3ed49a680a649af45a8767ad038de39e6c030919c2f443eb000",
+                "sha256:b117287a5bdc81f1bac891187275ec7e829e961b8032c9e5ff38b70fd036c78f",
+                "sha256:ba04f57d1715ca5ff74bb7f8a818bf929a204b3b3c2c2826d1e1cc3b1c13398c",
+                "sha256:cd878195166723f30865e05d87cbaf9421614501a4bd48792c5ed28f90fd36ca",
+                "sha256:cee815cc62d136e96cf76771b9d3eb58e0777ec18ea50de5cfcede8a7c429aa8",
+                "sha256:d1722b7aa4b40cf93ac3c80d3edd48bf93b9208241d166a14ad8e7a20ee1d4f3",
+                "sha256:d7c1c06246b05529f9984435fc4fa5a545ea26606e7f450bdbe00c153f5aeaad",
+                "sha256:e9c8066249c040efdda84793a2a669076f92a301ceabe69202446abb4c5c5ef9",
+                "sha256:f227d7e574d050ff3996049e086e1f18c7bd2d067ef24131e50a1d3fe5831fbc",
+                "sha256:fc9a12aad714af36cf3ad0275a96a733526571e52710319855628f476dcb144e"
+            ],
+            "version": "==5.4.1"
         },
         "prometheus-client": {
             "hashes": [
@@ -861,20 +809,20 @@
         },
         "pyarrow": {
             "hashes": [
-                "sha256:08cf372e4b6147afc020c4b803e0141b1a64b149e3e0db606a87c9b727880ce8",
-                "sha256:23788dba72cb365435630142537b327577c20944060be6ab012bb81f8379e18b",
-                "sha256:2e315224f8a8da69e50310ed3543cac40527dfa9a6d67c2285677ee40cb6bb3f",
-                "sha256:36746973e7d82afe6e78e46968e9236351094d0fb943e817f7a6972bb5d6d574",
-                "sha256:55ec39ae2c302e1e2c98008f1e69dc0d1a7efacdd15a9b9e3d04d25006989cd5",
-                "sha256:5b7cb30bf43b5e485346c90fbb5c61ac5fd3f4476c16637196b36e8d1f2c89af",
-                "sha256:a5519aac76168ed0b1ec37150b3c66e9d74a0838e210c6437c04c1caa3fdb9c6",
-                "sha256:ab9e9bb53a11a55ae76c0384d0fd628c3013f5d222c9ab43e7e3bc90dbd36d9e",
-                "sha256:b82edbd225b6f1b4c6512947aeda38a7b439027166574d6c429b9dc4b35e0e6c",
-                "sha256:e74daadd14c6e8c5822b9dca09f6c388c4588a0c8f67ebd5dc741ea85662b43c",
-                "sha256:f1ddc694375c985b350e545e9f33b3a86da4ddc40289cfca463ebffbb1d24d2a",
-                "sha256:ff723618043421e05a302a1dd7169dfaa9a6a8ec87255be62407db9a205ed68e"
-            ],
-            "version": "==0.11.1"
+                "sha256:1179d450955caf1ed85ac83b38794dffbb4939a07f4a786c17525e6d67e48b4a",
+                "sha256:2f3c1b3929dfcc43b1705a97d090e576a4d24640ed7db03a0a86936315503812",
+                "sha256:33d17f90e51ddf27789ac0b57f42479df13ba06cf7b44673815bb4c15b268b55",
+                "sha256:5b151650b67ad36c91aaa74c4cdf9602b91f3e2b5ee133ddb3ffa51e0b42c84c",
+                "sha256:8ddd3aa357990e94a744ca023b42ba341e4b7d564260cba25c9baba11d9100b0",
+                "sha256:a9197d14e0f9f3aadcd759438145b605c00b6faab00a5f049213c02c160286c6",
+                "sha256:a9ca4c3841a9eeb1e5f65b897ce5cba7dd0484c23d4f83ecc8843fe6e83796bf",
+                "sha256:ad0a3b5dec11719a2b96ec2aa5e3dc37126de228ca7713ae1d0dbbc6d80681e9",
+                "sha256:b4352bf16d76a4dc9396fd664d1dcce40acaa10d0d1626e13c51a94532804b53",
+                "sha256:bf66738b559c10b4899b96b90cbcf8234bb9285c6d69e511ee4e1df936c81133",
+                "sha256:d6aca88ca466c8b08847386862f17a02855bdfb0f5145c93d75d97a8fc65c666",
+                "sha256:e4b879fb34706418ca70e4b86c05e2b0a082650cbd2444d5dfd069724bb90145"
+            ],
+            "version": "==0.12.0"
         },
         "pygments": {
             "hashes": [
@@ -885,13 +833,23 @@
         },
         "pyparsing": {
             "hashes": [
-                "sha256:40856e74d4987de5d01761a22d1621ae1c7f8774585acae358aa5c5936c6c90b",
-                "sha256:f353aab21fd474459d97b709e527b5571314ee5f067441dc9f88e33eecd96592"
+                "sha256:66c9268862641abcac4a96ba74506e594c884e3f57690a696d21ad8210ed667a",
+                "sha256:f6c5ef0d7480ad048c054c37632c67fca55299990fff127850181659eea33fc3"
             ],
-            "version": "==2.3.0"
+            "version": "==2.3.1"
         },
         "pyproj": {
             "hashes": [
+                "sha256:026074694f9e9a3110013802c5ceb2728070dbdde9f1038609f942845f4207d1",
+                "sha256:25e244b84da0b673e2969fdfe2d98f2f94c74a8baea1dd88928f2cf7c1410cba",
+                "sha256:30739f8f0dc266563643799609c5d404d48d6b412bdba1d2fef8eed7f5782c5f",
+                "sha256:379cf8afd80f254dc7ee30c2a7a499e71bcc0f33c435e46b6c0ea30496faacb2",
+                "sha256:569c764b391e31d4b156acb09acde9afb0c1bf1a71ca6e829e4677220ca64a56",
+                "sha256:56dc74a5aa0878d2332e4edd931687e5b8fb18edd242cf8cff60217ce4ef0720",
+                "sha256:5b1553d80b35c6582a79252fc2a4e5d82d95383fbbfc671650d6fe54e18bbb9c",
+                "sha256:629acc34d8c2ff6fa2875e6075555fcb17a033cd3e181613e8782110fcc2f6b1",
+                "sha256:a7fa5da448dcdbd787e70e21dcf6c71a14bc048db86027f2fc3fe005b9440b93",
+                "sha256:c6d7c3c11c9f8f043fb00658f2146c10e3e0e21b5459022ae5716994650d6a02",
                 "sha256:e0c02b1554b20c710d16d673817b2a89ff94738b0b537aead8ecb2edc4c4487b"
             ],
             "index": "pypi",
@@ -899,9 +857,9 @@
         },
         "pyrsistent": {
             "hashes": [
-                "sha256:59880cc33ac293515892b2969aa8f4ed2cec592cbd0be4c4e20f2410468bbc62"
+                "sha256:5a3827d57ad3e46820e5ee4ed5b9e0ee7bc4686df6634a7368bc1863a5c48a77"
             ],
-            "version": "==0.14.8"
+            "version": "==0.14.9"
         },
         "python-dateutil": {
             "hashes": [
@@ -912,10 +870,10 @@
         },
         "pytz": {
             "hashes": [
-                "sha256:31cb35c89bd7d333cd32c5f278fca91b523b0834369e757f4c5641ea252236ca",
-                "sha256:8e0f8568c118d3077b46be7d654cc8167fa916092e28320cde048e54bfc9f1e6"
+                "sha256:32b0891edff07e28efe91284ed9c31e123d84bea3fd98e1f72be2508f43ef8d9",
+                "sha256:d5f05e487007e29e03409f9398d074e158d920d36eb82eaf66fb1136b0c5374c"
             ],
-            "version": "==2018.7"
+            "version": "==2018.9"
         },
         "pywavelets": {
             "hashes": [
@@ -1053,40 +1011,6 @@
             "index": "pypi",
             "version": "==0.14.1"
         },
-        "scikit-learn": {
-            "hashes": [
-                "sha256:05d061606657af85365b5f71484e3362d924429edde17a90068960843ad597f5",
-                "sha256:071317afbb5c67fa493635376ddd724b414290255cbf6947c1155846956e93f7",
-                "sha256:0d03aaf19a25e59edac3099cda6879ba05129f0fa1e152e23b728ccd36104f57",
-                "sha256:1665ea0d4b75ef24f5f2a9d1527b7296eeabcbe3a1329791c954541e2ebde5a2",
-                "sha256:24eccb0ff31f84e88e00936c09197735ef1dcabd370aacb10e55dbc8ee464a78",
-                "sha256:27b48cabacce677a205e6bcda1f32bdc968fbf40cd2aa0a4f52852f6997fce51",
-                "sha256:2c51826b9daa87d7d356bebd39f8665f7c32e90e3b21cbe853d6c7f0d6b0d23b",
-                "sha256:3116299d392bd1d054655fa2a740e7854de87f1d573fa85503e64494e52ac795",
-                "sha256:3771861abe1fd1b2bbeaec7ba8cfca58fdedd75d790f099960e5332af9d1ff7a",
-                "sha256:473ba7d9a5eaec47909ee83d74b4a3be47a44505c5189d2cab67c0418cd030f1",
-                "sha256:621e2c91f9afde06e9295d128cb15cb6fc77dc00719393e9ec9d47119895b0d4",
-                "sha256:645865462c383e5faad473b93145a8aee97d839c9ad1fd7a17ae54ec8256d42b",
-                "sha256:80e2276d4869d302e84b7c03b5bac4a67f6cd331162e62ae775a3e5855441a60",
-                "sha256:84d2cfe0dee3c22b26364266d69850e0eb406d99714045929875032f91d3c918",
-                "sha256:87ea9ace7fe811638dfc39b850b60887509b8bfc93c4006d5552fa066d04ddc7",
-                "sha256:a4d1e535c75881f668010e6e53dfeb89dd50db85b05c5c45af1991c8b832d757",
-                "sha256:a4f14c4327d2e44567bfb3a0bee8c55470f820bc9a67af3faf200abd8ed79bf2",
-                "sha256:a7b3c24e193e8c6eaeac075b5d0bb0a7fea478aa2e4b991f6a7b030fc4fd410d",
-                "sha256:ab2919aca84f1ac6ef60a482148eec0944364ab1832e63f28679b16f9ef279c8",
-                "sha256:b0f79d5ff74f3c68a4198ad5b4dfa891326b5ce272dd064d11d572b25aae5b43",
-                "sha256:bc5bc7c7ee2572a1edcb51698a6caf11fae554194aaab9a38105d9ec419f29e6",
-                "sha256:bc5c750d548795def79576533f8f0f065915f17f48d6e443afce2a111f713747",
-                "sha256:c68969c30b3b2c1fe07c1376110928eade61da4fc29c24c9f1a89435a7d08abe",
-                "sha256:d3b4f791d2645fe936579d61f1ff9b5dcf0c8f50db7f0245ca8f16407d7a5a46",
-                "sha256:dac0cd9fdd8ac6dd6108a10558e2e0ca1b411b8ea0a3165641f9ab0b4322df4e",
-                "sha256:eb7ddbdf33eb822fdc916819b0ab7009d954eb43c3a78e7dd2ec5455e074922a",
-                "sha256:ed537844348402ed53420187b3a6948c576986d0b2811a987a49613b6a26f29e",
-                "sha256:fcca54733e692fe03b8584f7d4b9344f4b6e3a74f5b326c6e5f5e9d2504bdce7"
-            ],
-            "index": "pypi",
-            "version": "==0.20.2"
-        },
         "scipy": {
             "hashes": [
                 "sha256:02cb79ea38114dc480e9b08d6b87095728e8fb39b9a49b449ee443d678001611",
@@ -1159,46 +1083,6 @@
             ],
             "version": "==1.4.2"
         },
-        "tensorboard": {
-            "hashes": [
-                "sha256:64edbe66864e02719f85708ae01efe3448af964c042a502fd2046cc87a3b1f12",
-                "sha256:e4ea6ac2e47bf715b915f08a186e6205fa097318bd73f0b265d437b1d7834484"
-            ],
-            "version": "==1.10.0"
-        },
-        "tensorflow": {
-            "hashes": [
-                "sha256:002ed1550e2fdd82df5939c53737ed8871d21462c354604917dd9f12f44c65ed",
-                "sha256:316bcfda289c40f6ff9ff16ed747744d0b113b577e98e99c839a4da835011dbf",
-                "sha256:34dfc6b017edffc8dfef1b57146edf45a39160dd6f2819449d05251df0181f36",
-                "sha256:3cdebd17ef32ce867ab05b5b9da1b6dea8d54c3d050a03d26373d94ae09d010c",
-                "sha256:4e629651f1570771e525de0208a8b1df8209ca550ce82cf56539b106bceccab3",
-                "sha256:8f9596d3f8cf8eba1f595286d8c43d690add1060eda791f3f337599967700dc2",
-                "sha256:9483e7e4815960797e67e89c0ce968b1f6115ed4cd49961119d943c71da260ac",
-                "sha256:a6aeda09080852f762bdfbde4acbef6d6aa2e729febaee87fd55700e82060cf2",
-                "sha256:b82d124316ce8dea1f8ead72bbd92a83e0fb455b82b4a23d04d392972f820347",
-                "sha256:c94cdd829fbb76d885c95172128e9261bb2b930a75a721b2972ad465ed532aff"
-            ],
-            "index": "pypi",
-            "version": "==1.10.1"
-        },
-        "tensorflow-gpu": {
-            "hashes": [
-                "sha256:2872b24f8c86bd16c8667bed0346b68ba8472f6c4d47c5554c560faf5f5bc64a",
-                "sha256:5b1b4a34c8631b2c30d11c6eb83c9c7c5700c06c51d761e842da1a8f839f51a3",
-                "sha256:bcb1703fe27a1248c7768c2143a33b025872b007bb9699e6282cc98474c3b8c3",
-                "sha256:d7e1dcc722541be88c8fad4219314313d97af129b3e3567c5c20daecfb7f8593",
-                "sha256:e83febd2c21c9cd05d00f11276458ae69af11d3656483018aa9897143669d65d"
-            ],
-            "index": "pypi",
-            "version": "==1.10.1"
-        },
-        "termcolor": {
-            "hashes": [
-                "sha256:1d6d69ce66211143803fbc56652b41d73b4a400a2891d7bf7a1cdf4c02de613b"
-            ],
-            "version": "==1.1.0"
-        },
         "terminado": {
             "hashes": [
                 "sha256:55abf9ade563b8f9be1f34e4233c7b7bde726059947a593322e8a553cc4c067a",
@@ -1208,10 +1092,10 @@
         },
         "testfixtures": {
             "hashes": [
-                "sha256:1e0affc9b459f039ebf9ae6e8af4059ded4d293863d4af9ffcd83e3b5e8df9cc",
-                "sha256:b040b59e0089809c2f157d3463ea288a10d890661695581649f40ae967944829"
+                "sha256:969e967df5d8e12012b5c90986428919b1068c20841b0077b3e29e9a928605d3",
+                "sha256:b6c05222ce8d3c34a1353ff30c73da55f61ef58153229a5664ef7110ec340cdd"
             ],
-            "version": "==6.4.1"
+            "version": "==6.4.3"
         },
         "testpath": {
             "hashes": [
@@ -1244,6 +1128,7 @@
                 "sha256:d4b3e5329f572f055b587efc57d29bd051589fb5a43ec8898c77a47ec2fa2bbb",
                 "sha256:e5f2585afccbff22390cddac29849df463b252b711aa2ce7c5f3f342a5b3b444"
             ],
+            "index": "pypi",
             "version": "==5.1.1"
         },
         "tqdm": {
@@ -1261,6 +1146,22 @@
             ],
             "version": "==4.3.2"
         },
+        "typing": {
+            "hashes": [
+                "sha256:4027c5f6127a6267a435201981ba156de91ad0d1d98e9ddc2aa173453453492d",
+                "sha256:57dcf675a99b74d64dacf6fba08fb17cf7e3d5fdff53d4a30ea2a5e7e52543d4",
+                "sha256:a4c8473ce11a65999c8f59cb093e70686b6c84c98df58c1dae9b3b196089858a"
+            ],
+            "version": "==3.6.6"
+        },
+        "typing-extensions": {
+            "hashes": [
+                "sha256:07b2c978670896022a43c4b915df8958bec4a6b84add7f2c87b2b728bda3ba64",
+                "sha256:f3f0e67e1d42de47b5c67c32c9b26641642e9170fe7e292991793705cd5fef7c",
+                "sha256:fb2cd053238d33a8ec939190f30cfd736c00653a85a2919415cecf7dc3d9da71"
+            ],
+            "version": "==3.7.2"
+        },
         "urllib3": {
             "hashes": [
                 "sha256:61bf29cada3fc2fbefad4fdf059ea4bd1b4a86d2b6d15e1c7c0b582b9752fe39",
@@ -1289,21 +1190,6 @@
             ],
             "version": "==0.54.0"
         },
-        "werkzeug": {
-            "hashes": [
-                "sha256:c3fd7a7d41976d9f44db327260e263132466836cef6f91512889ed60ad26557c",
-                "sha256:d5da73735293558eb1651ee2fddc4d0dedcfa06538b8813a2e20011583c9e49b"
-            ],
-            "version": "==0.14.1"
-        },
-        "wheel": {
-            "hashes": [
-                "sha256:029703bf514e16c8271c3821806a1c171220cc5bdd325cbf4e7da1e056a01db6",
-                "sha256:1e53cdb3f808d5ccd0df57f964263752aa74ea7359526d3da6c02114ec1e1d44"
-            ],
-            "markers": "python_version >= '3'",
-            "version": "==0.32.3"
-        },
         "wurlitzer": {
             "hashes": [
                 "sha256:15a7cb8be359e8ee42093468a60bf462af332088ea62e767af64d83fcc332ac0",
@@ -1313,10 +1199,10 @@
         },
         "xarray": {
             "hashes": [
-                "sha256:0289fe73eb2b0a4bf3e0c670fc232690f7b00b374d4280de0f0faa9c3801b509",
-                "sha256:cb0503a614b5c95702c0468a136c2ce32f9e18c92c9c8d8031413339bb4016dd"
+                "sha256:431e43d8e14cd48dae44932e572fae8d209848ce31d3ff96c82037d0cc3970f3",
+                "sha256:af7147152629701f11e424caf8e4fbf5ea1dc2d03ed7a5ca31b83dd64387cfb2"
             ],
-            "version": "==0.11.1"
+            "version": "==0.11.2"
         },
         "xlrd": {
             "hashes": [
@@ -1428,10 +1314,10 @@
         },
         "jsonschema": {
             "hashes": [
-                "sha256:3ae8afd6f4ca6417f14bf43ef61341311598f14234cdb4174fe43d42b236a3c8",
-                "sha256:dfd8426040892c8d0ef6da574085f282569f189cb24b70091a66c21c12d6705e"
+                "sha256:3eae63135c4a2cd15ecfd1424494494be77bd8a27014c44c8c2343e61d908770",
+                "sha256:8ba4f6c03b9db02e51f4a21579b7b0364b7c174361998888fb5d18fab4ed73f1"
             ],
-            "version": "==3.0.0a3"
+            "version": "==3.0.0b1"
         },
         "jupyter-client": {
             "hashes": [
@@ -1507,10 +1393,10 @@
         },
         "pluggy": {
             "hashes": [
-                "sha256:447ba94990e8014ee25ec853339faf7b0fc8050cdc3289d4d71f7f410fb90095",
-                "sha256:bde19360a8ec4dfd8a20dcb811780a30998101f078fc7ded6162f0076f50508f"
+                "sha256:8ddc32f03971bfdf900a81961a48ccf2fb677cf7715108f85295c67405798616",
+                "sha256:980710797ff6a041e9a73a5787804f848996ecaa6f8a1b1e08224a5894f2074a"
             ],
-            "version": "==0.8.0"
+            "version": "==0.8.1"
         },
         "prompt-toolkit": {
             "hashes": [
@@ -1544,9 +1430,9 @@
         },
         "pyrsistent": {
             "hashes": [
-                "sha256:59880cc33ac293515892b2969aa8f4ed2cec592cbd0be4c4e20f2410468bbc62"
+                "sha256:5a3827d57ad3e46820e5ee4ed5b9e0ee7bc4686df6634a7368bc1863a5c48a77"
             ],
-            "version": "==0.14.8"
+            "version": "==0.14.9"
         },
         "pytest": {
             "hashes": [
@@ -1610,6 +1496,7 @@
                 "sha256:d4b3e5329f572f055b587efc57d29bd051589fb5a43ec8898c77a47ec2fa2bbb",
                 "sha256:e5f2585afccbff22390cddac29849df463b252b711aa2ce7c5f3f342a5b3b444"
             ],
+            "index": "pypi",
             "version": "==5.1.1"
         },
         "traitlets": {
diff --git a/deepbedmap.ipynb b/deepbedmap.ipynb
index 530c96f..efca074 100644
--- a/deepbedmap.ipynb
+++ b/deepbedmap.ipynb
@@ -13,18 +13,11 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import math\n",
     "import os\n",
+    "import typing\n",
     "\n",
     "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n",
     "\n",
@@ -39,7 +32,7 @@
     "import skimage\n",
     "import xarray as xr\n",
     "\n",
-    "import keras\n",
+    "import chainer\n",
     "\n",
     "from features.environment import _load_ipynb_modules"
    ]
@@ -59,7 +52,7 @@
    },
    "outputs": [],
    "source": [
-    "def get_image_and_bounds(filepath: str):\n",
+    "def get_image_and_bounds(filepath: str) -> (np.ndarray, rasterio.coords.BoundingBox):\n",
     "    \"\"\"\n",
     "    Retrieve raster image in numpy array format and\n",
     "    geographic bounds as (xmin, ymin, xmax, ymax)\n",
@@ -68,8 +61,9 @@
     "        groundtruth = data.z.to_masked_array()\n",
     "        groundtruth = np.flipud(groundtruth)  # flip on y-axis...\n",
     "        groundtruth = np.expand_dims(\n",
-    "            np.expand_dims(groundtruth, axis=-1), axis=0\n",
+    "            np.expand_dims(groundtruth, axis=0), axis=0\n",
     "        )  # add extra dimensions (batch and channel)\n",
+    "        assert groundtruth.shape[0:2] == (1, 1)  # check that shape is like (1, 1, h, w)\n",
     "\n",
     "        xmin, xmax = float(data.x.min()), float(data.x.max())\n",
     "        ymin, ymax = float(data.y.min()), float(data.y.max())\n",
@@ -84,20 +78,11 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "BoundingBox(left=-1593714.328, bottom=-164173.7848, right=-1575464.328, top=-97923.7848)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "test_file = \"2007tx\"  # \"istarxx\"\n",
     "test_filepath = f\"highres/{test_file}\"\n",
-    "groundtruth, window_bound = get_image_and_bounds(filepath=f\"{test_filepath}.nc\")\n",
-    "print(window_bound)"
+    "groundtruth, window_bound = get_image_and_bounds(filepath=f\"{test_filepath}.nc\")"
    ]
   },
   {
@@ -117,7 +102,7 @@
    "source": [
     "def get_deepbedmap_model_inputs(\n",
     "    window_bound: rasterio.coords.BoundingBox, padding=1000\n",
-    "):\n",
+    ") -> typing.Dict[str, np.ndarray]:\n",
     "    \"\"\"\n",
     "    Outputs one large tile for each of\n",
     "    BEDMAP2, REMA and MEASURES Ice Flow Velocity\n",
@@ -144,7 +129,11 @@
     "        padding=padding,\n",
     "    )\n",
     "\n",
-    "    return X_tile, W1_tile, W2_tile"
+    "    return (\n",
+    "        np.rollaxis(X_tile, axis=3, start=1),\n",
+    "        np.rollaxis(W1_tile, axis=3, start=1),\n",
+    "        np.rollaxis(W2_tile, axis=3, start=1),\n",
+    "    )"
    ]
   },
   {
@@ -163,10 +152,10 @@
     "    cm_norm: matplotlib.colors.Normalize = None,\n",
     "    title: str = None,\n",
     "):\n",
-    "    # Get x, y, z data\n",
+    "    # Get x, y, z data, assuming image in NCHW format\n",
     "    image = img[0, :, :, :]\n",
-    "    xx, yy = np.mgrid[0 : image.shape[0], 0 : image.shape[1]]\n",
-    "    zz = image[:, :, 0]\n",
+    "    xx, yy = np.mgrid[0 : image.shape[1], 0 : image.shape[2]]\n",
+    "    zz = image[0, :, :]\n",
     "\n",
     "    # Make the 3D plot\n",
     "    ax.view_init(elev=elev, azim=azim)\n",
@@ -223,11 +212,11 @@
    ],
    "source": [
     "fig, axarr = plt.subplots(nrows=1, ncols=3, squeeze=False, figsize=(16, 12))\n",
-    "axarr[0, 0].imshow(X_tile[0, :, :, 0], cmap=\"BrBG\")\n",
+    "axarr[0, 0].imshow(X_tile[0, 0, :, :], cmap=\"BrBG\")\n",
     "axarr[0, 0].set_title(\"BEDMAP2\\n(1000m resolution)\")\n",
-    "axarr[0, 1].imshow(W1_tile[0, :, :, 0], cmap=\"BrBG\")\n",
+    "axarr[0, 1].imshow(W1_tile[0, 0, :, :], cmap=\"BrBG\")\n",
     "axarr[0, 1].set_title(\"Reference Elevation Model of Antarctica\\n(100m resolution)\")\n",
-    "axarr[0, 2].imshow(W2_tile[0, :, :, 0], cmap=\"BrBG\")\n",
+    "axarr[0, 2].imshow(W2_tile[0, 0, :, :], cmap=\"BrBG\")\n",
     "axarr[0, 2].set_title(\"MEaSUREs Ice Velocity\\n(450m, resampled to 500m)\")\n",
     "plt.show()"
    ]
@@ -295,29 +284,23 @@
    },
    "outputs": [],
    "source": [
-    "def load_trained_model(model_inputs: tuple):\n",
+    "def load_trained_model(\n",
+    "    filepath: str = \"model/weights/srgan_generator_model_weights.npz\"\n",
+    "):\n",
     "    \"\"\"\n",
-    "    Creates a custom DeepBedMap neural network model\n",
-    "    according to the shapes of the raster image inputs.\n",
-    "\n",
-    "    Also loads trained parameter weights into the model.\n",
+    "    Builds the Generator component of the DeepBedMap neural network.\n",
+    "    Also loads trained parameter weights into the model from a .npz file.\n",
     "    \"\"\"\n",
     "    srgan_train = _load_ipynb_modules(\"srgan_train.ipynb\")\n",
     "\n",
-    "    X_tile, W1_tile, W2_tile = model_inputs\n",
-    "\n",
-    "    network = srgan_train.generator_network(\n",
-    "        input1_shape=X_tile.shape[1:],\n",
-    "        input2_shape=W1_tile.shape[1:],\n",
-    "        input3_shape=W2_tile.shape[1:],\n",
-    "    )\n",
-    "\n",
-    "    model = keras.models.Model(\n",
-    "        inputs=network.inputs, outputs=network.outputs, name=\"generator_model\"\n",
+    "    model = srgan_train.GeneratorModel(\n",
+    "        inblock_class=srgan_train.DeepbedmapInputBlock,\n",
+    "        resblock_class=srgan_train.ResidualBlock,\n",
+    "        num_residual_blocks=16,\n",
     "    )\n",
     "\n",
     "    # Load trained neural network weights into model\n",
-    "    model.load_weights(filepath=\"model/weights/srgan_generator_model_weights.hdf5\")\n",
+    "    chainer.serializers.load_npz(file=filepath, obj=model)\n",
     "\n",
     "    return model"
    ]
@@ -334,17 +317,10 @@
    "execution_count": 10,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 178ms/step\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "(1, 264, 72, 1)"
+       "(1, 1, 264, 72)"
       ]
      },
      "execution_count": 10,
@@ -353,8 +329,8 @@
     }
    ],
    "source": [
-    "model = load_trained_model(model_inputs=(X_tile, W1_tile, W2_tile))\n",
-    "Y_hat = model.predict(x=[X_tile, W1_tile, W2_tile], verbose=1)\n",
+    "model = load_trained_model()\n",
+    "Y_hat = model.forward(inputs={\"x\": X_tile, \"w1\": W1_tile, \"w2\": W2_tile}).array\n",
     "Y_hat.shape"
    ]
   },
@@ -372,7 +348,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1YAAAK7CAYAAADx8R6DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzsvXm4LUlV5v2uzL332efce+vemhgKqgpRRIGWwWpEPxqxgVZUBG0HQBlabBw+obWxBXECnNDmaydUGhQZlFFURFFBbAYRBERAEBGbQQpqoKY7nbPPHjK+PyJWxsqda++dZ6hz7637/p7nPDtPZGRE5BSZGWvFuySEAEIIIYQQQgghu6c40w0ghBBCCCGEkHMdflgRQgghhBBCyB7hhxUhhBBCCCGE7BF+WBFCCCGEEELIHuGHFSGEEEIIIYTsEX5YEUIIIYQQQsge4YcVIYQYROQlIvKze9j+IyLy4H1s0m2Gs+XY7PUcHxQ7OV4iEkTki27lJt3qnE3nRkSeJSK/l5avEJFTIlLuopxnishv738LyW0NEfmUiDz0Vq7jiSLyN7di+f9BRD62ZP1Zc4/fGvDD6gySbqCt1FnfLCJ/JiKXp3UvEZFxWqd/H0zr7pIeopp+nYj8qYg8zCl/LCKXzKX/Q9r+LnPpz0rpXzGX/kQRmaW6TojIB0TkG9O6B4jIm0XkJhH5vIi8VkTuuP9Hi9xaiMgDReRvReR4Oo/vFJF/f6bbBbTukWvTfXH4TLdL8R4QIYR7hhDeeivV9zAR+T8iclJEbkz34tNFZHhr1LcXzsCxOZyukz+/Nco/E+zX8RKRt4rISJ8vKe2hIvKpjtvXHxjnKyGEfwshHA4hzJblE5EHi8jVc9v+fAjhe27dFpIuiMijReTvROS0iFyfln9ARORMt22e/bjvzPtib7/atYoQwjtCCHc/qPrONvhhdeZ5RAjhMIA7ArgOwK+bdb+UOnL9u/fctsfStvcG8GYAfyQiT5zL80kAj9F/ROTfAdiYb0TqVB4P4Kb0O8+7Ul3HAPwOgNeIyIUALgTwQgB3AXAlgJMAfrfLjpMzj4hcAOBPEa+7iwDcCcCzAWwfcDtERBb1R3qP3AfAfQH82MG17OxBRL4NwB8AeAWAK0MIFwP4DgB3BnD5sm1vhbYc2EN6B/xnxOv2YSJyh4OufMU1vNOybo3jexrAT94K5e4bu7EG7aDss/GaJQeIiDwNwK8C+J8A7gDg9gC+D8D/A2CwYJtb7ZrcK/vZ5+wXvM/4YXXWEEIYIb403WMX214bQvhVAM8C8ItzN9rL0fxQegKAlznF/AfEj7unAni0iLidTAihAvBiAOsAvjCE8OchhNeGEE6EEDYBPB+xkyLnBl8MACGEV4YQZiGErRDCm0IIHwLaI2bzo19pJPwXROQ9yZr5ehG5yOR/QLKG3SIiHxTj1pS2/TkReSeATQB3XdbQEMK1AP4S8QNLy1gTkeeJyL8ly+0LRGQ9rbtEoiX3lmSJe4feGyLypan+WyS6W32TV6c4LhNp/79IRJ4M4DsB/GiylLwhra9dOVL7fkVEPpf+fkVE1tK6B4vI1SLytDRyeo2I/JcF7RAA/wvAc0IILwoh3JSOycdCCE8JIXw85StE5Bki8n+TRes1ej7MuXtCOl43iMiPmzq6bPskEfk3AH+d0l8r0ZJ4XETeLiL3TOlLj42IXCbREmmvlfumNvXT/98tIh+VaM3/SxG5ctn1gdi3vQDAhwB819zxu6+IvF+ipe/VAIZm3UclWeDT/z2J1vf7pf93dA2na+YTqa5Pish3prxfKCJ/nY7tDSLy+yJyzJT1KYnWxw8BOJ3aYa+l+4vIu1I7rhGR58uCfnoBvwbgMSLyhd7KdE5el/b9kyLy1JT+dQCeCeA70rn8oIh8jYj8o9n2zSLyXvP/O0TkUWl54b0m0ar5WyLyRhE5DeBr5tp0RKKF9tfSPTDf5oX9z5Jrdtn5/AIReVs6d28GcIlZN9/3XSQivyvxvr5ZRP5YRA4B+HMAl0n2KLlM2v3oN6VjcUvahy816z4lIj8iIh+SeF+9Ws5Ci/S5hogcBfAcAD8QQviDEMLJEPmHEMJ3hhC2U77WNSkiR0XkZene+LSI/ITkZ0mXZ+TPSPQEOSkibxLjRSQij0tl3ijN/rh135ny5vucup9w2vT29HtLKucrTb7npWv3kyLy8CXH7n4SPZ1OSuzzXy3JG0Hyc+zpInItgN+VOautLO9/Fz6nz1XO6cbflhCRDcTR53fvoZg/BHA7ANYE+24AF6SHWwng0QA80/ITALwBwGvS/49Y0M4egO8BcArAx50sDwLwkV21npwJ/gXATEReKiIPl2iF3CmPB/DdiB/mU8QXOIjInQD8GYCfRbSG/QiA14nIpWbbxwF4MoAjAD69rBIRuTOAhwP4V5P8XMSPw/sA+CJEi9tPpXVPA3A1gEsRRyafCSBIfHF/A4A3Id4vTwHw+yKyI9eFEMILAfw+smXZu2d+HMADUvvuDeD+AH7CrL8DgKOp3U8C8BsLzsHdES1Tr1vRrKcAeBSArwZwGYCbAfzGXJ4HpvIeAuCnzEtdl22/GsCXAvja9P+fA7gb4nF8P+LxWHlsQgifA/AuRCuT8lgAfxBCmIjIIxHP17cgnr93AHjlop2W+NH14FTn78MMJkn8+PhjxEGmiwC8dq7eV8JY9dO+3RBCeP8uruHPI17/Dw8hHAHwVQA+oE0B8AuIx/ZLEa2Mz5rblccA+AZEb4Tp3LoZgB9GfNn/SsTz9wOLjonDZwG8CNEi3SC9yLwBwAcRr8WHAPghEfnaEMJfAPh5AK82nhPvBnC39FLUB/BliB8TRyQObFwF4B0d77XHAvg5xONXD2KIyMUA3gLgnSGEp4YQwoL9cvsfQ33NdjifrwDw94jH+GcQn4uLeDmi98c90779cgjhNGIf9TnjafI5u5GIfDHiNfdDiNf2GwG8QZofyd8O4OsAfAHisX3iknaQbnwlgDUAr++Qd/6a/HXEfvquiNfT4wG4g2BLyvsviNfJAPG6g4jcA8BvIfYhlwG4GLGfx4L7Tun83ER8JwOSh1MI4V3p/68A8DHEa/2XAPyOiDt4MQDwRwBegnjPvBLAN89lu0Nad2Vq1/z2y/pf9zm9Yp/Oavhhdeb5YxG5BcBxAA9DNFErP5K+4vXvpSvK0g78orl0tVo9DMBHER+wNemj7tsAvCKEMEG0nM27Az4gtfNaxIf/N4cQjs+V82WIL7X/Y0U7yVlCCOEE4ot2QHzp+ryI/ImI3H4Hxbw8hPDh9FLxkwC+PX3EfxeAN4YQ3hhCqEIIbwbwPgBfb7Z9SQjhIyGEabr2PP5YRE4C+AyA6wH8NFBbcZ4M4IdDCDeFEE4iPogenbabIL5sXRlCmCS/74D4oXMYwHNDCOMQwl8jukM+BvvPdyJama4PIXwe8aX2cWb9JK2fhBDeiDhg4X3g6QjntZogIq9K/cKmiGiZ3wfgx0MIV6cR2GcB+FZpumc8O1kmP4j4In3vHWz7rBDC6RDCFgCEEF6cRn41/73TyHAXXoF0zNO5fHRK07b8Qgjho+kD4+cB3EcWW60eB+BDIYR/AvAqAPcUkfumdQ8A0AfwK+k4/wGA95ptXwHgm1I/CMSXIP2I29E1jPhiXwG4l4ishxCuCSF8JB2rfw0hvDmEsJ2uhf+F+JJm+bUQwmf0+FpCCH8fQnh3ulc+BeB/O9uv4hcAPEKSZdHw7wFcGkJ4TronPoHYHzy6VUJsyxbiMXwQgC9HvI7eieit8AAAHw8h3Ihu99rrQwjvTMd3lNIuA/A2AK8NIdiBCI9F/Y9ir9mF51NErkjH4SfTOXo74kdhC4nziB8O4PtCCDen6+ptK9qpfAeAP0vXwgTA8xA9QL7K5Pm1EMLnkmX6DTBWerJrLkEcMKkHLCRbLrdE5EEmb31NIvbRjwbwY6mv+xSA/w/NfnwVvxtC+Jd0Db4G+Xx+K4A/DSG8PfWhP4nYf6yiy3NzFZ8O0fthBuCliM9K77n/AAA9xGtyEkL4QwDvmctTAfjpdN/M912r+t9Fz+lzFn5YnXkeFUI4hmga/UEAb5M8P+B5IYRj5m/Z6BkQRxqBOE/K8nLEl4UnwncD/GbEF4I3pv9/H8DD50Zl353acEkI4QEhhL+yBUhUo/pzAP8thPCOFe0kZxHp5fWJIYQ7A7gX4kvNr+ygiM+Y5U8jdqKXII5efZsdHED8iLvjgm0X8agQR/8fDOBLkD8yLkUcMf57U/5fpHQgDlL8K4A3SXTNekZKvwzAZ9JD07b7Tth/LkNzRPHTKU25MTQtE5uIL6Lz3Jh+62MXQnh06jveD0BfJK9EnGupx+OjiJYO+8C81izb+rpsW58vESlF5LkSXQdPAPhUWtUQy1nC6wB8ZXpJfRDiw1n7jisB/Kppy02IFp9F5+jxyNayzyK+lGt/eRmAz849rOtzEkL417Svj0gfV9+E/IG3o2s4vdx/B+KH4TUSBYm+BABE5PbpY/iz6Xj9HtrHauH9ICJfnFxmrk3b/7yz/VLSB93zEV2iLFciWpzsfj4T/ouW8jbEe/JBafmtiB96X53+B7rda94+fwPix8YLVu/Vwv7HW7/sfF4G4OZ0Dm15HpcDuCmEcHOH9s3T6BPSsfkMmsdk0T1Kds+NAC6xA0UhhK9KfeiNaL4P22vmEsRrar4f38nzYtH5vAzt/uNGrKbLc7Nzm0KcxgH415nXf87X/3kzKNJle3ssFz2nz1n4YXWWEOL8lj9EfJF54C6L+WbEEf2GzGUI4dOIIhZfj+guOM8TEG+of5PoI/taxI7ksV0qTaPIfwXgZ0IIL99l28lZQAjhnxFN/vdKSafRFDvxRAGscMIViCNQNyB2vi+fGxw4FEJ4rq1yB217W2rb81LSDQC2ANzTlH80RKELpNHFp4UQ7or4svzfReQhiJbdy+f8uK/AnCU30dh/aYsirGr/5xBf5mw9n1uQdxkfS+37lhX5PoPohmaP+TB9bKyiy7Z2fx8L4JEAHoroJnOXlC5O3hbppfRNiB8ijwXwKvPw/QyA751ry3oI4W/nyxGRr0J0R/yx9NFxLaKby2PTS9Q1AO405+ZyxVwx6g74SAD/lD62tB07uoZDCH8ZQngY4sv6PyNafoD4IRQA/LsQwgWI1pN515tlx+y3Unl3S9s/09m+C/8TcS7Tl5u0zwD45Nx+HgkhqGXOa9f8h9Xb0P6w6nKveWW/CHGQ5I0S5y0tY1H/45W/7HxeA+DCufrmrxNbzkVi5sit2B9Lo09I1+Xl8Psfsn+8C1Hc5pEd8tpzeAPiNTXfj+v56vKMXMQ1MNdvGti5eEE7FrVvVRv2av3x+s95saRldSztf5c8p89Z+GF1liCRRyKq7H10h9veXkR+ENFF6sfmRgeVJwH4j3OjcToP5iEAvhHRPK1zQX4RvjrgfN13QpwU/PwQQpfRRXIWISJfIlE84c7p/8sRXzB1rt8HADxIYgyXo/AV+b5LRO6RHgrPQZwnM0MckX+EiHxtsm4MJU5qvfMemvwriKpv907X+YsA/LKI3C61/04i8rVp+RslikwIoqvtDNEq8neIo4Y/KiJ9iZPXH4HoQjbPBxHdyu4jcQL5s+bWX4flohuvBPATInKpxAnLPwV/juNS0r4+DcBPi8h/FZELU59xNzStCi8A8HPqMpfq7fIisZttjyC+qNyI+FD/+bn1q44NEC1Dj0d0iXmFSX8B4oeSimEclaiK6PEERFXUeyD3YfdCtHg8HPGFagrgqel8fwviXDfLqwD8JwDfP9eOHV3DqS9+ZHo530Z07dT++Ej6/3jqN3fqMn0EwAkAp5IV7Pt3uD0AIIRwC6Ir04+a5PcAOClxAvp62td7SQ67cB2Au8x9IP0totvq/QG8J0SXxysRP2p1wvxO7rV5fhBxQOENkgRpFrCo//FYeD7TAOT7ADxbRAYi8kAsmGscQrgG0UPjN9O92JfsSnYdgItlsUvsawB8g4g8ROIctKchXiutQQOyf6Tr/tmI5+xbJc4HLETkPgAWfryna+k1iH3jkdQ//nfkfrzLM3IRfwDgGyWGPBkgXr/2HvPuO48PIIqO9UXkKsT+VPk8Yh+0qi9exLsQn50/KFFQ55Fo95+rtl/Y/y55Tp+z8MPqzPMGETmF+MD8OQBPSA8oICtq6d8Nc9veIlG15h8RrVHfFkJ4sVdJCOH/hhDe56x6HIAPhKgEd63+IU4A/jIRuZezjeV7EG/YZ9m2dtpzcjZwEvFF6O/StfRuAB9GfNgjxDkIr0ZUWvt7xPkR87wc0ZJ0LaJL61PTtp9BHB18JmLn/hnEl8ld9zvJlellyAIVT0d0I3i3RPeov0Keo3S39P8pxM79N0MI/yeEMEZ8YXo44mjkbwJ4fLLWzdf3L4gPu79CFGuZD6r4OwDuIdGt6I+dJv8s4svahxDv0/entB0TQng14qT270I8ljcgPvBfiGhlBqKU8J8gulWcRDyfX9EuzWWn274M0aXjswD+CW3hnVXHBqm+uwG4NsQ5XwCAEMIfIQ7uvCqd1w8jnq8G6WP32wH8uu2/QgifRLwun5DO97cgukLfhGgha1ju04vyuxDnubzapO/0Gi4QX7o+l+r6auQPoGcDuB/iy8OfzbehAz+CaNk7iTig8Orl2Zfyq4gvMADql0cdXPsk4rX124iWSCBfXzeKyPvTNqcRr+ePpGMMxGP46RDC9SlP53ttnmS9fDLixPbXy2JlPLf/WVDmqvP5WMRr/ibEgUrPdV55HKIl458RPUV+KNXxz4gDKp9I1751/UUI4WOI9/CvIx6TRyCGlBiD3KqEEH4J8f78UcSPlusQ5yo+Hcs/bJ+CaBX6BOIz4BWI6shdn5GL2vMRAP9vKu8aRMEgGwOtdd8t4CcBfGHa/tkwg0PJze/nALwzXY8P6Nq+tL32n08CcAvitfun6BiSpUP/6z6nd9LGsw0J5/YcMULIGURE3grg90IIv32m20IIOb9g/0PIwSMifwfgBSEExix1oMWKEEIIIYQQ0kJEvlpE7pBcAZ+AGALgL850u85WzvsIyYQQQgghhBCXuyO6nR9CdIf81uQ+TRzoCkgIIXtARL4Occ5KCeC35xTjCCHknIf9HCHd4IcVIYTsEomBSP8FMfj21YiBDx8TYqBaQgg552E/R0h3DtQV8JJLLg5XXLEoLIShES9zUZ5Faqo7z1fNuonxVNP9yzebdCsrzFYfi9ms27GYTbspWM6q1fn284NcpFsoll7ZbUqgFPs3dbDqcCwmk27H/+pbqhtCCJeuzknOIe4P4F9DCJ8AABF5FVIspEUbXHLxxeGKK+bVuvU6s/eCc4/pfWfvmTq6gs0fmvmbhbTKCyYtVLNGuTZ6Q71s09I9Upm+SssIpm+q08w9VWn7Gk0Kjd/Gbqw8PDFRCmnn0aQqJ2o+W5ekjJVJK6Sdz2vGsgBedttirn3ByVeafkzPj9dX2jSvJ9X1XjcbnMYXpl7tS63as14DH7v6NPuz84cd9XPloUOhf+Gx5rWZlgublu6DnrnmemWMdT7o5dfTtZS2Vua0QbomS6vMHSbxp4q/9t1O36Ua/dQs5ZvmtGoa02amn6hmVUqzfWHzLl91b+p+130T8v3a2NbpRLQu2yflLj7MZ3fbOEv7ULW7fxf7uuV1v9Xctt4u2A10/23/UqZKCufY2XevMJe/TNcDAEhRpl/T4FSePa96/u372mTafndbW+unNpm2p/o+9tnNTn3egX5YXXHFFfibt791dcbpTavzTDrkARAmx1fm2TreLS7f6Zs+2S3f9VevznPd6jwAMDq+Ogj3yZtv6VTWTTef7JRvc2u1iuZoNOlUVheGw36nfMeOrooTmcpbG+ylOQ26HItrr+t2/H/4dac+vToXOce4E5pR6K/GCnnzK664M/7mbW9qDiDVSst2UKD9EVNvY8OazDab+QGgGrW31SJm+ZrWj51qlu/nyfaJWOw4hrybTUa5qpQ2HedweJOtGF1hfDLfB7q8bdNOxb54up3L037EPkTHk7iPU/PQG49jWq+XH6hT56E4TYNH2qfYPPpA17IAYDDoOfmk0Q4AGPQ1X/t4Ts3Ho74U2jQtz9ahfVRV6QtTLlfzbayv1Wmaz+7//D7YugpzffR6xcJtveNz6HCOM9obxvBR5VoOIzXb3gIAPPB/vIv92fnDjvq5/oXHcOVTvxeDQX4W99KH0togP+/X0/qLjhyu0y49egEA4PKLc5zcL7jgQgDA3S64KDcoXZPHsJUr3o6x1yen4+/oRJ4GtH3q+ph2/PN12tZN1zd+AWDzxusAAKdO5X7qxMnYx9r3AduPxP3L95fehzZN+5Dh0ByT1F/oPRq3jcten2jfu3S99km2D9H+wrbx5GZs++nt/EWkt7/9SNIPiiPD/GWh6+3HxtYkNNJKs1K7JJt/PX2wbGzkfu2CI7GvGa613wHtsdZ91HfAIxfmuNz9jXjt9Ia53yr78Rjb8zo6cTMA4HPX5G+HG25svxPf9S4xLKTtVw8djVEnHvSM93Tq8/b0YUWfW0IIWY2IPBkxJg8uv/xO8QPJhq2pLUHWku18WGla5XxsNUZu47J4liiDWh9m01Frnf3YUvSDylrlq3F8AFrr1HQUX3aqiR0xTh9x5imuL/Y2rf7YWJG2U+qPjqI9OtrMt9jibV9e9OPFvgBVTtn5Iycfn/kPqqIR/zPmsx92hRMfVF/GGvWnxUG/MGmxLvsRNdqO57ZX2o+t9FE6zeddJjoa3P4oI8Ri+7je0aOYTqeNe0mXrZWgLOI1vrWd+wm97semP5mkvmtm+rOiNkG3B6i07wrOwNIqlt7/Tp+kH0+2X/HK0DR7z+k93DxOWk77Hrb3ujfI067TWsyav7EtqXzTrWoVa72cUT+i7NhMOnXolzqYk9f1l/SxzWMYKzt1Oj9/dNDp8KE8mKODPUX6YNIBHwAoyvZnjD5/vGee/bDVD7XmuWt/FO+UXftMJZ/b30AM/HcPAI8RkXvsuiWEEHLu8VkAl5v/75zSGoQQXhhCuCqEcNUlZiSWEELOAVb2c7aPKw5tgJDzlb1MRql9blNkZfW5JYSQ84X3AribiHyBiAwAPBrAn5zhNhFCyH7Cfo6QjuzFFbCTz23TBeby+dWEEHLOEkKYisgPAvhLRJfoF4cQPrJyQymAUDT/B9B059N1ZrtK51jZrttxGdT1wbjzFTFNbDaZpV8jWCDJ738Q3SS2T99Qryv7w9Tc7CZRJZedyeYpzCPG7UV94Sfj9vxY64oxSO1surjFg2H93ufnGMRyYr56DtPAzFNI8w1sXToXq6p6rXxNl52y8RvrVZe9vB86j8Kirk2eu0+BspHH4rn/NcpN7bSuLdome5zm19l2Wrec7CppJpcn1xt1wQHyHCty/rDTfi6EgPF43LiWdI7VeNwW7hr0ctpWWj82wgOnkkvx2BPqqkx5Sayimix2BWu0sxbUsW66i13s7D0075bsuY7ZPHmuY9s90qL3oecypy68Hl67bRl9FQDpLT8mnre1053UroK6C9b9z+t/NM32q97cUT0+tgx9jmh/ZJ8/umxd0WeTxXPjvflc9png9fU75VYXrwghvBDACwHgfve7L7XdCSG3KUIIbwTwxjPdDkIIubVgP0dIN/byYdVpbgEhhBBLFVX7vInVjYnYVTvNy7dEAdDKDVt1vzotiVbYfFXKp+uCEbGYbkfxCqsAWFVqscoKSzoCvHVztnYpo5EzUd2oV+mIpU1TkYdbjmc1QlXNs6PIqsanKl7eyKkdxT1xYquVT8uzyn6jk+PUtvZoq+XEya3GPth8nmiH1utZ506dzpYhrddTINR9tWkjIwawzPLVUBKbNdsGAEWyFHgjxIQsoqoqbG41LZuqEDidtvuzsVEKVIuVlTZX8YqR6YtqI4MR/FFxHa9f2+l1693fTeW/tgWqzpfuw6JvrDj9vlNG2zqlFnjb/6ka4LwSoS1jFdrONXNcZ0lv3Qqsetap0ovVUIbGOs/6tup46Xpr6VcL/GA9C1T0kgKkKv9ZC7oSVoQdyvXnNg3dfO19XWX5bJWxo9xN6HNLCCGEEEIIIdiDxWrXcwsIIYQQQggh5DbGnuZY7dznVuYmXS/Ktk95ABTl6mCxUnYLUFt0zFc6ZspWWYNuQWy7lOWZov18++fC0dX8vJ90idsAANMVkzOB7vFwugRC3nYClRKylDD1A/96bn/WlavexhGqgIkyP20LDNSTfO1EbZ3kbdoSQvN6tu40nkvELMUM0dghQHYL9CZg2/s4x7GyafHXCjr4cVzaMbBybKn2/a35m+58i/sU60KnohgengCEJ3LhxcBZ9H8sq+0y04w7FbFuNLXLoOmTDh+KzxDPjWiVa5FeM1aExMYmI8RjNpvhlltuaaSpK6CKWAD5Gi/Nta5Bg23aML17Heo570PmXVDf0VSAx1LHtlrhMpbb1o5L5bkM67pmfyGt/MvemTw34fFk4q7Padp3aSwsJ4ZeYx803pZ1U/YEgBbH+2v0dTuMKZiDIXtBk/O7tboADg4frdP6h4409sH2R3o+ZzD90pLXNk+owqPxbNhhn7cXV0BCCCGEEEIIITgAVUBCCCGGENoWq1oe3YyMqYywtVhJGrF1RS5yeTpi61mbdGI3AECtWGZ0tJ4Ann6tNWs6ikIJMzOCN93O4gnzWMuJZx3J+UwdaWTZjix6liUVaPAEJXR01Napo8eeZLKdUK8eAFYoIo/s5lFaTxijbpuxdGs+a/XSejWf3VfPsuZZ9nQyvGfJ9yTYvRHwVaPSap0KxnPCWiYJ8QmoQmhcryqzbi1WurxuPHh0ed0IWhxK1/rACFAUGoeiYbFqynHbvquaJil2Y3X3rFe1Baph7UlWHLFWZDTSPFEGT9BhFZ7YTeUJHS3BC53g7YNbf6rLs6ItS1ttMWtb83V5bZhlJNRSNTx2cc631hStsJ4T+kyy5/UBT3nL0n1U3viMu7fSfG+mgxOvIIQQQgghhBACflgRQgghhBBCyJ6hKyAhhBw4BeDNZZZBI8vcQnZ7aQhaJDc26y1StLas3WLEbKsug1aYp07rRfeMysSO6W8fNLGeAAAgAElEQVTEScTWhUbdNCanT+Xqk8vG+kaORQJEFzLrnqfuIRsbeb91vXXJ8EQb5suIy003Hi9mzLIJ6LHeqpXPc9nT9g0OLY+VVW+zlo+xuu/Vbod28vj64knutk26b9bdStOGa4PWNl7bLbVAhjlm/Y3DcVvjCrh29KKF7SMEiH3M2tqa6wpn0/oax8i4B+q12zduf+tlXN/z4imZXm5ZrCp1H+suXuGIx/Sse3BT5MF1u3Nc4ZYLUfiuePNlxPWL12l7v/vF19dpv/e9lzXW2WXPFdn20+O03uu7PdfF3Mbl+6B9k/YzALB25BgAYP3C2+W0o9EtUN051f3PstNYU7F9bfEOZbwHjR5arAghhBBCCCFkj9BiRQghB4lo2AlnQnJjgC+NezUmGzclduN67cbzEJukgmxxOqJnR3WDI1ChcushWap0lNCWMd3OAgYqXmFFLFT0YGsz59PRzs2tbcxjxR5UNMITrFg1GVzle3Uk1hPM8MJTWEELz4qV17VHlntoj5JbqXhvUrda4LS8xuT0yhvlb48YaztdoQrx9rsthmHT9BwcPnqkta2VWJcl1kNCFpGtyfn6Vkl1GzKgTjPXmVqvCs9iZfrHkNwAZIVAQ7uIsrW8Snhh3hpj+xVPgt3DFarYoYy5h1evWoc8a3azH2ibamqRn2KxVajZXy8Oe9EUAEmWwOFGnaZeEaVJK/vRe8ILeaRCSsuslYvQfnrqhMzZjfBIve2utySEEEIIIYQQAuCctlh1k5/s5HfZ0TfTzjVYhh3NXVhWRwfOLmV1DpzbMZBtly/1vXzN77asrkGJV40SxUzdjlmX4Mtr+xh4mRBCCCGEnJucwx9WhBByDqJxrFYNDnmxrdw4VtNWmsavsoNB6uLXTEuTd5NLIJAHo7QMOzi1ffKWmN9MHta4RnZS+GQzClk04p7M1MUuD5BsbqZ22ngzyaXEDrioe4oVb/AGZDS21RCDVl2a34sx5bmsLIu75dUJGNcWx13OiyOV22Ymj6fjZMvw0rIbkRG+WBKDx4vfdep0jmlWuydu57QyiVaoew4htwav+8+P2/E21588npYct+hduIXVRZRtV8D63nBc4ZaJV+wFzxXao2v9GvfOE69YlVYU01Yd8+2c2D6tavf1Xttqt0sjjqPLxRKXY+v2V6RlKfOz4b0v+HoAwOj4jXXa1skTAJp93ubmdtqHxWI+i/ZjGXQFJIQQQgghhJA9QosVIYScERxRilmWLMcyl9bKWLFmcQRuNh21sllL1EyFKowFSq1Y09GJOm2Slj035HGyWG0fvynXkaxXMyNwoBamEyezZcuzAKmQhV3nWawmyWKztd12x153ZMw99+hsncrrdPKyTdORXWthqkUmbL5Unq2ra76qakrKrxKWyGW1Zdw3Nx1p9zzvuyGkketXufe2Vcyi1kg7omyFLAjxEBH0er19nTIwT21ksP1kWtaQEauorVPG2qHWEM9is0y8YpVVI+dv901N8Yqulqpi7v+2ldpbb6c3eH3Msv1oyMKnNs9CErZwZpp4ohyNdqZ+pTT9i56ThsjSkuk62cJYttIstXjSZhZPUuuVteZ7IStsOJAu0GJFCCGEEEIIIXuEH1aEEEIIIYQQskfoCkgIIQeJSHRZse586h5SDHOSilbYGEd1vkErTWa5PBWosPFcdHk2yy5+dfwqx+2idvEb5fzqBjabZHeK8ak4idxzE7GuIJ5LmrqF2G23p+oy0nb/sF4yWnRpYjapG4eWZ+v3FD69fCqk0ZwMntxdpkaMY6r5smtLHe9lagUl2u6B8/ntOt2HhqCF42fjCWR4k7CHyVXSOxc2zZsE3984DAAo+2utcgnZCfley9f6rKPbm8c0uaChMK+xSbxC3cga/V8tctCOWXX/H/jLOu2dz/uqWGzRdplr9BOS7t3Q3gdVJt4PEYtFZJdpjfHVjqP1hz/8BXWajRW2vFztw2xsv3Y/ubSM0GxjbFO7f+kN12OacQVUt0AvLpU+12aOO3LjvKZl60KofZ4Vrzh+Ki5vjfN+rQ+0nfncDat2/Kxl0GJFCCGEEEIIIXuEFitCCDlIVG5dbPebRvZCW4Cikc+RYJckQBGsZLkjt67iFjatllQ31i4rpQ4AlRn105HC6agtbGFlzFWC3BOlsNYcFbnYnlqLkbYNrbSZGTEt04ii3XaSRCH6zuioJ2jhCWXoSG1DKt5Jy/vjiEc4LKt/2jDmtS1h8+2wy1aCfr5cII/UWguXZ+1Si17DopDOtzfKT8giRIBer9e4llQcZWou9knH2JoeM+0zxVjvU/9YlNHCYK/VWtq7IXIQ87/vRY+o02q5b2NF6TntrPu2qpt9oqu1Z6dkS5CRh0/3smfF8ttmBXjivtq+W8+dtw+lpPqlvc7Wqf3UcJitPxrGwYZzKJJ13LVYTdNzzXhM5O3M+Vpbb5Wh+3XSWKxuOBX3++Qot/3IMO7P+iA/zy7YYbQJWqwIIYQQQgghZI8csMUqNANbLszWJU8331xPgng3eQBgsnVqdSYAk82TK/OMT93SqSwbrHERNkDlMjr7x3bwe+7qO9wlnzd66uZzpEF3W2eBbmV5o8G7yUMIIYScH0jLQjIex/eUkXneD4dxTunXvOS36rQ7XHgMAHD5pZfUaXe98GIAwBcdvahOu2I9zv9Dz8z/0zlWojLqRsbbmWNVLJl31QxC27YK5fmhbRlxD52LZS3x+u7jztOyczyXzNnSfE15+MXzST17StM6H/Pdcvx0K1+zXpVv13rbVr1LLs6mnguOxBgQ/fVDddr6RbcDAAyPXdzatuzl+cbqWaHeFBqAHsjWqca2yepVOAHVrXVKl288beafps+POx3bvYWRb4SEEHLgFKsHkGq3P/PQDe1BFJ2g24xPFbcJjqCFxq6K62PadPu0SUtuH8kNzA4U6Tpbl+c6om6BNhaUJ9QwSWnWnU9d/CYzxz3QHIpBrw5kU6fNUjnTUvPkdlajttufouIUdn8s3suTN8lbsS85elwaLnZp4r260TTr0oni7fK8wbHCiXfmuR16eKIdG8gvqja+TF1fydcGQghZBF0BCSGEEEIIIWSPcOiJEEIOnKppsVLrlCfB7o1/mW2zUEXbmuUJVVST7F48HUdL1XQ7i1Gom8V0ezM1KZerLszjrZxfBSh0gjPgiz2oFWti8tXzv40hZrTdtsqohceyneYv983hUYNSlfJPjDGpn4xIvXK5jPtaT1p1avumDTeeVjG5baNpK5/dVo1mY5WWl3abrGFtkg7UemN9bOfm1nYrzZvI7glvWMuaJ5Gv18Xg8NHclg6u7uT8RgrBYDDA5mYWwhmNYr9jLcaatjXOV/aprZh2wmx74lB0Hzsxztf6KLmATcx92p8Xr7DiDSmtMKED1AXQClp4Ftmy15bbtmI988y7CQLZct1wJ3SszXp8rBW97XaXrdK9eh/MPb8kFIO1Zq8Ny9Q2Ez4jeRlcfzz3+5cc6bfrmAsLMR63rfm3uzT3G+tHLoj5TdvUFbC/fkHeViXVzRSd2Tj2QyqaZOXWaw8L40WhfZQVWdI2rfdzO3X5qOlYDw3aIUCsN0YXaLEihBBCCCGEkD1CixUhhBBCCNkXBIJeWdaCFQAwvuF6AMBmL792Hk6WqO1xtv6cSlas46ezxermw9HycPpQLm87WRQmZg5qf85W0BCvSIIWdt6gJ61ey7Jb4QPHOGUt9EBTECtbnfJ6tXA1BTDaIQ7yusXWIVtelrHPlY0LrcsE40UzeDoAbI/a4mha1+2OmuNUC2S05du1TYN+tuqpoNfaMAtQaDDg3tpGnTY5vdj6ba1SKlqhFvSZCQkyc/KPT0bPism47cVxaJivv6PTeJysFUu1yOyx26lUPj+sCCHkwCkA6waiohSNNH0AmoeutMUEPFRcohHHRdruIV6sEHWP0QeVFapwY4sseeh47mc2u8alaqQl1x7rOqfrrShW3saLnyKtcsva/c7kc4S8Js7+6LuIFdSwohmK5x7Y5ZnshZjx9tVTBfNedqxSaZ3Wz2ka28q6IvXSy0V/kK+x4dGo1lUOs69MaVypCCGENKErICGEEEIIIYTsEVqsCCHkwKmyYAWwQLwi0YhxkpYb4hUqo57zBUfQwhO5qCPZm8nL6nYRZm1BBbVeeRLjNi27p+Q0tURZrxe1xFi59WydsWXHX2sxKusJ4q2m1NYua+BR65hVItf1/dJOvEYrn2dZ022bbZdGfiCLYdht1QjoiWLMlw8sF8rwaMaliYVbi5Uue+5GNqZPNUsH3kwCt9YrQnwCqhAw2s79Cj53dfy9w2V1kucCt5Xct1TEwqaNprnfG2tfZIVtUj+qfWHDwl6oK2B2T6s3M4IVRbLYliY+kroKWjGEoYaUWCJsYPdvOGwLYOT7zwuZYEJFpHK8MAp1uAvXmr2q41jc9sOH8v67cbak6R5o6xquxeNlvR0U27+oUFLl5KuWuAJaQSWtYzYxz7CUzx5Dbac9D8fqcB85nx5G6wkw7/a5ijMQILhDMFvv5aJVVLeguLPJ1so8k9GJTmVtH7+xU77Nz1+zMs/Jm27qVpZRfFqYZ3N1HqD7xeHFeWnTLU5Kl6C+XQPs2heD/Shvv7jgSNcXjW5BoQkhhBBCyLkHLVaEEEIIIWRfEIniFRccOVKnnbjyrgCaA7feIO4giVusrw1aaTagdqnCD3aOpVpRBlEUY+zIrVdmsL033EhpxoqvQhaHcttnyVJlrfheaINl6ICvG+TbCTxuqaXSncFxz3tgL2hddoDasywqOnhuB9FziIe8nR5je6y3U/iOcjuLUaiV0ebT+b61N4WxcOk5seIVD/qx9y1sr+WFj49zSAfGEqhtttfmTo8xP6wIIeTAKZpxrJRGmnbsJk1dMswLQ/0gMi5+6gozMzGrqhQXxMa2qlWW7IuFilakB5aNW6SuG9b9RR/2Ns2LiaSL9r1hOmun6bN4lchF/sd5sUnueQ2D+VRfhLz6c8ETr7iUzxrDtybOC9BM41KZbavmPgDZjdF7J/OEOjz0RcYe416v/bKnL0ij7XyOVcFLRSwAoO0glbGqaZ6LKCGEkAjFKwghhBBCCCFkj9BiRQghB868a0VblMK1aCVLVTU+VSfpPNJgLFFqvQrBcR3ZNtHok2iFTdMJ2mqpasQHSfFmrJXEm7ytrhPWFUStPlaAorZimcNhxSDqbR0rltIz1qZ64nFaaDStF/OVoW0K2jbWJ41p4nl/nNw2ohRlW5RC67dpvbIt2lGnBW1vu67CuD3pfvRLL95N22WlOWm7bOUbT+J5tLFndGK8tUjN0nVhJdanxm2HEI8Q4r0/NHGMqktvBwDY2MhxjHo9FVHJ1+Z6Eo9YN7L/utw3wgdlslQXDYt1LCeICrG041hZQYvSiWNl4yzV+5PuiWKWO5T1DZ1bnQQVGpZj3R97byYBChMUq3DDV1St8jzm83mCNavw3N5yWvu4WuZdIf34XLlN+hwRE4NKxXG+/ClvqdPe+4Kvb9WlwhSVEwJkNudhAQB/9VP3BAA89DkfaZVlUZGNac8KhSS3SyvGtENXQFqsCCGEEEIIIWSP0GJFCCGEEEL2hYCA6WzWsISopcpaSdVi1TeTIVW0wopXDFXQwsqie3MQde5pkl231ikVr7CB0qWWYG/PIRQj360S4YWpv5fCDgyrtnV+GZ6Fv4t68jx6bKdJgt6zqjSt2e325XqXt70OuG4sUI/+jc8AAF7zlCta+bM1rS1eYfe+dGTWPYv4vECFJ15h01ZZqhSVXq+qfF5rq7+xjo7QTYVc4YcVIYQcOAWaohRpWawTgXbP7Yde2c8S/7PpqLW+dnuR9gO7MA+MMIguXtYlReO4hFPpBcOuSw/qVUpYur7xwpCEEkobOym5xBnvQEwrT9ih7RYzdmJVLcMqii1D3wVcYQl7Kjq+Cy0JVVOXV/ZyZTNPFayO7dVulJdmFb10vQ1XoS9ZNp8uW7eotaNRNatnYlfZODSEEEKa0BWQEEIIIYQQQvYILVaEEHLgVGiMayXXFV+C3ZhJVLzCSKtb+XRlOj4dNzXiFZqvMpN8NS5I5bhWqOViOjqey0gmFhtPxYuxomnbE2d/HLy5wQ0JdkeqfRlevlloS7Yvs3ZNVkjFT+r9NuWlU2otWypC0ZCPD80GFs5hshLw/bLtilPnM2mey466tvTKspWvOeE8pZl86r4zMeeWFiuyilBVGI1GDVc0dfvTX7tcmny6PDTiFQN1xbNxqbwwC2YtkN3/AGN5txb4nopXZHEWSde8FWxRN7NqnPtd7R/LtfXGdjZ/V/c8L56Xe28u6bAaoS20nzb1f89LPg8AeNl/vaOpP/UNu3BFXFZ/3i9r4o/Pn57pHPVZ845f/Io6rXD6l2rO3c8KVYQdumJarFtqXf9629tiY0OvhZs7lXuwH1YhAFUHX0Xv5WIOcdSuPLyXjlYex5XGw8ZzWZ7v1Mo8o9HqdnXNN+768rKPgeS8jmD3ZXX05dnH8rq2f5UyDwAMh4OVeQghhBBCyG0bWqwIIeRAqYDZ/ATdtty6Dh6pnLolGCuWWq+sFSuvMxLsU0eqNkmrV2aidh3dXgMEm1HaWjLYGaSxad4EbU+K3Btk1HyeLPuq8ubzNwL1euNKzviKZ4maOvOudH1jTriTTwMJL7OUWeuUHpNDa+15V9ZyVFbeKHbcIXv8dbTVBghWbJpKD5e9PIqro/H2MAVnwjkhlhACxuMxBoNug47v/J6n7Lqua08cN/+peEX8bQhV1PNOTXB1UcuWteamwNtlW+SiMPtTzllPGoIKzj1Sz3U08xqXWVnsfa3WEytAMT+A3BCKqHZv4fYGpr1yXvH9d57Lb+pPabsZzP8PT/8HAMDbf+Gq1jrvWCv/8Vkf3nFd9bxSxzo4GOR+sL9xeEflco4VIYQQQgghhOwRflgRQgghhBBCyB6hKyAhhBwoAUDVnEuaXPuqiY1K355fqS4p4608iTakfLNJniuqEuyzJGIR16c4IqN2nJDpKLsbqpuFzhW1LibqYmZdUjy3v2qJO5118avd7lbMO96paEUhbV9AXbR1FY4oRS8NN25P25Vum23XkkS6l89606jwxDIZdc1jt1UXQgDop8S+mdc96MX19vjr3NGq357I7rn72Mnbut662ah4hY3f03WuMTl/CSFgOp264hX7TWVDMSyJY1W785XZna8qRu18yQWwmaYuY0bYJS27+evlfC+pO19D5MHZH1/IQsvI5RWT1UIWnqthFdoug96Uc08Mw3MJ9EJreC6Aedv2dWD3+W0/e992YxLL3JDf+pwva9W/yhWx19PwIe0DYI9dF90ECy1WhBBCCCGEELJHaLEihJCDJkybMupVHLu0o54hWZ3sBGwdsSt7wzptukT5VKQ9dmalaut8SyS0m6POReMXAKZTWZzPSrun3bXWmSxjbkZxNfBvaUdk2yOlWl7PGR4snQnY9ahvO7trEfPKmHojttJe9oQquk7j9oQ31LJXhfax65ftAzAcZkuUnpeBMXepNH7POXg2QHB/4wiA5vWhghaELCIEtCxWWQJ82krbC41bstC725FbL9vS2l1xBS3mLFXNMAW63XLJ9CIsFplYFfh7Xg26EcKhtjDlMn7zsRe2yqjE6fgc1EPBWrvqdlaehS1Zzs1+1RathoaOSvDncsui/Syat1R5101vLT8TMd1q1Z/bYZ9Ti5971to1GnVTDldosSKEEEIIIYSQPUKLFSGEEEII2ScCqtmsYZ3S5YZ1etotBufymhomq/iT5ljZ+VS13LqdCyVti4XOp2pY8ZMFym5bJsuuzkOsnHlaxT6FJtDAyHaa2qC/+vV9VdDg3hJPBWsVUgt3cz5ne25VXW5Py2i3peq1JeOLwgRodto0b7Gy7dD6rST6dHvk5NP5VCZAdbJyeV4cNuzE8NCh1vpl8MOKEEIOkhCAUEHMC0FInf1sdNrJ3u70ZyaouRfHKtRpJo6ViVWlTLfbQhbzDxn7IM6TooOT5k2UzstefKqdsixmlaWeOm4nis+kY5tkYTutUIUrhuHMJ1/W5hwfq51pPG0LWtj90fWFmPOVLgH7QqHnzwYyL+o4M7nx1kWo3ta5Psr+WiuNEEJI5Oz8sHLmBcwTsFgRpVnU4i/yOo8zauHW6XzVuvk6jFJMO5bVJcDaboKwHRRFh3N5Jujq2+35Oc+zbNSHEEIIIYScH5ydH1aEEHKbJSDMthsOLDoY07A61RLsW1iGyqwHK6ObLFV2kEfl1m1aHcneDPR0Ghgygzk6+OBZsTxhh1Wo4cSquKuQRGWsSJ5ohTZBLTtWgGJ+HQBMHGvSrBaKyGlqqSoll+fJxysqxQ40ZdPrtsyN60zdY5O30/KsdazvjOd450ItUZ4bjz2Pw1m0aG1sLLdIzRzLJyENQgBm04bYg7r9ee6B933+8+q0iy+IgilXXHpJnXZ5Wr7r0QvrtLsfi2m37xvRguQCqIPzdgBeB9k9QZ9G02dt98RaqKIyr8yTcStfqy4zsO+JVyxTtGkILxTxxrcD1dYCvQhvYLjRT+szprLuebHN1po9GsV99VzwvLr0FLtiF+O2eIQVzKldLK3wUeqn+oNBqx26P5decsc67fSNn4/t3s7naIj28eolIZ7pdvsZ2xCS2qFL59lpTiCEEEIIIYSQcwharAghhBBCyP4QAExnqEo7P7NtHVGL1XicLQtbafmUkbjWtLGxYmxNo1W+cuf8qYhFth2opcpa9nX+6iorfl2qtWLUlpVpqwyVXg9Vzq/WmdHIzHv1goY70xSmThDy+W2tNV3rslYlT7xiOk2iGA3LYvKeMMfpdGqz9SIYpgD3nniFTnVRS5fF5h+Mkty6CeGg1sFqlq1Iag2bpXNuy1BRitPXXd2qa7jWnlfaNfDvTr04LPywIoSQM4AVlsiugDltNo5CFjYWC9KDfbJ1S942vRxYl8FqGh9ok82TOV96UExHbUECm09dBifpZaZqCEDMFqY19s1xp9Nl657nxYXy4jipK55N6yJkYV0HPbGJOo6WSdNnthWq0Hxjk6Yui1477Lb2hWeeSb2xjXfTzqfuhNbFcFK0ty2TkMV4kq8jffGw50xfMqw7kfviO4rXVG+YX3yWvYwQQsj5Dl0BCSGEEEIIIWSP0GJFCCFnADuJugrROmStU8Faquptojml7A3b62wclbTcG27UaWp98LAS2lU/tSXFArGTktVlxEpzV6PQTvPMOFBrl0nqta05tXXKeJisOyqwmq9ftgUqFCsskS1lxtqW2mKbVFvWGpambhLxfXVZMbF61ALmHZJ+2i/Hm6axX10l6rXeQT9fO55bkLr7NBRN07LGdgGA4dGLO9VLiMsK9d0cHym7jI3G0dq6td12DxwZYYnJMves5KaGhttflZKMe2LyEJgZIYqqarsHzhyhCl2vFlybXwUYSiPKUDllLFN09mNQtWNLeWEStM+267QuW4ZfV2i1ba3uO8w+zlnivX0pjPndi3ulaeNTx3NaOmaeK2YtiuSIctgy9Hj1HIUju6+T8WIBkr1AixUhhBBCCCGE7BFarAghhBBCyP4giGbYXn7F7KXlnkkrnPmHam2YWZGBJHIxMxYotc4WDWt2Wp88AMIshwYYnbgGADDdPpHLGEcrfjXO+dwQFI5AhWfFau2LY3VpiDcki9KqWKR6KKylpghtq1S9TqXdxUqb67q2FcnD5tM6GvV78vFz25pTndPKtsXKilz00rGw5S4NQp/Wjcd5XqlnlZvPH/N1sxjulIP/sJIOVRZtN5dd5QHQXzuyMk9veEGnsgZHjnXLd/joyjwbmwcfC2Q8acdn8OgSPLdrgN0uNY7H3QLceB3IbssrvBniDl32s2uwZ0IAACEgVLOG615vcAgAMBnlh76UySUitK+vrjE2vAe7lO1YLF4g9fxwdFzIGi8H8QFkY4Z4LwzqJjcxbnXakom5ZTVfQ/ii0AdrTiuXvRQs6QK856V13dMXNZumu+GJcVhUjGJVzC5dX8393yjLuP95dalCV2m6Ms1n+yQVVxs6j0ybb7jWdj1VURMp226mhBBC2tAVkBBCCCGEEEL2CF0BCSHkDOBZmqw1oEzL0yS73shn47OkfGrhAoCiLrstWGFjsVSlbtu2QpQalX6a5bU9S69OEO5Ncxnjqh3jpHbtMMFQth0LjxqmrTdHbbwx1Q9T0dabo5ZPT79WAMJDt7UTsdUqZsvVtjTk3lX4wliTdH3fiHJ41iYtWw+nV4ZlzRH50G29/A1Le9mOn1NPGjfnQrc5fNi4OKVrwJtITsgipCjQ3ziEoTGT6vJwLQvlDAaDxjoAOLIRr7mjh7LwzgUbcXloBH2G5ZLXV8czquzHOmbTHB+r6CWRCSPyo9j+WarFdfU3DgPwxSlsb1kVbeEfZWrifXkhLfR+tS5uy9z4fNphF5bmDrl8bbMnBqHPBN8l0PT/oh4Q1hUxLtt+qAt+/K+22Iefz8Qbc9q8quwu0GJFCCGEEEIIIXuEFitCCCGEELIvlGUPx44da8j+1xYrY51aT8sXbOQA1BcdiRagS47mue8XJovVBYNs7VrvxbJdw02yWEmZ8/fWYnkNT4Flku22uGR5sVYpLScka1dlLGiVI3ax7GW7EdKiSp4KxmSuFpNer2qldZ3v3tUSU0uVw3g2iMag6Da/0rMEPf5F13Ta9qzml7tZCflhRQghB4kIpChdV0CNqwLkh3JhXPzUBXDiuAdaQmg/bNXdr9o2Li7phcG6etVp6SXCd/Fou841RC5mbWUndZCw+SbJZdAKUcyWCEA0XPGCtsVkmNvtmfPi0HQxDI2ybB2uC5/Ztue48SkaYwsA1gdJjMNsq21WgQrPZdHuq5ZnvYh6SWTDilz0yraLX88JklWkl6aNjbXWOvuiNt2OrqS9tfziq3F7CCGEtKErICGEEEIIIYTsEVqsCK7QaxIAACAASURBVCHkQJFohTKGhGoWrUM2PIRar6zcukjcyIaIkHHZKAMAkOTbLbXrirFOqWtL/9DhhfmtoEZRbKXfbE5RUQTrpqLrbcwSDffQnLwc8/WtoEVaXGVFcuc795rmo/HUuNjU1ZpJ4Um0wlqYVMiiV7ZNUbZONQRZS5QnMjHQfM4wptbvbWfniaulrG+Ouy56svPDYbZyDvoagyYXWMvmO649vTUjOHD0YgDNa6DnTPQnxFKWJY4dPVqLUwDZ7W84yO6BR5OL3+H1fM1dmFwBL70g94UXJoupdQVcS9b7ZhyrdI2reEWR6++nPjOYfrK27JvrW6/1woYYGG0CAGbO/eK5CWqMK0/Qwr50qxdBz+TLIS3s/Rrb+fXP/VirPHL2QYsVIYQQQgghhOwRWqwIIYQQQsi+0CtLXHjsKNatxSotW+vUYRWvMNLqasW60MzruzBZUQ/1srWrnyywAmcyplqsJNevQdiPXP61O98hQnbAwX5YSQEUTvj3eUIHTfuymztCMVhdVn/9WKey1i+6Xad8o+M3rsxzeLsdX8ajS6yCvajCeIw7lNe1rKqD6k7XeAw7jXWwtE7pZqytHBGAefazXeT8IEBif5iQXnLdm2ZRirJou52oe551+1NBCytyoW6EhYn7kuNdGReXpEBV9LOLjZTJ3a8/SNu17+GeUawaDGJfYO8DL1ZLrWK1InaIClrMC1EsYln30TXUi41jVceHMl3ctGMYJxXDsOV5qFiFdUGcZ7pi/70uuI7BZRqsUXs21o1CWvJjHE+yWMpwLZ5v6yo6TS5QFroCEkLIYugKSAghhBBCCCF7hK6AhBBykNSWe2uSSG4tJu6KJJEFa51Sq1QvG6dc61TZi54BMxtlPlmq+htGIGOyHReMhHZvGF1wymSxsvLatWS7mWyt1g872VoFLWDaWVtJjGVrhHFal4/FoaItGa7WGSstfmitLTdehZSmcV+sQMgSC1AjX2j+2uWeJ0DhpDkGu4b1TGXgPdEKt31LhkBXWeXUI2C0ba6jZOWcTtsbH55mK1aRzrNeCwAw02uGkAV8yaW3w7uf/N/OYAtUvOfyM9gGcr5CixUhhBBCCCGE7BF+WBFCCCGEEELIHqErICGEHDRSYNW4lgpVWFEKZbY9qpdz3JW2eIWlHEQXP5maOC5erKokZDGbbab/bbnTVpqW0TO+dta1T5ki7U8jFlPRStNietY/L8XAUtEHoOkCOI8X92oZDbc/x2XQK2/Z2bPCE3UcK09sIu2D585n41NpeV79VgBjvd92o2wcR902JQ2H2X1UBUSs8EXhiCzNTnUTXiKEkPMRWqwIIYQQQgghZI/QYkUIIQdKEq+wUv4aYsKGkZBoWWoYOpKFp79+UZ00G0dxCStyoTFbGoyT3LqVeXck3a0cOwCURl5bRQyKMj86VJLbChwURWxTQ/Z7FNtnQx2opcparDa3ojiClWBf78dyVM48tbTVdj1aKmJhrTl9RyiiTKfA5uulXesFYzGatfP167bn8jyRCRWo8MQwvO1qoQxzGrR+m6aHzFrxlI2NLIIy6PdSO61VMFnKCnsuUv5BtmKtHY3Xmb1OVNyEEEJIG1qsCCGEEEIIIWSPHKjFKkAwQXuEdJ5+r0PA3o4BXusI3EvYONqej+Ax2bqlU75q1iUo7urjAAC9tes7lHVDp7K6BvWddmj/dNotKO60Q2TNru3qHNS3Q3ldgxJ3af94wgDBhBBCCCHnO3QFJISQA6doDg7pAFA1MlmGznbJd03yx7zGr9LYVQAwdWJb1ctVe7Cgv364Xg5JoCKsRZev4OSfmThWheY3+cq0bVWdrtM8EYUqJOGNoi22UBkVCXVZm3UY9NkNdpxlmfCFdfvz4lfpNqvGbdT1b7oktpY3plO1Q5/VMbFivbFib0BoY33N/Fe18g3XBqmOXInGMGu4efbbYiqEEEIidAUkhBBCCCGEkD1CixUhhBwkIkAxyIIVAOoxrobrcrIcrMgn0pZbL3pta1dRThr5AQDTthWpv3Ek5lfZ9SROAWQRg2qyndOS2MVslGW4Q7IsWStWUUQr13icXa+nyWRjRRRUbMG62KrrblllU9Cgl+TBjXFm3tq01msLUDSsTimfJwAxdUxWKme+CK1/zRHKsFasWqCi49CmY+yrKRvy9U6bUqKKgsTyYoGeG/PGNF9HZS8etKqwFrAxCCGE+NBiRQghhBBCCCF7hB9WhBBCCCGEELJH6ApICCEHiiCKV5juNzgqBprmKZsagQF1z/OEKoKJbaVpDf0DJ998bKtikEUPVLTC5lGF08pROg1GbEJFEVapdnZVCdVsy+JDVc5htWmaf7aiTnUftG6Hy9zzJrNcnhfHSlHxCs9N0KaNk8de37oxOnVpbK/RKLtb5lhheeMqXVsqWAFkIYupUdQoxvF8D0xss+2tLEhCCCGkCS1WhBBCCCGEELJHaLEihJADxROvUNOFEZ0IyYpkrVlFW+o6JIn2oszmlJ6niJ0sSjIZmaR+K1tD3AK+3Lplsnky1ulsY7cNm3F5MMg51TplY+ep5aRnYh7qNjb+3Gw7WmUGjadY0zq0Pc3WnKwnkcvohbh+NDFpabixNIddLVpWtqHO54hHWOuUZ9mqrW162s0Qp5emljJbltZlhTc0bWMjXwCDfj+Vl/PpcbdpvV4v/eaK1y84CqBpoZy3aBJCCMnQYkUIIYQQQgghe+RALVYBAdthdYDHSVgRXRHAenlBpzpFVgczDKEtOeuxceyKTvlWjfDuhC7BGLvWd+r0aHUmAMXW6uPvBaDcbb6ucyp6XbWJ95Fph4CkoxHlh8kOCZUvrd6YAZXufXGiwjbGxNJyYSTYUz/rWReslUq7joaVam7Olq3dK0/KVO9k8TytRpoz8anyJkM11ofGb1dc+XGTNnHmOHUtr3Q2WtY8b109n8rJvypAcF7MBav1ajyemm3i+sOH2hL89nh6AZynRkK/zjdcd1pLCCEEoMWKEEIIIYQQQvbMyg8rEXmxiFwvIh82aReJyJtF5OPp98Jbt5mEEEIIIYQQcvbSxRXwJQCeD+BlJu0ZAN4SQniuiDwj/f/0/W8eIYTcBpHC/z84aZVxVVbvM+tGqPlMmeraV5TZlbjsRd+yapaluCUJY5T97CYW1F1bBSiMeEZRLhYuqIxL8myynYpo+7NZ9zN1AbQS3/U6U68n1d5PbZkscdf1ZMw9lzzrBedKtKdtVnkja33LpNhtHbXYhSMB75XbSCva6/x8MXFza7tOU7e/0XZ23xxO47UyXDOy/cW4kR/w3QMJIYREVlqsQghvB3DTXPIjAbw0Lb8UwKP2uV2EEEIIIYQQcs6wW/GK24cQrknL1wK4/aKMIvJkAE8GgDtffvkuqyOEkNsSxZzcesJKq7uiOmrZWi5Es1Q8wlqskpWrIYteW6raliANENzIP5umXxMMOOWrjKDFJAWbtcIK40lctiI3Khhj0zzRCrVULQsQ7BjCMAvtsqxQhFp9vKDBNkmPcCNf2tgLJOxZpbxgwF6atx/eujJdHvYYa2BgK62ux9YGCPYEQjSf3XZMsR5CCFnInsUrQggBTQeW+fUvDCFcFUK46uJLLt5rdYQQQgghhBBy1rHbD6vrROSOAJB+r9+/JhFCCCGEEELIucVuXQH/BMATADw3/b5+31pECCG3ZUSS0IQTx6owceuCuv0ZXy8VrSg2zKbJLVCy+5e6EJS9duyihhtfcg8sTR0qVhGSy6D1KuytxRhG09Fmzr95MlZvhC102aapAIV1K+ul9YNBPhbTLXU/y+N+ut66B6orYN/6zPXUnU3/b6s5NFzs6uJCa33DdW6mdZq4T4X+5nzaZOv2p+2biRHtCM18ZlfdNK3fpmm1GrvKsrGxltuZfBF75lyou2XRaLu00tY34vm251EFLQghhLTpIrf+SgDvAnB3EblaRJ6E+EH1MBH5OICHpv8JIYQQQggh5LxkpcUqhPCYBasestPKqhCwVS2Wxt0J47BkNq9h3Y4AL2Ctd1Gnsnobm6szARhOl08sB4BZhzxAc3R5EZPNU53KGt5wY6d8vXJ12wpP13eXVJ6+scNoNFmdCb50826xI+SL2Bp7IgOErEAci5VnnYJNc2TZ4VzvTv+ofYkVtqgm7Xtqvs8JDRn1tihFXdY4p6mQxXQ79yV6X9r7XS0nVmzBE6rQ+9CuK0UtLNbc0016fdk6LXc6Wzh1eCG6a3YXPLGMOr8KVTjrPEGNhgBGvdS2ttnjqcfu8KH1nC9ltMez52jET8bt8+zlI4QQEtmzeAUhhBBCCCGEnO/ww4oQQgghhBBC9shuxSsIIYTshhCiq57rmmZd+HTcy7iaei7QXprGp3JiUXUmuQwGx7XWxqzS5dlku06bbm/FX+PPpu5n47FxBXRc/DSfF8fKc11rbKsuc5rHegku8exT979VeEIRXrmet1wj3pYKVDgufp4ns+fGqB6QDTdGJ6O6Sm5ubZu0mG9UZVdQFQgZDvutbb0YWIQQQtrQYkUIIYQQQgghe4QWK0IIOUhUbr1haXLEK+CJoqh4xQqBGZVMd8Qcqlm2UqhFq5qNW2lh5ghUJKuUtU7NHCELtWJZa5IKKkyNtasWtAjLrViKtZxsT9rHZ34Ta/1ZJl5hBSbUetWwMDlWqdraFNpp1qij1quG8ERo/roWKSsL74hiFHPrIjGDd2yagkNx64bMfWq0tUiNk4S/zTfaptw6IYQsghYrQgghhBBCCNkj/LAihBBCCCGEkD1CV0BCCDlQJMWosu5aReMHABDSei+2VTFsFzubtvKV/Ry7KKRyRHIlRRnj/IVyYtLi8gynW1X0hhup3LU6bYLFcfSs+5m69vVKE0erF9vUm+Y0dUWz26ormnVT66dyvDrqWFlTK4qhSzl/IWn9VFr5Bo4roBeyzgpk6LbW7a+f2jeTXGDtClg1t1uUpvV6LoM9Z3h0fS0LUKjIR1OAomql6TG2cao2NgYpH8dgCSGkCwf6YRWwXJlJqbA6U9EhDwBMOgQSXusQRBjAXEDPxRRlf1/yAEDZX922LnmApp/8XvN1DRLZJVhv1wDBXdWovLkZu61z0qGs09s7DyRKCCGEEEJuW9BiRQghB4rKrdsBERWvaFudXFn24FkQzGBBKscTr2gU46zXNBEdPMnWrKoWpVherpRtKfS6jIZQRVy2ghaKtZJMp6vEGHw8Q8sy6fKV5ZltPetU5YhMqDCGJ7eu404Dp347juQJZejpbtg9U76xMa3pMVbrU2x7W9Lek68fjfK5V7oO0BFCyPkI7fuEEEIIIYQQskf4YUUIIYQQQgghe4Q2fUIIOXCqptvfMtx81v/MiSuUBCo0JhVgXPwKMz9y1nb1qikWz6MszDp1+7NuhV4cq15SWRhtL3cj9IQSdE5ks7yysW5+PdB03ZuPHTW/vIxaUELaaY18qTw7BXVeqMKmeXGvvDZ5AhVdUbe/zc1xK826AqqL33Ct7TJYGMETzz2QEEJIhBYrQgghhBBCCNkjtFgRQsiBU6BhdVKsdUpFHsQZ//KsVI1yqvRjrEjJelUZK1VOG7fSwqxdRzXZjr+OdUocC5cVmBiP2/vrqXN2VewcO9rnuq0nGFHn8dJMlbUohZPPipyqFcmmeRYoV3hiSVtmKbE0x247ycZbY57KuFsdEbWUbU1ywYN0TZViVTtiQVaIQq191oo1HksrHy1Wt11E5FMATgKYAZiGEK4SkYsAvBrAXQB8CsC3hxBuPlNtJORshxYrQgjpgIh8SkT+UUQ+ICLvS2kXicibReTj6ffCM91OQgjZA18TQrhPCOGq9P8zALwlhHA3AG9J/xNCFsAPK0II6Q5fOggh5xOPBPDStPxSAI86g20h5KyHroCEELJ7HgngwWn5pQDeCuDpyzeR6N7nCSc0Yls5ohUa26qw7nJpedaOgVX21+uk4LgHFmWqr5fT1FWwklEsSkyso+FGLHfzVJ2mAcqriRFHSGm9nhVMiO0bFPmxo25nVnTCi32lQhVekHDrblhVSWwhlbFt8teuc4UtP6WFnKbF9U25M2m7Fqpbnn2IerGt1D3Pi2M1X2dcbgfamjn5vG21XBtqaq3fS23LGTW2lRUKqffHKG9srK+1tiW3aQKAN4lIAPC/QwgvBHD7EMI1af21AG7vbSgiTwbwZAC44oorDqKthJyVnLMfVpX7VnLr19qFVUE5u+bZb+wDcxldAkB2DRLpvSTN482V8Jg4QUQ9Zh3ydayyntuwjNPjM3EtkjPAvrx0XH75nQ+irYQQslMeGEL4rIjcDsCbReSf7coQQkj9X4vUH74QAK666io+FMl5yzn7YUUIIQfMvrx03O9+9wkIVbY+NbCDN0u6Z0/Qwiln1QCOrldrVmMbFaMwRahAhThy6zatKBcLWUyn3QaoLN4AjaY1LVaL5dZV0KKh5r5EMn1Vmt/Obvl1/WQWF/qltNbNvH1uSMWn/TH7qAIVTfn2OIp0aNh32psPgGcVHG23BUwG/XY55LZBCOGz6fd6EfkjAPcHcJ2I3DGEcI2I3BHA9We0kYSc5XCOFSGEdMC+dABovHQAAF86CCHnKiJySESO6DKA/wTgwwD+BMATUrYnAHj9mWkhIecG/LAihJAV8KWDEHIb5/YA/kZEPgjgPQD+LITwFwCeC+BhIvJxAA9N/xNCFkBXQEIIWc3tAfyRRFerHoBXhBD+QkTeC+A1IvIkAJ8G8O3di/TiWK2IbeXl87ZZMmbWiDc1WxyTSJK7oesmuKi8Jair2TK3PgDoJTfCytmvRr4USMovL25rYzfVsa1Mseput0qbQbdZNU1Vm2LzaZqtV9M0VpXdBc8FsFzSwNIRu7BF9NLq0yb+lBa3Nc7nc62XxEUGVlwk7oh1t/TcA8m5TwjhEwDu7aTfCOAhB98iQs5N+GFFCCEr4EsHIYQQQlbBDytCCDlQVG59lXVK1zvWpzB28qGVFkK2SNRCFY7VyabpNsGxZs2SpHoj/xKBjKbEdzdp9elM81UmX3sfPRGMeeXQrkIUDRn11OSZlWAvtG3tfF55Xj5Ps2OVQEVd3lQta/l4arnbDWtfSmsYOdsWLd3WWtYmqZyGeAXi+R7086vCeNJRUpUQQs5DOMeKEEIIIYQQQvYIP6wIIYQQQgghZI+cs66AhePesOt87uRvL183F4hqyYTwuqgOeYDserMfFP1Bp3w9JwbNPMO1bmV1CRBsXU+W0jFbl+K6BP4FugUS3mKAYLIbbCwq7YNsbCuvvyn0vqucfKavSOVIuZY3TS571SznK8oYkyiUfZM2SPkmjTwA0Ftbj+vG23XadLSZmpb7BO1ryp4pt4jl9cwueoHGq+22C6QXY8mLY9WHCmRoGfnezCIPxsVP00x/4AlK1PGpHLc717XQnB7NZ+Nn1buR6rLaFF5529PFIhuFMzy61ssZ9RBbkQstr2/6ej1mhSnw8KFhK63wKiSEEAKAFitCCCGEEEII2TPnrMWKEELObcy4VtuYYqxXjrT6Kit7Wi+mQJVNt/Los8loYRG13LqzrjKCFVIufoxYYQu1LDk6FK44RbFKA30JalWZzdrma1vsMsO1l88aa9QCVIVuAhSe3Hot914aWfiU5glquA4AVXtxMjMCISFuPOy3xTis2Idar+y5GI3anhW9VZrzhBByHkOLFSGEEEIIIYTsEX5YEUIIIYQQQsgeoSsgIYQcKCG66llPt9q1b1VsK8VxD2xsm+JYOfGm3DhWxrVQxS00zYrx6LaVI6oTjFtZcGJRLXPts6IQRXJBrBx3x4Z4RK9opeV8cVtPnMFzseuXy90ONd+aybdMl8fR5HBxhTJCu/yuXpGeroRu63j1NeJorfXiORsM8nGfprTCCK10FhsihJDzEFqsCCGEEEIIIWSP0GJFCCFnAs8SVXUMr1AZ0QmvnGTtmU1zvhCSxcpYgjRtNtkym6pVaqvxP5Cl1a11auakuU1eYlmy1qxpbe0yAgyOtPry8orG/zthmXVoVfSIWpbdWTd1Eq3IxLJ8aiQqZLksvD7Qm2EiFjfaWuq0XnvMxqkgK4u/ebpbqBBCCDkfocWKEEIIIYQQQvYILVaEEHKgSAwO3DAkJCtBYQJve9YrlWAXk0+SecIxTJS9Yb2cLU/Z4iAS5yBpUGAAkGKxBHvRX0u/Jn+axyRmPpMu2wDBakaxAWYH/bjeWp+myXTS67dNR9aaovmsFUuXtQ47H0hlwntOkOFtk6bFrZtJcNO0azYYeN4N006VMW/MGZvPby1QrV1EzxnuHKfd9uaCeWXYOV42WLCiAYKtcroeWnt+Ntbj+abEOiGEdOOc/bASdJvNa6PNL2S2+EWiUWe13SnfbHx6dZ5ptzqr8eo67QvNMnpr653ybWxsrczjueHslq7uOl0nTY86tG3s6QE4bE1Wl3V6vH/HghBCCCGEnJvQFZAQQgghhBBC9sg5a7EihJBzGiNh7eoLqNufI6Puyq0bEQtJBc5m2Z1QhSosoZZWb8uyS5Es4bO2WIEn2e7RNV9Dbt3xbdM0a9zObn85v7qseWIXumzd2tQK3nfqrAqrga5l5CR12bNtmiyxltt885LqtnpXbl1dDB2xi9LuY1psCGAktz/rEqjbWCcAPSzWg2C0Ha+PYlKYfHQLJISQRdBiRQghhBBCCCF7hBYrQgg5UIIJ6ju/yrFOeSIWNp9aqqyMetWWVtdAv42gwcEJJDyXZtdVkzjnU2XXLTZosC57AYIbMuqhLUCRrU5mH1XswZiMeo4og1qgcnntsUPPOmatWPV6x9pmrT6zJdYpawGbhZjPy+4JT3SdvqpWJ1uGV57m8+aLWouVCl6s9fJxHwzQYrpCVp8QQs5naLEihBBCCCGEkD3CDytCCCGEEEII2SN0BSSEkANF41gZzX9HgKKxXKPugaNWmhgFDBWtmE1y6AR16atMqAdNs+EfQr3tKP1mFz+7PJ9Wzdrt9cIyeOEVrCugt02RhD4Ko5ugLmlNgYqikbZKFKOqYtrYxF9wxTNSkhWP8EQm8nI7n0XdCKdLIk00PCGTO2HPUbmwjnka58q6+BWy2BXRxrvy2qLHxbpKjkZtMRNCCCERWqwIIYQQQgghZI/QYkUIIQdOYeTUASBZTAqjFqAmCytUods4+QKyNUml0osy5wuLBDPm8lWyOHh5kcqVsv3oKPtruYxkxeob9YOtzWg9c4UiDJrW6+U6iqLd9l6l1qk8PqjWME2zQcW13mlpxDg865gj4z5eGlHcWMVmcRsru56FLGxdyaKWLFHWEKWWI2udU2OgWqQa7XUEK4b9vOxts50k2K1Ue5kOo93vjY21VEc+xp5FjxBCSOTAP6wqN2BLkwKrO+6ufXtPOhjlXJebNtXMUedysG41C8uadiur6hAHRopucUX6G0c65dvokKcobu5UVq+3+vh7rkEem5sdj3+1+nzqi8UqPCWteZa+cxFCCCGEkPMCugISQgghhBBCyB6hKyAhhJwRzLiWuvhZ63ntKujEtrKEtnhFVcexMvGpHOu3rm/GsWrmc7dzhCrcfCZtmaBENVkuMuFvG4+ftYqrGIVi3Q4967m6Cg6MikN2RSxNvuRiKHZ/4u/ECkWktDIYQY1dxqqyAhSlxEJmVVsUw5avrn1WiELdDW0MLt3Glue5DKpQhRdnjBBCSBtarAghhBBCCCFkj9BiRQghZzONOaCOoIVnxfKKCYstSt46b+6mSqt71ikrt+6tzyITy+XWVSDDWqfUAmetTlqenaM5b+1qCmVUrTye9WWKxfNardVn4gpvpHa0VdGb8ulLTFa6SiXWgWyxsmgdVoBi2dxjb76onWu6lt4G1vttwQ+LFQQhhBDShBYrQgghhBBCCNkj/LAihBBCCCGEkD1CV0BCCDlQJApTBCd8gHXxq7zwAmksrGOIiFWCEnVVs0lrebod404Fx8UvzEwsqHG7ndVssbtY08VvcRyrVfGS5mNWeWnWTVDd2ryYVA1RDClaaYp1z3Pb5AhKrNoGAMbmcGmTrfufuh32qrbbXzVrC1A0XfySoIVzSqxro9Zr26tuf9YlcDTqFvaCEELOR2ixIoQQQgghhJA9cqAWq4Buo3cd4gN3ptuXY7fJ33ZUdxneiPBuKToE/+0Nu4T07U5vuL4yj5TdJHeL4qaVecaTbqPvveOnO+XrQhe54675pt0uH0JqAgAR0/2qdaoYmEx6X9gLLPVoxdBsq2VmS4IKTxRlLi8ka1gw/ZgEzdfP+cwyAEiZ26nlevd/2V/LTerHtlQTa91oWz8865T2Bz1bR9oNmzZNVrFBvy2VvkzavTBB46vQ7eaty9vM+VWdfNo4FLE+2yespW0dNfPasjQwZUydfGqU8uKtl45lb72f09SKtWYut61xaJenUvHGUraxEc+pbz3cdNIIIeT8hhYrQgghhBBCCNkj/LAihBBCCCGEkD1C8QpCCDkDBDOuJeraVxn3KnUL9NzVZOdjYl2FLDStSG5308n20nLVLbDp9rcYL+7UdNoWj7B4ghI9xx2xdvdzYmZpWs889abJ29LGx/LcE9vlZnplPoaTffAC12odjY2Ga3IdHyvYY/f/s/e+obJs6Xnfu+pfd++9z7l3/kiTyYyEhCxIHAeNw6Ak+IuQCTiOiB0wRnZwlERk8sEiDjFEsr9YwRbIHxIlIUQwQUJjCFYESZAwBiMcGRGIba5sR7GkDx5bUTTKzNzJvXPvOWfv3V1dtVY+1HprPdXr7a46p+/pc+/Zzw8uu++qt1at6t6ne3c9Tz3v8LPrIZQibsegCj0NnK82vIpWUIXV24oQQsgAFStCCCGEEEIIORMqVoQQ8gqwM3qMNAEB6eJU2AIoPc4dVxXcQrXLGcE5Vty64o1YdkSVjg5Ox6s6BuqQ1qGypSoSKkupLk+Y0fmqKg+2gGmhDkIxunxNum/TpPk0th3n80bIRGfEBWUTbwAAIABJREFUmGudFTyB6p2CgRLj2t30J86HT4ku5XaXz7uHwjoWruusbIL1fBNCCBmgYkUIIYQQQgghZ8IvVoQQQgghhBByJrQCEkLIK8Gw9YXOeAzXv9T25Zf1fgvBCKcAO6Ha/bBHnz7udvdDPdj++hhQ4cHqZ9sCo2UQ6tRiZ1n8EK2bwwqZWNLH6tRcQ10elLF0bXoIyy13ykG373Ob4GS7WiEDBFBoUMXk12io201+PYZCq4fkCgIr9BgejqF2R7RA3t6fDjMhhJCHDBUrQgghhBBCCDmTiypWIYi0CzrdN5f+vrdgTa+Kcn01W+OM2GGLarVZVOeNG88PWbIuEZGyXs3WtO1+tkZketX0FD7Mxz7jFeIPoo6QpQQR6UKQCsMmVL3S2HUREb/VB7B3/DdQGP/+8Pe+GPapmuu0eQyeSHUaclFW6bi+HJQqVbPw/aWshwh4H3+KiPh2mK8oq6wOFasink9hhGLgNb4uSjBzEejK88Z/Y2R6CqBAxWg4Pr7f6HZUrKyQDZ3aikpHdi7OF6crIJyiiO853eRUY8iG8dFoCXGbOg2m00hj2/iWi09dFdWrGia8uRl+L/A5S4/v8wMTQsgDh4oVIYQQQgghhJwJv1gRQgghhBBCyJkwvIIQQi6Ik8H61UGYQBFtgSXa/tyJt2ezF9XC62RgxQvd9kRZtLrNOHXVKoi2P8uenEIhfDY2GwoR7WloSTucYzKmlsV9bgvGcl2L92hFHH5OLYb7Rcf3J85j0m8qWv98EQMjYDftRdUZKRY4pI9xXis8Q22J6KS26iybYWt4Gp/XekkIIQ8JKlaEEEIIIYQQciZUrAgh5BWAgQUjEyUqPvbzYSyn5znY5MrssTMCJYpKAyruYNq8blSnQNmyIth1X1yZBuVgKIUqIjhmKVp102Rj4SB4p4TAHt3W7XKVDlWYMcjCEJ/W63p8bKk5CqpDKtDtjflSUESSjqxpdXk1xKP3hqJVGkkWKqht4fXR6PUOxlbVsO+mSfPqMzxR5U7lxhNCyAOHihUhhBBCCCGEnAm/WBFCCCGEEELImdAKSAghFySIiA9h2rto3Ag+MLUAWra+Hu2Bx6+PHVrjRERCyMd8n9sNfWeMGWEQlu1Pj4vWQatOsUIh0HKm2+eCE8beW5ZlUa2Ik2P12bH0GO0+vRZqS+z2IRtDdHlWyARyGB7hjVaKOH032glDtr2b7Dts303shENhH7BX1/AT+13tx4WmMbU7rtfJdnl7v8sXSwghREQu/sUqTN7cj2LcenDIh9nmXZT1bE1Zr2drllLW+b0GFtYfGy/7mKGf6ZQpIqt3314019I0KqthJiGEEEIIIS8TKlaEEHJhvATMKxCJ6lXp4IKFi1KEpViVV+lxH8MlJvHsw75lDeENIUaL9ymxQC8ClVW60OPrYbvbDyEP1Tody1Sn4hhGrPt4QcWBwqUqVgEXZVyRK2B9TFQoq/wClRXjbl3k0WPgeq19V6pitWkdGsG+XtXZWAfzFXFfDLQo2qhsQdhGHV8+vOCzczE8wuu2XDmahJt0w5gGTCA1nJZecLxeQRiIZqAEUKI0Uh72rWIwRg0Lffx4eO1RnUuP77O1EELIQ4f3WBFCCCGEEELImfCLFSGEEEIIIYScCa2AhBDyCujAC1jEe09LtP25U2/PxjUx3Dda1wLcsGoHWRipCXqE2Meqb5PlS+10aKtT253fpbqyXmXHtO/LjPuCZVAtgGgZ1HlwDt/nARm6Lj2+L9I9njpHtU72yL2OgSeui7tYgRqFYcu0bHLmvgXYA6Ptzus9x1Bex9esc3lQBaJBFlbvKgzDaONj7K1lhWyozRDXbvXqahr+2UAIIcegYkUIIYQQQgghZ8JLT4QQ8gqwrmpN8yzi27PfPv/kqqxAKqfGrE8UnlhnJYZqoAXGrruyyupHdQhUJxVMsM4KlJA+D7QojJAJKfMY9Xo1KE+WEqd1pXFe/X6X1602UDEobxhAoSrO1VVaZxeTJzyofjq2XqMCNsyzx+CLKDKpctWDdNQaImJSk9LYJNxiXKdkdRpesd1DXdx11+fxutdG9juqct7KhieEECIiVKwIIYQQQggh5GyoWBFCyAUJMig6eFWriPfVOAE1QJsF4309qo6EPKZcTtwvJSLinKoo+2wbRrCP0xkK1zgXqErOx8hyjFGP6lXh00eMrg4VK71nClUnPZ6lRNn3Xa2Ors+KW8c1qTrmUU2Kx6hAsWrqYZ/tznjecX0nmuiVoDDp/XVabrVOLvJb5szteBtUE8e6472Yh/mMBsF6DOw1qWobRspvt/nvCiGEkAEqVoQQQgghhBByJhdVrIKIdDNXVUVEujD/fS9I7g2355qvq0+mbyWwiebJunozX9NcL5qrKK0kraxo0VxL129doT6kWrh+vSfjFLv33100V/O7X19Up/ctEEIIIYQQciloBSSEkIsSpA9+EoXu48MCTASlZSgo4lt2AP+XsyyDB/ViW+xcvHCDF110e9cPtreyzrchupIaLshoXY9BFNv7ON8qq0M0XGIaaJGHZoxWQIhPV+vfGMueHGzjfB3Ewh8eE8EwDrXCdX0eaIFU8XzbfXp9NMod64vo39NAiwouBtXxFAtw3N3vhxd0BXVq5ytqI4Bileo0vKKDa5q7Ls5X5RehVnX6nXn86CquHeYbo+mfZvsSQshDZ1Yacs59m3PuV5xzv+mc+w3n3J+L4x93zv2yc+6fxJ8fe/nLJYQQQgghhJAPH0sUq05E/nwI4R845x6JyK85535ZRP59EfnbIYSfdM79mIj8mIj86MtbKiGEfPQJYVAbPNiZ9Y0Yx0rTohxlh4X2ZQtUfXx/PIxB1awANWM8OYZIxEj3rk9KUH31aNj29L004Tq3SPt2mNsKrzgVSnEMXVdhNCNWW3IF0ep6fDNuHiPY20E+quD4Pjbw7QojZMMIsaiqdB3T+yL+HF7PqXs5BluAKFkXedy6IfaNYHx7H39l0CKtS8G6FOkOIRtRUfP7JHc1NciAhBBCJswqViGEr4YQ/kF8/FREfktEPiMif0xEvhTLviQif/xlLZIQQgghhBBCPsw8Vyqgc+47ROQPisjfE5FPhRC+Gjd9TUQ+dWSfLzjn3nLOvfXNd945Y6mEEEIIIYQQ8uFksZ/EOXcjIv+ziPynIYQnDnpyhBCCc86M3wshfFFEvigi8vs/9z3LovwIIeQB4Y2UU3V6lZOeVfFaGIZXjL2tfD7mU12/z0MbLEYrXgy06D32nVrH6dNYtR4CDrBnVB+DH8q4TUSkiJZBDIUI0VqHFj/LxpeFUohIcZX3wErrPG4jxOqiabLjd9u7bF8Nnmia9JHZYgOpsW74XGwgNKQbUyPydWoQhAdLnnr80E2oh8UxfexDbjuc2P7iqd3u8t+xab+rYfujrGpq/zvVq4sQQh46ixQr51wtw5eq/zGE8L/E4a875z4dt39aRN5+OUskhBBCCCGEkA83s4qVG6SpnxGR3woh/Few6ZdE5IdE5Cfjz198KSskhJDXiKGfX5iqDzF6vQIngNOgisn1L1Wn2nxM8h6BIeQqyUThiY+xLhyOYcT5Pg+7UIWraJJKpIpVgUEZWo9BFT4PjdBACVWTjqGx6RqUIZKCNMa54Fh1DM/ojXNAtU2j3S0lDJWlicr0nKgC5o1j6O9FCb8gvj9+LA8vu4ZbTASwE60jN006Rm+cTxclLVSptve5UkcIIWRgiRXwD4nInxGR/8s594/i2F+U4QvVLzjnflhEfkdE/uTLWSIhhBBCCCGEfLiZ/WIVQvjfReSYqfoPP8/BfAhy281f7SqMpoWHrIzmjBY744rtIXWxnq0REZFyWV21slzqU6yroWbdgvVjc8+Tdc31orqiPH2lWESka2+XzVXPr63FSOYT3Fx/eVFdU83PV5fL7hNYUlYVvHWQEEIIIeSh8+LNUAghhLwAQXrxEiB0QK2A93DBZRNtdG7y5V5TDOCCxRhkkV9swoskZT1Y4Xy/H8e0V1URcr9Yv98O+1Wne0dpHfaHGntbQZ2P1r1qlQIt1EaIF5rUloc2QrUjYrCFni3Wubh9tCfC8TX4Iqzyflp7eZqN1fubbJ1d9/44tl7n/Zx8MzyPGGxRGBcBq2qo2261BsImOq3fwx4xFAM+sdXi18HTpHa+a7D4aUBGVaY16fKmL62b1IuIPH50la2vqflnAyGEHOO54tYJIYQQQgghhOTw0hMhhFyQIEO4wDRifZAfCpeudXVBt4P64HRk2TUxVIJ8b4Q2nAi3UJUG93NxfQ5UoqIalCDfpboixp07CIooD9QkkRTeECaR7setyK5MH1mqimFUOs59jGqdFCsNwMBjjlHxENm+v3s27Fvh2uNrhikkvsjG0mOMYO/jfMcj2EtQvZbajTXwog9QH49Vw/Pkjdd9FY+La++64+sjhBCSQ8WKEEIIIYQQQs6EX6wIIYQQQggh5ExoBSSEkAvTZWERwzWuGhxc+2gP3GA4w4meVVOG7VZ/KrTLYf+mQ0bbn8MQibyuKOvJ/MNYDExYp6CKEO123fY+1Wl4RZHb+azkVOxTpXPj+agtsDAsgXqu3rAd4rxa1+932Rja4LZbnQ96W8XXtalTsEW7xxCKKVrn4ffBtN/Fw07sfGpFNPpPrSBgQq1997t8He0kpHc47hWM6L4YwDGxPhJCCJlAxYoQQgghhBBCzoSKFSGEXJAQRPYhSOVOX/mvo2KE2lQRr4W5AFLD+HhaefT4qAQZ4RFpncdVLwtnqE4WGBQxqmg+fRT5qBQVEB6hYxgooSETq5s3sjGNZ+8hPKO+HvoL+u3dONbFx1bcO6p5ur1tk+qjSpU3ouoRVXsKCKBQ1ccbapMyCYkwVKy0Dtgn7oKqlxU2oaITxrJr4AWqU10XFbgmjW23xxU4Qgh56Fz0i5WXINt+/k25KedTh+4XNtid++NFROSmupqtERGRclldvX48WxNmPowV5+ZFRe1PM1u3enNRnbj5X4tqvV021YKUrt0n31401+PHy87zerOardk8W7b+fT+fxkVrDCGEEEIIoRWQEEIIIYQQQs6EVkBCCLkgQYLsgxe8rqWhFPcgZKsQWhvXv1blDUwYrYAe+lS5GGxQJttd1VyLiMh+9xRmGrZbV9j6blB1y2o9jvnoOEDF3e/v47FSYINa5yb9oXRp0IuqMAI1JnkKWhctgAXMV0ZnA/axqptHk/lwW7U6rni3T99Lc1zfTM4BcbGflYhI0eZ9wcb5IBWiiUESqGyrPW+7beP/p+dOrXZoyUt1GF4xqOkV2P7UAvj4cXJX6HEbWJOuz1LbGwi+uLkZXnvsr1ZVvB5LCCHH4DskIYQQQgghhJwJFStCCLkgQUR8CGOcuohIF2/l28Clrp0f1ITOpfv8GlUO3Py9i4f4Bfe3ikzDKob/N+4HnUSrN9l+qhgVFShW+1zh0Vh0b4yh2mTta6EKmY/BE6iOWSqab/NQDI1jr6+SKojR60rdxGN5637Z/KMV1aF2PyhGVrDEej2oV89u8wCKufs5tQ7XpA+tY2F4hjW3bm+79Pw3Df9sIISQY1CxIoQQQgghhJAz4RcrQgghhBBCCDkTavqEEHJBgoi0wUsRkvVKXVhNgMCCaEnbFPA27dIc45C2RyiSxU0DLQJYBjFcQvH9YPHq+2T1cnEfzZNAC+GhTXByXv50v6t6M1jrfJfb+vrbFKihFjy0/6F9L9U9OroWrbfW4aE/ldoNMRTD74YwDm/0tprUxfVhyIRa8NTOJ5ICJTpI5dAwCLXVoSWvM3pWqY2vAntk1x9/LdD2p8e6u8/tjBqKgWuxLIGWjZAQQkgOFStCCCGEEEIIORMqVoQQckFCCLLre1mB+lBEKQpjEDZRCQmgT3XhVMNquE42NvmGSHeVoCYK1PFG5ar2+D5XmCzlCiPTXZwX1SlLPVKqdYpC77b32dioGDV582+MUddjaGiGpY4FUMKsUAxVu3o8H1W2fBoL8bGGWIiI9F0eEGKpPV0XX+8Tfe4x9vyUOmUFUOCYFMdf46ur9HxaSpmOYWCFxsETQgjJuegXq94HedLmdoSs7uQfD5ETPUkQ/YPlFNfFsqfhqny8qM7V8wlW6zK3trwooVj2XEj15rK6YsHa/HbRVKtH88/t5s23F8119YlPLaq7uX5ntubR0/nfw4F5Ufd+v+D3lRBCCCGEvNbQCkgIIYQQQgghZ0IrICGEXBAvIq3vpSlzi9gOvGG1G9T2yqXrX1204FUuKfFXxdXwIEA6gpoKw3occdUwhkdV+x7uOc4QLYBlleZQ66AvwU4Y69ykt9Z+Mr9Isunh2Gi7gz3r60eTbcNa4nmDU0HnwfVp4oYGdfTtbdoU66pVUtu71bB9f5fCMw7nF0lWwPbp+2m+2NvKg52wkmF9amfEfYsidzK0rYvbChgzXo246ySUItoJrUCLq02y+Kk9EOu0jxYGVajdb71Oz/vNNTy3WlfzzwZCCDkGFStCCCGEEEIIORNeeiKEkFfAM1A6mqiObMr0lryL6hDec1pGpWoSYqFqh8N7I+PcDq+dDY9RWQrxXknn8mtsqthg3LrFVKmKR4qKUYB5LcVK0Sh2EZEuxp1rAMWwTz+ZV0SkqDfZ2ss4pscqm+vsWDg2BmpcwfnE9Tl4LcY11U1WZ6l9BYRNFFEpwvMuYigEKlVKVcUxuA3UikDXMQy2sOo0gMIM0YB9K0NBTVHxKQADgywIIYRMoWJFCCGEEEIIIWfCL1aEEEIIIYQQcibU9Akh5IKEEGTve6nBGtYb/aT2fhgroE4DLTz0turj9bHSwdu5tkzAQAsX7YHQXqKKtjhs96TBFLkxTKTfG20W4voC9LtS+yD2rirrPAgh6L5Q11wZbS2iBVDtf7j2SXjFAWi/0zAOPIdq/TibVwMvrJCN5lFqWeFjeEUBfaz2t0MIxgrq+r16+iDk4sCW5316/dtWX/dk61PL4GgThDrLwjfdd9hu9Z+aBGU0+b66TrQs4hoIIYRM4TskIYQQQgghhJzJRRUrL0Hue+tW3+enNm6AtigXNAi+Na74WVyVN/NFIiL+brbkA20pu3Rd1bIGx1JczdcsbBAsxpX4Q5qrTyyaav3msrqbm+NXsJVN/WTRXH7BC2XcL07ISfoQpIb/L43wiNoINvAxtMJsfI6KlQZZOPh3qseABuAuvhOFgIHn8VhRdTLDK0Bh0n1RddLHONZroAQoVxo84cp0rqoUFUbMe716NI5pUIWDQAtdlypQfp9izy1la799MlmHSAq38CDjaWjFRAEblTpU5VLM+eEY1o3nWAzn1XXpjcafeNPBbaoidV0HYzHQooNQCiO0QsFYdp27cHn0OwZW3N3lsfGEEEIGqFgRQgghhBBCyJnwixUhhBBCCCGEnAnDKwgh5BWgPakQb5iEO7DT+mgBLDzYv+I0NwVaAePjAuxvGmQBNt4Q7WT1CvsuYT8skQKse94Ir+i7YayAUINitBHmgRaT+bVnlMutgPUqWZfVbljBmNY1GwiUGI8x2AM99KzSsS6GU+BaerAMqu3Q6u21ff+d8XFdH7dge29ZK+E1i9ZCtUqu1+k5UTvf1BIYzwvsoX4MN0lWSLUAXl0li59a+9ASuN222Xx6XLT9Xd/ktnD2sSKEkONQsSKEEEIIIYSQM+GlJ0IIuTClc7IFBUPVqwaCdMa4dVC2+hheoSEWw+O4bTJ/vGbmDBXLgWIUYtAOqEguqkzOCGdQ1aeAwAgrvGJUokI6H2cE2agqVELc+TjFJORimKcCBUofY6CEq2O4RdAo8jx4o9k02Zi1JlSsut3TuE54nsrh+Sz2SZUr14PC4++eprG4T18e/7jFuPVTYJ0qWqfCLkREuvj6YYy6qk64r45hnb6mS8MwCCHkoUPFihBCCCGEEELOhIoVIYRcEOec1EU5ucdKFSarPQTWWTHrel8WqlipWTBcOxsVK2h5ofdgwX1Xh+rRfpfUF++He5ECqE+qJgXUzIy49Qqi0sd9rQbB10NbBYxHH++7WsN9V0WMMcdzLKZqVPAYDR7vnfK7VB6VOlyb3jM2aRAc17J6nFo+aBx7AOXRR/WqWqXnUM/Najg8njeoXj7eP+fhOS6M6H09H6Qw2pBoA+F2n9Q7VaomDYIjGMGua66w93R9WvEjhJCHDBUrQgghhBBCCDkTfrEihBBCCCGEkDO5qBWwD0GetLvZum2R2xOsuT4ork/cVIx8vMqjZy3K8vF80dLvtEbkb37AJccT2bn1fJGI3Pf5Dd+HNK6erRERuao/OVuzfvzPL5vrk59eVPf40fzr9Oab17M1IiLVszxe+pCmmv99JeSQBq1h0erVgOVLLYCW/a8y3hcwql3tcc4Mr4AxtZuB7UztacEImxinwuMX+iNZxHqNLIfwCp0Pgy+sUAq1EaIlUfcJDqxuY6Q8/HvXdenajbfPYJwrora//f17sE+088F5F9VwvkW9kkOqdVrTPgZZuHJZ6IMVRqHhERgsYYVXWFHt+mtmzbte5+/jeIy+Gz4LyirVbW9vs30IIYQMULEihBBCCCGEkDNheAUhhFyQQga1qgR1SjUCDKCoozpiNQ3eY7BB3NyENF8bgw1WGOagDYIdKFGjWg9jY5BGnB8Upj6qOQ7i2fvYcFdVnWEKDaWA5sIxxr2aNO2Nqg8EVejxSmgGbKpTGrwxOcd4Hro+DK/Q84friZWKYn1SpjW8YnXzrdma9tsnaTpV9iHrwz8aHBndNjUcbm5SA+O07zTcAyPtUxR6ek1UsbKizlGJ0n0wgEIVKFSi2v3wXFSgoqUGwen1Xr3x8ex41TqPxieEEDJAxYoQQgghhBBCzoRfrAghhBBCHgDOuZ91zr3tnPvHMPZx59wvO+f+Sfz5sTjunHP/rXPuy865X3fO/SuvbuWEfDSgFZAQQl4BLdq/oiWrgJ5VnYY9wJhaBS17II7V8ZpZgOCL8RFa57R/lYMx7XNVDB8PheT2M7T9hV7tdnkohdrqhnmi7Q9shC5a7NBuOPaqmgRvNPnaLSug1lm2Rw2ewHn1/Is0plZF3ycboRXoUdTrOAYWyGitLKHXkzf6WGmQhY45w5KHaB+rAsIzuvj7g5bBdEwPj+N5zdgI1YKIfarUothDn62Sfaw+6vyciPx3IvLXYOzHRORvhxB+0jn3Y/H/f1RE/k0R+e74378qIj8dfxJCjkDFihBCIryaSwh5nQkh/KqIvHsw/MdE5Evx8ZdE5I/D+F8LA39XRN50zi2L5yXkgULFihBCEj8nL/lqrnNONgctHlSkqEGRUAWqwWjzXMxI9SBieafKVtqhLIxAB722NjlGrFOBo0/x2s7lqkcRY9En0eUx2GESrR4DKjCowsJpogSqaLp2VJsKY554Hn1sLTFZbYjnHaCFQnmTTRGiilVOAjWG49erR+OYj+eIz4ml5vjtXb7Ow2OCenkqRh3VrCLo4yKrQ1Spatu8LYQHBU63b3pU4IZ9S4jSL6hYvY58KoTw1fj4ayLyqfj4MyLyu1D3lTj2VTnAOfcFEfmCiMi3f/u3v7yVEvIhh4oVIYREeDWXEPKQCSEEEcNrPL/fF0MInw8hfP5bvuVbXsLKCPlowC9WhBBymue9mpvhnPuCc+4t59xb773zzstbKSGEPD9f14tC8efbcfz3ROTboO6zcYwQcoSLWgF77+W93Xa2ri7mv+/14bkvqBzlZqG14f3YhX6Oj1d535IMt/Cpd/PPRcDeLid40u0W1d2DFeQYS14jERFXzT+3myXPl4hsPvbPLaq7/sT81bI3n87bc0SmN4sfo9m1szUD9/Ml5ENNCCE4517oaq6IfFFE5F/83PcEEZEKQik0oKJ2aOXKbV36vreCIIQi1qELTOuayRSG7c8k2sOiTSxM7H/5e4Na5iZBFdECGEJuHZysSHtW1dAbKeRhDOn90lg7WALDYZX1PouBFqJrxpCL2EeqT+/3Y/AEnL6uvYPwCh/fO9ESuL99OsxR5msZQyzK/HnCsAkNqujgvVnfmzqf1lnEdaKNcFybYS20+ljhWnwMrUD7X/vs/Wxu8pHnl0Tkh0TkJ+PPX4TxH3HO/bwMNuf34SITIcSA91gRQshpvu6c+3QI4au8mksI+SjjnPvrIvJ9IvJJ59xXROQvyfCF6heccz8sIr8jIn8ylv9NEfmjIvJlEbkTkf/g4gsm5CMGv1gRQshpPtCruU6m6pKOiYjsQYlXRQtLVVfpJqrOoFw4j/sOP1uoW6l6E0BhVbUngBKk+zhDOYpiRr16PA517RBuUc6EUoxri3HmIiIuqi5FDaq7BlVgsEQZt0OgRYhrx1XuYoCGBn+sQG0zVfZgqM0xvKJav5Ft2t9/M+0an6d687GsrtulwI/Nx79VRES27ycLaOinQRLBcAkURVIANT69MM6hqiCCvRvqbm7y1wKDLbQO9x3nWyX1sHmUuwnKepWNkY8OIYQ/dWTTHzZqg4j82Ze7IkJeL/jFihBCIryaSwghhJAXhV+sCCEkwqu5hBBCCHlR+MWKEEJeAR4SjX20AFYQLNHFMStqAsN7CiNLQ11fk5AfdYJhf6ixpxP2saoOfsI2tbAV6aOjrKMlD8IeNNhhEgChtj/obaX9oQIeYwyjAJOf2hPLtHbbFhm3xdPewxza06uBsAvn9fzBEqjnXYDtUAMqIDTERV/kpH+XngKE9vguBkDgvjHIQkMuMByijPN1EPSkFkC081n9rtTaZ4VXNE0N/7fP6tbrJq4tD9LQEAsRkXK9ybYTQggZYNw6IYQQQgghhJwJFStCCLkwhbiJYqVKFV7pUq2lxAh2VbEwqt2IZVcc1GkwRu0MdcqIGx8VGw8tMsaxVK+qk3O50lGUebsFV2L4QThYBz7GdcbHoE51YxWoOPFh53MVS58L3FJWMYQDQyzGgjSm54YBHX0M7XBFft4OnmNVtDwoWxphxhzrAAAgAElEQVReoWOoemlkuxWPjopVcZiAcqRuPJs2bxeCx9DtjRWkAYpaYZwvIYSQASpWhBBCCCGEEHImF1WsfAjybDffpHYz8YLbNP2yZr23+/nvjs/2yxq83i1odisisvbzx7zCKOEz2VoNNQ26hU2Vl8zmF87VL1lbsex5rSDi+RQab3yKm298fdFcS6jueH2CEEIIIeShQysgIYRckCDDRY4GehKpcWtq8RvAAAq9TFG74/Y/kRTeUJ4uA4udtVDtZwUfEyEZ8MYhN51KJFnhgnVhxUMPJ7WVBSOowlo7WPZWxdDbagf76oWcynh+dJuHiUs1FGKgRwyWQAuk2v1Cl46l5+b7tCZvXPBTa5/Vq+pwfhGRIoZHVFUa84a10dqmNr2uS8fCeQ5pmvTaWsfQ0Ir6Kl0I3D197+h8hBDy0OGldkIIIYQQQgg5EypWhBByYbwEaUEhUIXlHkIMSlN1MSK2VYHBVPR0oBGN7A5Q6MZIdVBsXNxJLboBFCatc6gwDapGcBijPqwAlZgxoKFMARBBF1heSQZahI116jmioqd2Z316Akhxq6ii7UJ6juviIFoezgePXzaDYtPv72HsWkREKlCs9BwDRs/fxOj5fW6D19h1pDDGvH8yHLNKz7EeC2PUNYBi8yi3TZdgee+7oa5A1TSeb331aBxrHr2RrXNTL7NuE0LIQ4SKFSGEEEIIIYScCb9YEUIIIYQQQsiZ0ApICCEXJIQgnfdSgg2rjcEG6zJZ57roYpv0aRK1umE/oxhYACEOTbS97WGsivu04A9Ue5zZR0otcbhNezthUkXQx2neMewBrI3jmCB5fypRe94k0CLaEYtkGdSjYq+uVew3tYvH7eFgahOcBFssTFSdBG7oULQA4jla6Ha007lyGihRgr3Ot3lKrVoAcb/UHwte4xhU0e+SZVF7UFXrTZoQWpMd1iG6Zu27JTK1ChJCCJlCxYoQQgghhBBCzoSKFSGEXJAgIq3vpQcVQIMqKlCxVGxZgcIyZlY4jGAPsT7VqTrTuHwMFR7VWspJVvpBUAREnI+BDn2X14NiNa6uSMcKzgqK0LhzvMaXK2Bj3SQCfQjBWMO+bVSgVOXD+PpVkV9HDHFfZx4LY9SHx2WdVJ++28ZtKajCd7kUpNtR9fEHvRN7o5eih3h2Vaowsl0VJgdjqmKVq002tr97lh/jRIw7rvmU2kYIISRBxYoQQgghhBBCzuSiipUPXu4N//iLUBfLrpotqbs1rhaadU3us7ewroy+KG6mEahIup/gg2LJ6osF6xrmWlDnlv0aVjHeeI7m5s3Zmus3P7ZoLpFvLqwjhBBCCCEPGVoBCSHkgvgQ5Lbby9roV4TBDnpJYgcBC320bl3BvtrHqYO9dV9vWQZDPlZOrn8c2MPwwoc3wiuMAAoX8os9YeyHZdjPgnHRKmCghu6KVjsfl5L6YmkYR1Xml4c03KOBtTt/lx9fH8NYiPt27W06+l6tgGgZjLY/uNjlu/zC3Rjk0R+/WIeBFl0Mo8CAidHiBxcrNbzi0GqIx5wcA8Ysi58eAwMr2qfvHV0zIYQ8dGgFJIQQQgghhJAzoWJFCCEXJEiQtu/HiHURkSaqBVuMtTYUBt1+D4rVjaoYcJlMo9on0eaF/kjylIuKVgE7l6oAjcoNzhG3eSPsAZSt4AwlRpWdKkWmJ5UL49tzxWiMdLeCL7AuBm6UY1R8UnhGVQ7rVYHr707PO57CbTYWQKkryiEWPYAqVtbDc9Zt0zFUHSrrlYiIVOv0nOzvng5zGBZvVKxUlUJlS0Mw6qubdCwj+l73tVSs+jrtu3rjE9n6SiOWnRBCyAAVK0IIIYQQQgg5E36xIoQQQgghhJAzoRWQEEIuiA9Btn0nPVjIbqPrbAMWvyLayUrsOxUm5r7JPi1Y/JqYTLoF+5duLaC3lBhOvI3a2MY+VnlgBAZVjMyMjZa0YNgIrUALHNPQCgiqGOe27IHjCaGdzx/UCPToytfkAvSnijZGM6hiJrxC4hj2ggrbIYzCxzq8wqnPE9r0CsN+p3ZC7IGVQjFyG2G1Sna+7qBeRKQwwlSKuB1tjBhkQQghZAoVK0IIIYQQQgg5EypWhBByQfoQ5Em7W1zfWDHZoGI92Q9zPY5BCNN907UzVbtaVJGijlWBEtY7PcZzXnfDep8rUEE1Myta3UM8eNEsq1OlCiPY9dzGOaC+f5bPp6EVWNcNY+3uyTi0v38vTp+UoH4/qE7d7hbG8kAJ3ceKVldFyKOyaClHcczDHKo2WWESlsKloRgIxrIHY59RUZsoW8t6SBJCyEPkol+sQhBpu/kmu001vyxvWGJetG57opcIsrOsLgb3hg3jkHKhWFgs6K/rlz0Vy5r1yvSPtmM0C//oWjKXaSEy0MStOerreavKUjvLekHz6K5btn5CCCGEEPL6QsWKEEIuyNAguJ0oUUW8AIEXgnQML/xog2C8YKFR7fdQV0alqoOLFrpPCccIMW7dwXzZJZMClIxue7g17YGqj1546U9fmEj3XWEEurUQPQYc3xkfX2NUejv9f5zDQ7R6VKz8Po318RxVpRIR2W8H9Qrj1kNsgjyNMc+VSN2O90LpmDfULN1WNBCjHu/JKkGV7I1jHc6BWA2ALWULGSPdIb59xwbBhBByFN5jRQghhBBCCCFnwi9WhBBCCCGEEHImtAISQsgF8SHItuukdcmupTa9PVi4ag0ngPsZdXsL4RD6+GOrFEX+pB3sbGu4L7GohmNg3LqGVuzQOhYPt1GrHd4DaQVLjBZAuE5n3DfZRRtd1VyPYxoAUdabtOto58PrfhoBj8c4sP0h/oQVEMbUArjfpWCHZN27T3XR0qiWwGEpedz5eF4QT16uh5hzDIAY3Y76GoPVTm1/GJmuwRIYVKFWQd/mFsNynZ5PC92ngjq1JeL9p1ef/PRwrCod13fz950SQshDhYoVIYQQQgghhJwJFStCCLkgPgS53+/NbZsaFCY3KCsNhA5oeEVRY9PgYeweElcfN0PIQQtKVBeGt/taUnjFPu5bBSu8wrrupmM+HzOUIw14EEkKDzbZ1aAIVKzEn0hpLYywBTOU02hkHNeHjX/1+LgmUcUKgjq6qGh1u6Ri6fnsb5PapYrSpGlvfP2KBuLwYZ5DNFACwyacPx5xboVSoNql2ydqlzYhhn1XUanCpsGqgO3vU1R9vUnqGiGEkClUrAghhBBCCCHkTPjFihBCCCGEEELOhFZAQgi5ICEEabtutPWJpL5T+lMkWQBbsHXpGPa72kYL4KZMb+fa++q6StZCHy2AezhuZXQg7+Pc6g50E6/dsmbYoR96LPk+2e7G3k0hD1vAUIiyHkI43NyhxnAN7K01bYKFtj89BtoT29t34jrTmjTIAntWqRUO+011W+2BlZ8P2ulcfF1w32o1WB/VMohz6L44h2XdE6NXlYK2P93HCtkIxtjKCL7Q9YqIuIXN4Qkh5CFy0S9W3nvZ7uYThZpqflm9kTr1onXtiQ8o5JnxIWRRu/yPlUOKBTXDXPMfYsbfRmfVrd1xP7+yKeZrRERWSz6ErVSvMyhnml6KiDSP3lg016kmnMp6ZzVNJYQQQgghDwkqVoQQckF8CHLfthN16hRbCKVQpapp0oUNjWrvQcVSNQzj2wtpJvUitv6k2604CzPGPD5GdUiju1EJGhWr3g7uOAQVm7KKUfIQbOEKVdbgIk+8kBb6IRxiD6qTsr9/b3ysEfCoTmloBQY27O+exnkhAt24IKfbC7i4o8oWxq1b6pHiF17A0/l6I6jCT1736dpE0sUnZ1yEwjpdJ8atd7v8OSWEEDIw+8nunFs75/6+c+7/dM79hnPuv4jj3+mc+3vOuS875/4n59y8TEAIIYQQQgghryFLLpnuROT7QwjfIyKfE5E/4pz710Tkr4rIT4UQfp+IfFNEfvjlLZMQQgghhBBCPrzMWgFDCEFE1BNRx/+CiHy/iPzpOP4lEflxEfnpD36JhBDy+uC9l9v7rTR1evstDFug3muKlsH7aPHDe0f1fs26zO2BpYd9Y3hC4aps3xbmG3tbxfsjS7xPsoiWPLQCymAXQ0teUQ4GBrT9dd1gIRttfYK9oiBkI9oHyyIFJmjghM4rAlY8DHSIa3VxLQ7WpHNg+IKOYc8qnbePFj6kfZZshEW9yrarxQ7tfPXV0PfJQbiIP7ARmveF5oefziHD/Z/Y40rtgcUkvCL+Hq2v0r7x3tHm5s1xTM+7gvCKzRufGrZB4EdRpteKEELIlEUmf+dc6Zz7RyLytoj8soj8UxF5L4Tx0/UrIvKZI/t+wTn3lnPure2TJ1YJIYQQQgghhHykWRReEYbLVZ9zzr0pIv+riPwLSw8QQviiiHxRROST3/1dYaacEEJee3rv5X4mIVUDKDAldYxld6DwxNAKjGA/nGPYZ1CnCoilsPbB7TqSA7EXqgDBVKo6odKh6hGqQ34/PLaUKLcwebSAY4S49lGdgjm6bR66YAVpjCEboCrt7zRuHZSblcaYp+RQK1+2ivug2jQeS8MuUE1qd9m8p8BQDF1zCcfSmHc8vqptc8paZ4R/VM31onURQshD5LkaUoQQ3hORXxGRf11E3nRu9JR8VkR+7wNeGyGEEEIIIYR8JFiSCvgtUakS59xGRP4NEfktGb5g/YlY9kMi8osva5GEEEIIIYQQ8mFmiRXw0yLyJTf4OAoR+YUQwt9wzv2miPy8c+6viMg/FJGfeYnrJISQ1wq06andz6N170SgBTYY18dW0/EGAy3ifB48eyujGThuF5GDoIoTDdf73Npo9qwCi51a9bDfldoCsU9UcH38WWR1iNO1j+dlWP0gqCOoZRGO1e3u4zY4/minA2vhLk+XUBudZc/zEIahYRWhiNvaXVY/CaqItkAc0+cOe2LpmnW9w7FWsS63FnbGGP7OWKCVkxBCyJQlqYC/LiJ/0Bj/ZyLyvc9zMG2MOcd6Nd8Sq/fLbtdqe8v1PmW/oEZEZNt380Uisg/z698azSVNFpg1V26Zo7NeWLdacG/DZuFc0i8ILOmN+CurbOEHul/4ei5h6X0ehBBCCCHkYbMovIIQQsgHixVKUcOYbkdla9w2iVbPY9GLcSypWPqogjpVpzCwYpwPqkZUvfJtNoZBFaoKTVSnqA5ZKtYkAt2pYpPGrAscY0AFrFTDK/x4rLROnQPX5FQd26WQBlV7VLnCfVDFUmWpWm2yfZ0Rn15BQMX+9mk8vqEYRgUKVadRiSo6oy6/kITneBjtPhy3mvxEUAHb3w+hHfXmZhxrb9/P9iGEEDLwXOEVhBBCCCGEEEJy+MWKEEIIIYQQQs6EVkBCCLkgQQZ7nxVOgagFEOt0DO8dvY7v4n3ILYM99Kla2kRQ5+lHOyF+TMS1FGvYYbCOuSpZ4ly0nxVl6rfVxzocC8aa1eJXlKlfkloFTUsg9PQaV6kWP2N+PL41r/Z4Kg07X7t9b3xcrYfznfSHMtaHFsDxuAcWQNwvxMeT8IpoS3QL783Vc8C50Z7YxSCN+upROq72wIK69eNPDscH+yZaHwkhhEyhYkUIIYQQQgghZ0LFihBCLoiTQYXCUIq2G0IJMBHVUqyaalAfrEjsBlQP3Y7hFWUMdsBQ9ko0qj2NaXjFeFRUfcaQCVSCioOfR9I0dQwDLTRavAbVyVBlVDEpYV43RqobaymGj7aySsraeCyIadd1ThSr0hiLj1HNUVUIQyb6+LiUpBhplHrR5CqSFSyh6/RGFDoGVXTbQcWyYtRVTRvWFJUoUM5UDcOAjjECHo6xvx9SXTFp1VLyCCGEDFCxIoQQQgghhJAz4RcrQgghhBBCCDmTi1oBlzYIfsPo2/KizN0gvrRGZGqrOcWSquYDbOq7csua2G4WNrut3YLb3Lv35mtEFjUI9rt3F03V3r6zqG73/nxd+3RZL5bd+/Nr227zvjyEHMM5J5tVI32f3ufqOlrXJra/YWzTJJvcOlq48L1oE+tq49/3GoIaqrgP9rGqC7X9nXrXwj5WfvpTJNkDPfRYMnpVjaCdzwiXKJshtAL7YhWlYT8r9OPLeI888b6J86b50/PkixjGgXbLeDoF2ODGnlVGLyisUwugBkaI5CEXftLvK++ZpY/x2dL1hV1+PtiLSq17+7uned02WQHrq5tsuzf6dxFCCDkOFStCCCGEEEIIOROGVxBCyCsAgypUnbpZp7AFK6BCFa3HEKetShXWq7KFY+WoWOXqFCpgqqY7MdSp0E1/wnYHge4aGtHvkyKiqpPvk5riolLkQHW3gipQUUqFcQ3W5cG4DdUpZ4RnpFPIAzVQpTmlDlngfBpegaEPqiiN6hTWx8eoOqnaVcLrrqEVfp+7QFCJshQwBQM6NAwD1bay18CPVGcpX4QQQgaoWBFCCCGEEELImfCLFSGEEEIIIYScCa2AhBByQZxzUlfVaP8TkcljZW2MqWWvD8l210R7oBWug72yNDRiLlzHx7lLtQJiEIQ+LiBMIoY4BId2vcFCVsUgChGR9n4IvHFG2A5a0tQWh4EVYw8qrIs2P+eSfTJtizY5CNFw49rRYthk8xbVMDbpBRXtdlintjy0yel2tPY5w9J58rzGUIzTYUPWvrrOwug1hfNZ1sLRAglrL+NzgHZDs0cZIYQQEaFiRQghhBBCCCFnQ8WKEEIuiHNDCAWqSW03qBQ3q6QgtDE4YAPKlRVUUUQlBuPWNzG8osL4diOCXMcwbr0YlS+NUcdodeMjY5y3gqFhLf1um+Y1AihOxbKjcjJeAjQ6cTgM0jhB0JANSzEzxqy4c4xWHwMtjLpj8yzZpuoQBlDo2CTkIo51GFQRt2O0u9ZV66vsWN0u1al6haEZ7dP3suMSQgg5DhUrQgghhBBCCDkTfrEihBBCCCGEkDO5qBXQey/P7rezde3NvLVjclP2Cawbug+pF96Me13lNwRbbBbMt5m5Mfl55roulr2Mzt/NF4mIdM8W1DxZNtf+vdmS23d/e9FUT/7fLy+r+8o/na15+o2vL5rr2e387+uzZ/M1hChVUcrHb66l9ymAQoMq0OLXxH/7a7CfjXVo+4tj+F6nFkC0/6nFrwF7oNXbKrvaNulZpX2s8P037hHSvwPtVYUWuyDH7WTY20p7YGEPqtECCIsbrYU+rU/3UYshzqtjfXubHXdyLAPLCmfa+DR4w3jf3t89y+o0qELAEdmPFr+7bKyU9BnkzeMPT1S/S/ZAtWVu338nq2/vU13dDHOv3vh4Om60Gfo+PcdW3yxCCCEDVKwIIYQQQggh5EwYXkEIIa8AjFNXpeoxxF83hqqtStU1xGmr6rSpUjiEhlGgiqWPJ0EV8XENypZTecgbysSoXqFilctJGmM+iTsPeRS5qk4oCOk+k6t+KoqBSqNqkxXL3nfb7FiKbsNj4Tp9F8MjDGXG73fpWIYqNUaWwza3Gx77SfDEbnI+3gilwOOrAmWFV2y3qa4o4msM6pgVAa/KVlVBVHu3n8x7uI+CyhshhJApVKwIIYQQQggh5Ez4xYoQQgghhBBCzoRWQEIIuSDODYEUJYRIaEAFWvd0rA8p5EK3Y3hPHS2AGNNTGJk9avtzkm/0ErKx0eI36X8VHzsI8lF7YAGWvBh2gL2rLOteGsO6fTws2OliCAaGTIw2NSPfZ7Tk9bmdzxn9vCzLWwl2S7XloRWxiLbNAMEOOtZj8ESZf8zqPJbdsAe7oaLWPdeftvO17bCW9fXpoKWu3cd9820Tq2adz+MWBi8RQshDhIoVIYQQQgghhJwJFStCCLkgTtxErRIR2UeV4AZVkqhUYTx6EdUWDLYo4xhGpmsYRYEx6paKNd+N4iBaXbHi1iGW3elHS1JfVKkyo8uNY2CdRrVjnTVPmm8auz5ZOYxZipaqSBgo0cXwCFRrVKnCsAcf63wLgRLxNcW6LsaYqzpVgKo1qlkQmd51/eTnsL5wfGy3zfZFZeuwHtG1iaRI98lrYcS8E0IIGaBiRQghhBBCCCFnctkGwSHIfTvfXLDt5hsEf5AsaSIsIrJa6C1fL2kQvLQp8YLmv8sb/y5s6tvPx+mGNm82abF98tX5mqdfWzTX7de/sqhuSfPfd7+5LDL47j6/3+EQNggmz0PhXNZsXJv8YjNgfV/CaHVtZo7vWZu4D8aoV4aKpdurSQR7MakXkanylP2/t2twG2yfKB163xPcJ6XqESpHLipbGIsuPlesXLwvq+9QTZk2Bp7EqMcxbBDc74dj7O+heW87/JvvQbnRe6ZQxdImwKjwdLuhrlpdjWMaTz5p+KvKlqEIqbLVtmnteu8Uqk6qRKHq1MX5ttu0b1UNry2+T2ks+3YHdRr5/zj/PNwv+NwmhBBCxYoQQgghhBBCzoZfrAghhBBCCCHkTBheQQghr4ANZF2rxe9xsxrHLIuyjt2AlVADKMqJ7S8f08doBayiPbA0bHy23c9C7YFWyEUO2t40Uh3cgeKjBRBj0dUC6DFswgie8CesgGonVPvfcKxY1ybLr4ZXYOy5WgD3t0/HsTJGq6v9TwRsfPvT4RVqD7TYbtUKmJ7/7W4YW6/S6662Pyu8ooDnrutcXBPEwsdfmgrs7T4+x9Z8HuL97+5oCySEkGNQsSKEEEIIIYSQM6FiRQghF6RwbqJWiaRI9RairK+N5qwawd6C6nMVwysqo/GthQPFKjUGNvYNRoPgMSfBqHf5x8m0GfA+G9OACgdjoTseBqONgof52rg8bCT8fNcKNTzCG9HtzmrsC3VWI19dSwdR6akeYtl9rg6lbXkEelKO8m0FxPG3MWSius6fhwKy9VME++njprAMDM1YpkwSQshDhIoVIYQQQgghhJwJv1gRQgghhBBCyJnQCkgIIa+AHgIBbrvBJvdmkyxX+2g7m4RSLLS6WcEX2scqhGT50vnQBObkwOqFIRYLAyo00AJ7Vo1TWGMQRDH2uxLDJod1cS2hz9c0zmEEXFh1BdgJu2jZC306b2/0cdJ9sY+VBlRg3yc1Obb3qU6DKVJ/KgybGM5HAytEkk0P++qptQ9DLhTsT6W2Q+yBlY6FfbmKyfEH9pPj43EJIYTkULEihBBCCCGEkDO5qGIVQpAtdJM/Bl7JPZdywRXetXGTsllX5Ff8LK6K+fmuF9SIiLj+eCzvSP9k0VzSLavzu3dna3bP3l401/3Tr83WPPva7yya6+nX/p9Fdf/fO09na9795nyNiMjtdv739XaX3/RNyDGcDPHqNbyfaHhFA/HXGlRxXeUhFhUEFhRRncJ3Oh1rHNYNP6cKmItrwrj1Q8UK/1+j1Q0lCJQtF1Wpvs///WAAhN8PKo636gIGRWxjXTquM96PdZ5gxK337X2cC5SgGLOO8ecaSoFjGqmOkekafNFDUIVGpaOqs729FZHD+HRVgobnEz8WVTHCeo1Wb+r0uTEGWhgqIu6ratizZ1sYK+M60vlo9DoeI60JlS0qVoQQcgwqVoQQQgghhBByJvxiRQghhBBCCCFnwvAKQgi5IEFE+uBlAxZkDZFAe+A62vR6sHo1cTta/NSYVaDFT9Til8bUFl0I1umiMADh0O43Y80e9011wegLpTa+gNa1eD7Yu8raV22BuK8ZWnFgAfRdbt1T+59IsgVaPalCj5bFGEoB9kC1Irbg41PLnBX2gIESatWz+lip7Q+3ab2fsclbPaaqrpzMi48xDEXXjHVWbysM0CCEEDKFihUhhBBCCCGEnAkVK0IIuSBOBvWoBfXhOoYJ7FGtUXWqyAMtWlBuNq4c510CKls6S3nyGpu17fSYqjluLjxI487LFNDR9bfZvi6eI+ZV+Bho4co6DTotyMMwihjO4CEgxCgbFTMHdXo+E8XIUI80sryFkIvCF9m++thShA7nmgPnGKPVYe0aVIF1o2I1k8e0Xg/PLapna8nDVAghhAxQsSKEEEIIIYSQM+EXK0IIIYQQQgg5E1oBCSHkFYChFLfdfL80EexBlXu40FSmrYaC5FYzH9JY5QwDYdYXyQpMWDZm9aeaoBa7dpttmgRVaHiFGWzxfH0PHYSGaCgFjukxMLwCe1+NS49WvQ5eO+0LNe0jVWZj7b6bjGFvKLXsbc0eep0xltB9dX6RY0EVPptN7YMTy2C0AOJ8Vp8rQgghA1SsCCGEEEIIIeRMLnrpKYQgXXf6ipuISD8TKfs8FNYV2QOacuYO3kg9dyO2zregM73z+RVaE383X9MvqBGRsH9/Ud3u7p3Zmu2ztxfNtX3/67M19+8um+vJk/tldU/nn4+nd8sUgtv2+M3lyv1+voaQUzRGUIEGVazd6cj0U5Quj1bH98Q8xgKJe6AipI8NlcgFVHiGf6sBxlRZ0th1kRRAYSpRMDaqSEZUu0Adzn2Iqk4Yt57Wkca67X2s38H2YV8MlNCYdSsWHRUo3Y5R6Ck+PUx+TrcZCqDPlS1Ej4traprhY17VNAQ/j7ti2EcDK0REvKFOnQrcIISQhw4VK0IIIYQQQgg5E36xIoQQQgghhJAz4V2ohBByQYIMwRWN0URoU6W3ZA0b6CFsQm3GGDqh1r7SsD1blsHp1TS19nUzYwfb0Dpo1sVNhp1PZgIoku0vtxHivrg9W2U32N6s0AmP1sH42KyDMV3T3V2yB6rtzrLGYVBFCpTYZ2PJJgg2ypDPp5t7eK719cb6MloFC8OOjsdQS2NVYaCFcR4xtAL7Yt3d51ZKQgghA1SsCCGEEEIIIeRMqFgRQsgrYKI0RPWhhcCCJl72qkHZuu8HBaGEEIUiROUE5vNRqfIuVyGmWkZx8BMfx7VgaE84rJExyCIY6piDtYcuBvagUhfj2B0co+/vY1mT5nFRxcJlxhhxB3Ui7eS4BSgt+syWdarvtQ7W1Gs8OdSlxxBocTI8Ir2OqgoVk4CSaTAFbuv7XInTQ03CSHQf+J3R3ymNeMfH00CNPBhDt2PdzfU6HiJkdYQQQnKoWBFCCCGEEOmues0AACAASURBVELImfCLFSGEEEIIIYScCa2AhBDyCughsOG+Hx4XRq88yzKIWMas6kSQBRrYnNW/6jCM4mSIBQ7ldVZ/qsn2kPenUhufGU5hzXfiGG5iO8zrsFdVtjYMuTDCLcbwCli7ZQ9U2x2GR1ihFYdA/oVY7ru9cT7WOvQYaA/U1w/rtN/VdO15Dy6rjhBCyMBF3yGdc7Jqmtm6pppfVmk01HzZLD1iuaSRsNFg84XrTqRyIb5f1hQ3LKhbUiMy/4fVMNd8jchyb//0D4gjNct6Qsuqmj/mib+NCCGEEELIA4GXnggh5II4GS6+oGJlRa8rGF6hitXS+AArln2ybzDi09Me+ZDWGxd8UGHSizjBqPP7LUzXT35ODwXR6uN8acy5XNk6vHhkxb1b0erT9bXxmBjLPpwHxpNrpLqlDuFFIEvFOgRVyXMu1JiR+3EtGAE/Hss62HU+hOfN8ApCCDkO77EihBBCCCGEkDPhFytCCCGEEEIIORNaAQkh5IIEGYIrNmV6+9XQig3cX1ob9kC9f9Oy+BWGDczG548n92ke2AODUQ9zuBiHYRneJhY/tf2BdU+tgqfsfLh9Yu2T4545rfMz929a9kANvMAxtfOhnU7HvM/DKyb2wP54UIVaANEtqI/Rcadje+wn5abb4hlla+o6yca0B9ZcEIWGVqzX9Th2d3888IMQQh46VKwIIYQQQggh5EyoWBFCyCsAAwtUAZpEq4c8gr2NCksTkppVjapH2tc7SwkZ/qecCFvFwU9AlSpMOQ15ffCDsqNhEsdQdaooUzKsmRqqY6DYjUEVkqtdRZnUlNAfT1FVJaqAeYt6NWwDdUpDK6r1Ji0pbt9uU52GOBSQUKsKEKo6TT2MWXHrGjYxfU2syPbhZ20ER9RlOr6qUqhEWSmpqrxZ23Ds5mYtItPAiiWpq4QQ8lChYkUIIYQQQgghZ8IvVoQQQgghhBByJrQCEkLIBdE+Vq1hgyuM5uKlA3tgtI71EESgljk0kBVGp6sUbpFbEE8y06RcLXa+P90fKk1nhFecsgTC3NgXawyomDnGybX0x5urYx+rvhsCNYqFjenxddTwCqv/k86HwRJq98OgCitjwlpLmi/vrdXUyTJpWgDLMlu7gqEdc4EXhBDykLnoO2RVlvKxG6P74AGbppmtacplPu9TjTcVq6mixfLUrQXM/LHyKnHGB2vGgudVRKSs51/L+vpm0Vzq959ju1v2B94S7u7mE7Cq4vgfZ4QQQggh5GHAS0+EEHJBnDgpnZMSwh5SiEF+8WYNsexj3DqoFe7gJ2KIJFNCHp+ehVbMxK2naPMUk54Upt6oa/M62NeKVte5cUyVskld18Z6I549qlMYo26h+1iqF6o1GGQxHj8qRVUFitUYd54uwKiyVBSqLKX6vRERr6/jXHCEqmJYp2OdMa8VAe8Dxsf7yRy4dvLRxDn3syLyAyLydgjhD8SxHxeR/0hEvhHL/mII4W/GbX9BRH5YRHoR+U9CCH/r4osm5CME3yEJIYQQQh4GPycif8QY/6kQwufif/ql6veLyA+KyL8U9/nv3Vz8JyEPHH6xIoQQQgh5AIQQflVE3l1Y/sdE5OdDCLsQwm+LyJdF5Htf2uIIeQ2gFZAQQiKXtMnURW4F3FTpLVlDBCa9rdQS9gL3e45X0az7OwPeJ+iNsYN9l94jatjpwsy+oxXPCKpAe55a+vA+zkMLoG/TPZLOuC9X+1P51rD1GdY5DHEY6/zp8/EveD8t2jh93trq9DFhTdq3azoWLaWGtXDSNy2e73qdnuMl952SjyQ/4pz790TkLRH58yGEb4rIZ0Tk70LNV+IYIeQIVKwIISTxc0KbDCHkYfHTIvJdIvI5EfmqiPyXzzuBc+4Lzrm3nHNvfeMb35jfgZDXFCpWhBASCSH8qnPuOxaWjzYZEflt55zaZP6Pk8eQMKhQqOZEVQHVKVUsMNBCI9rLPo1pUAKKGl2cp4T5dKyGujGgwuFHweH1Nvj/sT6NadhEUaaZx4AKUOVCtx12nUkd1aAKZySPTsaskIeoSvWGUuZiCAgqV0VUu5wRaFGtN2ntOt82hWyo2oMx5kUx1GGwxXqVJ6MeKl8YdqFBERiZroEWpsJkJJTgMXU7junxMYxDFS1cy6PHeWJrvSC1l3y0CCF8XR875/4HEfkb8X9/T0S+DUo/G8esOb4oIl8UEfn85z//nBorIa8PVKwIIWSeH3HO/bpz7medcx+LY58Rkd+FmqM2Gbya+/Tdb77stRJCyGKcc5+G//13ROQfx8e/JCI/6JxbOee+U0S+W0T+/qXXR8hHCSpWhBBymp8Wkb8sgyj0l2WwyfyHzzMBXs39rn/5D4TSuYk61ccocLOn3kzPOB/n8ah2nbpkhoqRP78Hm7ofzQbBM/dYpfupjNjvrs3qMCrdau6ripW17VQzYAtsEBxONPm1wEjyUQkCpayVYS12fHp+T9aqyD+q7YbD+ViKgE/bVKnC+iYqUVacujZIFhGpVsv6CZIPJ865vy4i3ycin3TOfUVE/pKIfJ9z7nMyvMf93yLyH4uIhBB+wzn3CyLymyLSicifDdY/VkLIyEW/WBXOyc1m/k35ekFNvbCXxpLmv1an+ZfOB3nMhXMtavy79JAL5yqqBQ2Crx4tmmt183hR3Q3Ydc6lqef/iVTVduFs9+cthrwSPgibDCGEfBgIIfwpY/hnTtT/hIj8xMtbESGvF7QCEkLICWiTIYQQQsgSaAUkhJDIJW0yqKbr49JQ4qd1MSYb6ooXiF4/jVrR4jECWPyMuHU9ZQ2dwMdWtDpGput23NeqUwsg2vk0jALruv0u1uUvg87Rb5NyHCyrohHtro8x7MGKXlcwAKJt4zl6tGq6yU8/yVPXffN4dDy+hc6HFkMd6zorPj4do2mGNaxXKYxDz9uyBxJCCMnhFytCCInQJkMIIYSQF4VfrAgh5IJo3Pq6TG+/qkpZY1aD4EJOq1SjAAJlSdkCdUTvlTRDLKzGtj5Om+awcpVPKkEo6hl1GlrhJ+ERRmPe2PwX49NVqfLaIBjCLoo6v99TVSxvxK0vbRA82cdoFpzUnlyBOtVc2ArAmEPvF8b6qtKgilx1Wq/z8AxUu1TlajYper69572ihBByDOr7hBBCCCGEEHIm/GJFCCGEEEIIIWdCKyAhhLwC9miDi72qtmB5U1sghle0cR8cq+JjD7a/LoZCVAH2jWObSauE+NjBR4E+Dl2+LdrZguTWuaKss8e+S60INKhC+17FwjiWX+Mr0OLn848qtfFVzSrbphZDV2CIQ+y3BfOW0R7o2/x8qvUmHzMCIDT0YTjG8Hy3e3gdDbudPn1ttOxhKIUGWaCdz8cXF9s/aB32otLHN9f52tcr+F2I68PeWul80jHWjz+WbS8NSyUhhJABKlaEEEIIIYQQciZUrAgh5BXQYpx4yCMgylGJyoMqsN7H+AgPMRJW03P7KprPt4bDsWXBCRitjkrROBbXFMQItjBi2a3wCA2smOxrBFtYeEPFGtdmBGBYdagOWYqRVadgBHvXTWPRJ69Xkb8mVto5zpfqjl8rxTVppDrGvOtayqqWQ7pdCqyoVrkaRgghZOCiX6yqspSP39zM1t2s17M1G+PN36I2rA6HNMYH6MtnoVho/IH0onNZfyiYdeW81aOsl324ohXoGM2jNxfNtf74ty6qW3Ke6yffXDTXdpv31zlkrrdM4r2FdYQQQggh5KMGrYCEEEIIIYQQciaLFSs33HH8loj8XgjhB5xz3ykiPy8inxCRXxORPxNCyO8AJoQQMhJEpA9eSlCjm2jhKg3bXzGpG9TYZoESf4gazNB06Kxra+Px4k/Dpjcpj2EUwSd1ut8fV6qtPlbY92pUnGf6WFkhChpoEQwb4djjCnpWWXXjNliTPp7Y5LrjajaGQnRjb6085KIpqmyb+Pi8F9D3Kr4m2GPqcC58jDZBtQd2RvAGhmwoVg8ytP+5F/jdI4SQh8LzKFZ/TkR+C/7/r4rIT4UQfp+IfFNEfviDXBghhBBCCCGEfFRYpFg55z4rIv+WiPyEiPxnzjknIt8vIn86lnxJRH5cRH76JayREEJeO1CdUkViU1XZGNJrZLmcVg18DLdwhgJ2ZI98yFKqxuNDeEY4HvbwIliKiYWqU8WJ41pKmDfmP6VwTfbdnzZlmEEWUYGytk2UqufEDs0osnmryh2tv9qkqHqNd8fXUZ+/AtTB/d2zF14zIYS87ixVrP5rEfnPJX36fkJE3gtBG53IV0TkM9aOzrkvOOfecs69tX3/yVmLJYQQQgghhJAPI7NfrJxzPyAib4cQfu1FDhBC+GII4fMhhM+v33j8IlMQQgghhBBCyIeaJVbAPyQi/7Zz7o+KyFpEHovIfyMibzrnqqhafVZEfu/lLZMQQl4vsBdVGS12910KE7g2whn20Zp1B5kDj60Qhzj3Hux8TYhWMLQY6mOHHwUH19twmxvmCy7ZxTS8Ah15RTmEPEyCKvb3cVta72g1K+tsrDQsaa6EtcSeVkWT7GwuhlyEYqjvwbqnYRdoD/T7XZzXCIWA51W3oxVQ14fPvtrtui497+v1caui1k17XOUWTA2esFo7TMIr4ut5c5O3LMF927bLxtQ+WF+lliirNz+RzVPWq2yMEELIwKxiFUL4CyGEz4YQvkNEflBE/rcQwr8rIr8iIn8ilv2QiPziS1slIYQQQgghhHyIOadB8I+KyM875/6KiPxDEfmZD2ZJhBDyeuNDGEMfRFIoBQZWbKP6YkWwY3t0zSnwqIAV+TWzQoZ5UA8pYWuGM+LWD6PYxQ52UFWqhwbhOub7NtsXAxP0MSpLqjahAmWpTKpoafAE1mhohXUsjG7XOlRm9ndPs/n0GMXkudYxK1gijWlsuhVeYe17KmYdf2esfdM2iO1v8pj3uhmeA+scJ6+toZASQggZeK4vViGEvyMifyc+/mci8r3Ps39ROLne5BaFQxrjQySba2HaVfMBJVU9DyEsSHoyEr/sugUfYm7Zy4gWnFOU9fxrVFbzNSIivrmerWmuj/eDQa4++elFdVZ/m0Pqq0eL5lo9e2+2xrLnEEIIIYSQh8Xz9LEihBBCCCGEEGLAS+2EEHJhDhX3U8o6WgFrta7NKN6qmZ9whh1g2f3ix4PVz2pyLKM/U5/3exoteDNtqjTIAo/aG/2jCiNEQcMoxhrjee2xP1W0DjqwHerZW2eNangvw5rQsqh2O3QHavDEJGQi9ray1G615+l+IincojLsjxh8occvq2QWVWtft9vKIXgMieezQqtk3LeQPMiDEEJIDhUrQgghhBBCCDkTKlaEEHJhfAij+iSSVKkG1IB1VFM8KEYao75ZcB/qqWOn4564tmYpVSfUK7yH0xWDOuIglj3IPm4zQicmdbmkVWjc+WQpeRjFIX6iJuWhGEHj2fvTMpqqNLjvqeMiY8jEJOTCx/WFSY21H9ZZ23FbMeaNoCpnBV8Mhev16ftRrRCQ9un8faeEEPJQoWJFCCGEEEIIIWfCL1aEEEIIIYQQcia0AhJCyCugn1j8BltX79OYL3Lb3T5avO671KLgpsrtXGr368D2p49rcJ3pVoctG/Sx6+IAbCvisUKXxtSgV6Q6tQVObXdDOAK2atDtE+ta7O9VGlY7OIR0u/uhDgIlRsugWtggzMIKuxjnMsYKWFMd50UbnLm+Ou/75OJzVrTQCyra+NrWTf5fxLb9dfF8rLCLSShGfFxf3cCammxsf/dMRESq1WYc09dg9ejNcayBxwruQwghZAoVK0IIIYQQQgg5kw+lYtUbV+xelNbPZPvK9MrxKboljX+X1hULu9e7PGY4r1n2Mga37GZrt6Bu8Y3bC5oSa7zyHNX6alGdN6KZX5Sw5PfnAzweef0JIUjv/SSiQRUmjFYvYyR3aTRD7+E9xkfdCd91rAbqKmx4qHRjVLp1jc0KHo9jGHoR8roxMh1i14t6UDr8/h7qoppTJgUu6PsxBk+M86U6VarM96IxdMFQeJqkXLn4b7cENUsDL1AJ27fP4nyGSuXTWH8iUMNSliozhGQabCEiUjiNcT8dcjENyIizxXMs4Hz03PD9zWqaru9tGvIx7Htc+SOEkIcOFStCCCGEEEIIORN+sSKEEEIIIYSQM/lQWgEJIeR1xTknZVFM7HraT6oAi50GWTQVhELE7Q1YzYpo58tNYiKVYQm0sex+Vb7NcjlbdTqTYQVGe6CAtS/tU+ezRccaWuwsu53vou1NLXtg3dNAC7S1qbXPgZ1PLYhWb6uJZbC3Ii/iOdT5eU9txcPjqsqvbXbjtGCtLI6HXKCdUK2A1vEti/TExnz3VEQOgi+KvH8YIYSQ41CxIoQQQgghhJAzoWJFCCEXRMMr6iqFtjRRaWhAYWmMIAJlBXUqYliBFbNriT+ne0Z9IhxXZKbX5Hw2lgJwckUKw3FUdUL1SZUiB+qdbg990k6CERQx1sVtvscAjGFbv8UI+FyVGlUaeI5V2eohvv3wmIePT9VZz8vzokpV16VzaJrhOcPzsgI3dC2oTllYQR4YOU8IIWQKFStCCCGEEEIIORN+sSKEEEIIIYSQM6EVkBBCLoiGV2D/vD4MZrwWLFxqBcRAi320Zu0mFrf8bVz7YmFLQG/4/nQFJfbC08dOrYBw/c35aQ3O4iAwIdoIsUddGG1l6zQW4li1zuqsAAa0EfbdNjvG4Rwi0EerGuYrjX54GOKgNrkCwibK/nigxWR90XaHQRF6Hmis1AiMvhssgdh/qijyY7TtsHfT5OfqoCeiHh+fO6v/X7e9ExG7J1Xz6M3xsVoFsR+Y9boQQggZoGJFCCGEEEIIIWdyUcXK+yDbNo98PWTTzF8R256Iup3M1c+f4tK59mFZ6OwunL6qOcx1/CZnpC7W80VLakSmV5RPgFeUjx6yWnbMop9/vZ0RyWxRLrxSuuSKamFcqTWPud7M19zxCi5ZjoZX4LuEKkwlBFC0MW69dEl20u39zHtRaQRZFEa2xckra6pKYYiFqmegoknQx7CmuK9zad8xRh1OXOPYA8Su6/uPhzGtw/O2lKox8GJcJoRiqBKGEexG3LqqUvh+00eFxwqCKHz6jNHVWSEWeNzgp6oY1mu6PoZSaCy7dT4WqGLq+RRNrmKhAldf50EWquQVULf0fZgQQh4iVKwIIYQQQggh5Ez4xYoQQgghhBBCzoThFYQQckE0vAKpDetYH62ATQXBAdHqVoIVz7L4KdXS3lamtXDhdbfRMmjYfgtYe/yJFr9xCpf3wHIu2c+8MfdoIfZYN51bAytERHzXxnLs8TSsD61u+kxgAEWqzy2DHqzkp4I3LKy6cQ54YQvj92MM2YA51G446a2lgRqGDb/b3RlrSjbpajVYoT0GeRh2SEIIIQNUrAghhBBCCCHkTKhYEULIBdHwihqu/GvYxLpKgQxNcfy61wr2LWJ+egE56oUVXiEL1atxoctCffAII6pAQdy7Rqtj6IQ/EW5jBUCgshVOKF+qimHAgzVfiGqTN4IgcExVn2CoWBgUoWqXFSxhHf8cVKmaRLuXm6PHt6ivHp3cruEVFYT47O+ePdc6CSHkIUHFihBCCCGEEELOhIoVIYS8AjRiXUSkj49fpEFwVQ1KlAd5aGwQDFHtur2G62ldrKvN+PQINgMORoNgvf/JbBqM8ejDsQLcO1WUg7Lky6Q+uXgMJ6DoVcM+2hRYBBQrvJ8otrHQM+gNxWoae57fYzUeE+5dUmUL77HS+54wsryMY/1+B/MM9yzh/UzaeLfb3ovIVBFSBQrvBet395P9jq1T14dtIvQ+KaSL81nR6aubN9I6rwdFa3rPFv9sIISQY1CxIoQQQgghhJAz4RcrQgghhBBCCDmTi2r6IQS53x2/WVm5Xs/fNN0a1g2zzlsxwlPuu2U3aS+Za2ndrli2/qqYf4lcsSzaVxbWFeV83dIbsZfMhTezfyDHXFC3pGbxXAujlQlR+hCkR6tXfM8oIXSi9dHWBXY+3d6b8eiJU+EVaBlsRgtgyOqTtQ+ONYZSFHndJOyimNaLSAjGmuK//anVbBjDcArz374ZbhHtedJn+2mgw9z7iF9Yl9ab1xU+f98uIcZcI9otK954XPj90PcYK+7dev8pjPOuVldZHYZcaJAFhnZoeMXk9fmAQzgIIeR1gooVIYQQQgghhJwJ70IlhJALUx4oShq9PlGsoiJxUydFV4MsGkM1sOLUy6XXziwFTNUmP6PoG9HquKpUpx830FBXgyqMBsFBkmJlxpfHfQKsXePbx3kN5aafafyrDYfxmPp4GmjRT35iXWHUWY2ELbXLwlK2LKVKVbFiEmgxPO8YqKFMnou7pyIi0ty8mdV52JfhFYQQchwqVoQQQgghhBByJvxiRQghhBBCCCFnQk2fEEIuTB+CrKv09ltGW1sN1rUm2sR66HdVRLdfVaRrYpYFcClqojMNaWPPqiIfs8A6seyBuT3PPGxYFuwTTqwlGHY+67jan8qyBwYjIAnrrGAbZ/S2GuvBQtcb1sbDtWO/LT8GVeTzocXwcA6RZN2zLHwaWDGs2ZgnjlXrFHyxe/re0bUTQshDh4oVIYQQQgghhJwJFStCCHkF+IAx6sPPPSgNjRFosdWYbhirYlABxqjr3L2DYIeobNVwPU3rSocfBXGfcTpDibJULJzDVLti2AQIbBq3rqETiIM2DC5u12CLYaetTpLq4mMXj++7Nts2UYJSPkZaU6zzZT5vMYmFj2NNHiLh23Tcar0RkanapfNpoARGsYf4eKJE7e6HuYzI9BLWpGvBOiuqvd/eZ2MhzqPrFbGDLFDlIoQQMoWKFSGEEEIIIYScyUUVKx+C3LfzDYJvt9vZmpv1etExt5VxSfKwxvCWW9wvrNssiNDdLWw2XLr5uk2x7LmQhXXB3c3WlNWyufz+frZmchX6IwgbZhJCCCGEEFoBCSHkFdBCwIE+LptVPgZBFWoBxEALH/QnJEWcyLPowDLYuBOmhbE/lTfGDHugzNQZ8xWl2uhuszG8KGNefNELGkYQxNgnyuhjZYEWP2/sewpzX+PimjVW+PwjeDwuXMiz7Hyn5rXGrF5YHuyJZbQAYsgFWinHtSzsvUUIIQ8RWgEJIYQQQggh5EyoWBFCyCsAAyhQlVI0yMJSrHBfpbDGzohiN5UoK+LcUrbMKPT8HL0VSiG5fVvrMIrdxeMG6aFuHw9/XMWyotUtrDksJcgvrTNs8KpEYTy7dVwMtzg8hhWogWqbKlV43no8a8yKkccodiu2nRBCyAAVK0IIIYQQQgg5E36xIoQQQgghhJAzoaZPCCEXJMgQPtGAXayO9qsabFi6vYcE0T7a/a4NuxaGV2ivKOxtZZH6WFl1pxJJZ9JKR3vgTFm0AKLFbymhn098PRVYMcxx3B6I9rylY6esgm7SP6udrA+te9q/qgDL3djvCnpMjf224BzLeAw871NWxWq1ybZN6orcWtg+fe/kPoQQ8pChYkUIIYQQQgghZ0LFihBCLoiTIXwClSgNntij+qBBFRBecd91k20iImUz9JRDcej/b+99YyTZzvO+51R19/T82b17l7y64r8b0jFlhw4iSiAUGxIE2YodWzFCGTAEGYnDKELoD5RDAQpiil8iwBDAALFsBQiEXFm26UCOLFByRDiCYoZRkAiI6IgSI0okBTIKFZG+vCR1/+3OTk93VZ18qPNWPdX1dnXt9kzPzO7zAy6m9tSpU6d79p7ePs9Tz1uYEkUqVtNGeRamaEXaYwvr+22BPyZsztQWi34/C6/gOHebCvfL6n5eeEXI23jwLK/bKk+l4vCGdD9TWFiJMnWmchSmULVjmBLEqk+wWHQaL3Piy208974Dqlg2a8fyVK/1uXG/nPrb654enVC/SeccAJSLVKvQiYrP50dN2/SwHUcIIcR2pFgJIYQQQgghxI7sVbEqqwqvnj7Y2u9kPt/a58yJrvWYT7a/xEWx3asPAIsRnn4AOHeKPvbGGirMSXixyusc5v2dU5cw8tc9ol+WT8cNNXZue8Z77uBRyRQ/LIQQQgjxxKN/EQohxB6x8IqSbHp2nE/bDRezBXK/9f4Mh1dkod/WXssWxPp+HEVBpjgbmc56G0JOvya8InPa+sEXwbHzVXHpnm9vm9oGalZlZJPz2qq0V5Y5ljjvnp5Nj9u8WlBD9bC8MIz1sTbNxWOoxlTnvZjVdbGKRbvRafWuOu9P0Q3ZWD8WQgjRRVZAIYQQQgghhNgRKVZCCLFHLLyiE0CR9fe4TJXKyA3cBFrQtRaCUVEqhWlCrHNUTXjEtgnaXDyFaSBmnftZoIWndlG/YOEZW1SQqkzKCcWym7LFolgsuvMLjnLEwRKDipGnNDnK0dAYjGc/5tCK3r22qGPNGNODwfvanFmdamLmF2e9tu79JulcMdhPCCFEjRQrIYQQQgghhNgRfbESQgghhBBCiB2RFVAIIa4Atv9ZHatZp81CHPp2umxLqmiFzb6/ivIsqsCjJpxwiR5b7YHe/AZqW/HQY+7P/aq+PbCd5sPb9MzqxpY3s8J1alsNBErweI2xkvoXZMED/AAMprnv/LB3f7bpmXWP7Y6tnc+xIjq1uLaxenD/oa8RQognBSlWQgghhBBCCLEjUqyEEOIKsNAJoI24Lp3IdMYi2E9JkcgbtatVPWyckiShMilGE3C/uq0gZWtqdeyqRf2T69pFT3Wq+v1MnYpOP9rPi+m+nvqTUQ28mK9SW1tLsEp1BQPV1Asp3CKb1NeWy1YZsjaX5TnfeGO34rwNgDAliHcnG2WJlS0nWn29jRUrr390IuC9aHVTxaZHt3rn8vlRc7x6cK9uI8XKVC4Ow8gdRUvhFUIIsZm9frGKMXb+MbGJMX3G4tVx6d1vRJ+H6VeN63ZhjL1duOEC5VhbD9tgNo5F9pkhLrKQsBBCCCGEeHy52f/SFkIIfFtqDgAAIABJREFUIYQQQohrgKyAQghxBbACvlwli9u0f75Ts8osfnStZ/szOhET6XRB/WZp7KpzbbqfWfsiqbtmAWSLX7M/t8Vp0IxHinIaj0Mn7NisfhuHcwI8Qkg2umRjHAqY6FznhEd0akatbLrD49m1mdPPU79NhXcDK8p+UMbWel9r4/K1Gb0emx+r+2b742vtPLdJxRdCiM1IsRJCCCGEEEKIHZFiJYQQe4ZVKO/Pm87ZMbdVTjx582wpDVuNfhoz0ahTNH6jXjlx64/Qz8ImOGK9UYVIsfKUGk/FibF0+3C/yolR99gW0uBFsHtqTtb070e6ewpY5Twj6vWz+3LYRDMGhXFks/r8atEGb9g8i/M23MOOD06eau+bVC6Ohx/7rKsQQjyJSLESQgghhBBCiB3RFyshhBBCCCGE2BFZAYUQYs+UMWLGYQJOLSqz+y2p/IS1HXeu7e+PDdn+Ip2rogVVeB0Hwig6tr8L6Ne5ZLPtbyzWvyq2l14AujY9s8l5Fj8vFIKv9UIhmkCJkkMhqG4WgByOnY9tiul4Uh62909/Byoay+bEFj/7kOc5NTZCel/tNbKd0dt5tRpYQggh+kixEkIIIYQQQogdkWIlhBBXQKcQelbvcXGMuhdosUxqwimpD8cpJpvHC2m8glSiiaNOmbLF9227pWsDfUyMVJ1oIv1rabyYQi686PQsb7PnY7nstVVN24zGq+8RvBCJyazX5sWdN7HjjhLlvXovqr0TlZ7Oc7+sqt+DdeUKaFWnnNQk68djuOEd6Xhy0Fe2crSvf/Xgfn/u6R6sYuXzo34/hVcIIcRG9vrFKmLtHxM7kGfjxLbpiH5DiVyP0i8b1+3CGH+7i3nvgW6K13C/7R/C9g+kbZROWpY73oh+Y/oAQLXc3s/7x5EQQgghhHiykBVQCCGEEEIIIXZEVkAhhLgCOJQCdkwrcjlQn4qvnTdtrTpsIRgcYWHKNgdb5IN6t9Wxcmo9dax7Tr0rb8/OLIA8nmMBbIInqI4VnPAKswB2AiXWxvPsf969PDsft2GFHiGf9O5vwQ/b7IHVWv0sz1633gfoqvaeZbEJtODxnKAK69cd76B/bap91Qm+2FLfSwghnmSkWAkhhBBCCCHEjkixEkKIPTPmeU1Tp6adCPb652zkM6bM5gD2DQRPiUrHrDpFr60Y1S+kZzC9ZzY7oQylIxk5DD37GZ3IdFcpsthxjkz3+qVnKzvR6gPPZA7dl9UpL1Cjo54l7L6sRNk15aKNW8+SsnWeAivqfvX9VtRm95iz2ubEt0dHSRNCCFEjxUoIIYQQQgghdkSKlRBC7Jkyxo5qZSmnU7dAMEWBh839OuMP6FMVnTJ9J3hRpp764ypCu7TZsH01h4sCe3HsF4GpL95zQ9zm9TOFx7uWk0Kz9CxWR5Uqu+qZqUreOT5mRczuXzht4fxBe/9GgWvn5D1H1qhdznNcDKtcQgghukixEkIIIYQQQogd0RcrIYQQQgghhNgRWQGFEGKPRNRWwDJS7Hl0rHtOxPii2BziMM/b5dwKsRdkE6xi2kcj119MlkGOdq+S7S5vrHu0/9YEWtCNRxYLb/vxfl6ywuXT9v6pYLjfRpa5Jtq8Ha8pSJ7OlcWiOWfjdeLZ03vG9jcLg6icuHUOsbC7esESHM/eWBrJCtjYCB3b43ofoA3S4Db3vja3FJ0O+Ha+sqytghx8USYrIPc3i2LoBGQovEIIITahL1Zon2/YRnaBPv9sRCoYAGSDdWaMh/2Hze7jeWlabr8RaV5DSV5MtTzf3glAQc8XbOxDqVlD8LMSm+D0MCGEEEII8WSiL1ZCCHEFsEpUOoKVqU7exk/32vriFW12HE6mvWs8Qtq44Q2crHe0RaFwY9mdNqdfdDaOmsK/sey1lbRRYypKt0BwV8XxQi86oRjpmJUbL1p9iE7hX2eTxY15T9dUFiIxcN2mew21MUUq8hvy/sf9toLDFZa9fkIIITajZ6yEEEIIIYQQYkf0xUoIIYQQQgghdkRWQCGEuGKsPpVXk4qfx8xSYEFOFje7hp8BtSvGPaO5gaGgCrb4NW1kGYwD9kE+V6X6UM5zlmyFs/CKzvSS7S+CLHZxsy3O7lEV7VhDdju2v5nFj21yFvbA9j/vmUx7ZZ1rVxbG0bczVo61z+vnBV9YW7lonzNt6l2d958rLaifvQ6uqZXjIJ0b93yrEEI86UixEkIIIYQQQogdkWIlhBBXwIyUBlOdpjm31fteHEph6tXcCSJgnCyMQYKXUuqmdVZrP9ePB/qNTP/0VKdWnVr1+nlR5WOx6HAvsIJjxe08q06mBFVO/DirWF7IRqMsDahOjPXLSDlqxnXUpLBox8tmtQJVLfuqX0Z/j+x1dNQzuy/Ft5+/8ke9cYQQQtRIsRJCCCGEEEKIHdEXKyGEEEIIIYTYkb1aAWOMWBbbq7Zb/ZaLYEzx37HFer0Hyz0mIwoJj32ofDLmnhdY+HfseEMPiXeH2t4vOg+me4ytpTKm35giwsC44r+q8SIehoi6DhXXrsq9NcNxuC2c9fM22bQMW0OrvF0/vP+rYzINxthOpgp2e7uC55Y5bQl+DdFbA9N4of3YCVl9X6/elNWuqudXXxvytj5XcGx0WV5bBZt1h9aWLF0bJ2znS6EUTsFwrvtkda7Kqm8F3EZwgidszra+5BQY4f6e3Nc62djf7H88NtfqsgLpHRtlsgKy7W9ycNib++zWU84dhRBCAFKshBBCCCGEEGJnRilWIYQvArgHoARQxBjfFUK4C+CfAngrgC8C+L4Y48uXM00hhHhMiBFVjChJGa5CP26iTCoSK+V2XJHCZP0qGs+U+oqG9XbRgqOcZ70jUslMWXLVqarfr/Oy+jOI3v2TUsUqialNHLvuhUJYv3LVV2SG1HNWn6ITd+6HWzx6W2987zrvnhwsMdCPaaLd6b1ogjdIbZ8e3+rPK52vOkqdMq+EEGITD6NY/dkY4ztjjO9Kf/4AgI/HGN8O4OPpz0IIIYQQQgjxxLGLFfDdAD6cjj8M4Ht3n44QQgghhBBC3DzGavoRwL8IIUQA/12M8XkAz8YYX0jnvwLgWe/CEMJ7AbwXAGZ3n95xukIIccMJIQXmkOkuWfy8gBwvgGfq1MDKyJ5no2wLv8mCTcnpZ+NxmFAsuj8ZbvPON9B41fYwIwCoylWvzQIvIspevzgQwFMVrf0tOjWoYlOzqqB+Tm2pVD+K7XSN7S7vWxA55MbG84ItvHs1/cp+P8/G6I1RUB0ru1+xaEN87PVYYAUABAvVGBFCJIQQYvwXq++IMX45hPANAD4WQvgcn4wxxvSlq0f6EvY8AJy89bmHrVsphBBCCCGEENeeUV+sYoxfTj+/GkL4ZwC+DcCLIYQ3xBhfCCG8AcBXL3GeQgjxWOGpTjNSHzInqMKYbSkj4e1geWUlLNyCR2t6merTCarw7ubFsg/1o3vZe7BFEPHi2JsI9mxc7LnHkMLjqUim6vB9vfAIVqeG5mdqV0Zx6160ejPPLYEWBitReYpP57lbVPtkftTOxVPv0n05Dn5575WNr0cIIZ50tj5jFUI4DiHcsmMAfwHA7wD4KID3pG7vAfBLlzVJIYQQQgghhLjOjAmveBbAr4UQ/m8A/xLA/xRj/BUAHwLw50MInwfw76Q/CyGEEEKIa0gI4S0hhF8NIXwmhPC7IYT3p/a7IYSPhRA+n34+ndpDCOG/CSF8IYTw2yGEb73aVyDE9WarFTDG+PsAvtlp/yMA3/0wN4sxYlVsfwh2WWx/oHlMHwBYjqkh4lhtLpts+Jnyh2PwQfHL6ec9TO73W47oM26sbfVamn4jfudCXBmpjlUV27+npWPTm0/6y7OtZ2zrmzpWszIFTlR528/WOc9FyCY9Ww2Da/HLnDa7wKtt5e3dtW0RfTtfLOs2q0kFADG9VyHQaw39/8/tmqaelVP3Kpu0trZsZja5fj+eUzZL15y3Fjuz01XcL/0sO7W18l7bemhF515kuzOaoAqnFpW33pn9j+fevAYA5aJf5ytzrp0enfTGFjeeAsCPxBh/M7mRPhlC+BiA/xh1CZ0PhRA+gLqEzt8C8JcAvD39928D+Kn0UwjhsEvcuhBCCCGEuCHEGF+IMf5mOr4H4LMA3oTNJXTeDeAfx5pfB3AnPVcvhHBQCXUhhNgjEUAZK5TblHJHOM6dQAsbp6KIcS+ivQ3DaNumg1trdpL0LFOlXHXK6dd5iZtVLlaiQlKiODLdzneUrZiOB5RsHtdTzy1SnSPLLVAi5BPq11eMvLCHakAt78x9bc5uKMWWQI2hNm8cDqConOCNfH6Idaomep4UOEdREzeTEMJbAXwLgE9gcwmdNwH4Q7rsS6ntBWrrlNZ57rnnLm3OQlx3pFgJIURCzx8IIZ4EQggnAH4BwA/HGF/jczHGiA0ZoJuIMT4fY3xXjPFdzzzzzAXOVIibhb5YCSFEiz1/8A4AfxrA+0II70D9vMHHY4xvB/Dx9Geg+/zBe1E/fyCEENeWEMIU9Zeqn40x/mJqftEsfmsldL4M4C10+ZtTmxDCQVZAIYRIJCvMC+n4XgiBnz/4rtTtwwD+N9QPdjfPHwD49RDCHavvt+keAUAeMrB1Lk+WuJwsdmbxO6QQC7PzcT+zB3KIhUVWTJz6T6ODcxo7H1n87Dj2a1J1+5lNzrMHtha6kEIpOMjDw2x8rhWO7mtBOEP2uKoYDtQx21tFdZ+sLSN7oNWF6tS7csIoqma85cZ+22x/Xi0st96VWfec179a3e+1cb0rszZOj241bfZ3qmQr4A51w8TVE0IIAH4GwGdjjD9Bp6yEzofQLaHzUQA/FEL4OdShFa8OrW9CPOnoi5UQQjhc1vMHT7/hGy9tzkIIsYVvB/DXAXw6hPCp1PZB1F+ofj6E8IMA/gDA96VzvwzgewB8AcADAD+w3+kKcbPQFyshhFhj/fmDQPHmMcYYQnjo5w8APA8Ab/lT/0YsY9VRnUwZmFHohAVQWHQ6gCYr/ZhULC+own1NA+d4hKZf9OLWh26wJW59bLmHsQyoUqZixS1KWHTUJA6yWKckFaudRl9Z8hQjb9wh1cnr54ZjOG0crJF5bUl5Y3XK7sGKlIVXTCjYYnnv1f4cxI0hxvhr2Lwc9EroJDX+fZc6KSEeI/SMlRBCEHr+QAghhBCPwl4Vq6qqcPrgwdZ+r55u73P31mLUPU/m8619zkYWG1448boeYwoOVxdZk9h5jkIM4z2z4DEmWnjsWOL6o+cPhBBCCPGoyAoohBAte3n+oA6mIIufkwVxmCx+bPVbJtvXIms3eWaexcyGpU0eO8rIBWQbPHx76xeslTdu3DpWduD08zaQAtWHSpa9Tr2puEpt7XjN5kUKp+BrO0M3dbGy3u1tDN4IMXuetzni9WPy6UF9j7IfXlFyfai0OVM6dsPGfkcbONGpHdX0o3kM2Q5tbgCQzeqx+RWUi7POOb4fz8WsgtmAPVIIIUSLvlgJIURCzx8IIYQQ4lHRFyshhNgjERFlVWFJapJFpndW5GLtHB2XpNZYuEVJ4w0FVVSk40zDQE9TljqhE04ohRfLDkfZgtPPsTE3ahP1Y0VrvV9FKla/D8eeL9MtnbFIkbEgi5D3Px7H2n63KWDrKtNQYAXD4RVjDeCmROWsiqVjVsWmRyf1PB11imPZvfdFCCFEjR7OEUIIIYQQQogd0RcrIYQQQgghhNgRafpCCLFHAkIdSEH1qSygInOseWwFnCabFtfAsmu5nwVUeOMxmWMatFnlnnWvOevY/kCWQbMPOgETTEh1piqn3hRb4czGt60uldkCvTpW1lYVy/6FfN+mtlW/ZlVGNjirC9WdZz9kwtq4n92jCaBwgiq24d3LG8OsfUUKrGC4LpfNgWtbtWO0r1spqEIIsRkpVkIIIYQQQgixI1KshBBij1h4Bceoz5IKMCM1IHfUJotPP56MW7rDhmOjSONNqa2dlRO3Hof24rbErUdP7erjxag3bR11yNSpzQpPR7lxgjJadaofhe7221LL0NQjDpkYwubnqUAVKZpVysXP2xR1N9rdxuF5DsWyT49bdapR0TpBHrWilVF8e7nYXmdSCCGeVKRYCSGEEEIIIcSO7FWxqqqIxWKxtd+rD7bviL16Om7XbE4FEDdx++Bgax8AOCuGdyuN5ZbnCgCgGNGn7udV2FzD2Ym9bMY+B3CRjPX2c6zwJuLIHWUhhBBCCCHGICugEELsk5hqTpHVy4xoHDZx6NQLWqYNgUXWbvLMHOtamTx4XNvK26KJqTVSvyK1TW0qgeYRin5bXDpt1o9thHaQUZNj+3PscZ6Nrz1H/UZsvnTGdd47O8/9srRBx2EPFmQRuT6UBUo44/FmVJl+49aW8RjWb8UhG/33ycb1witysu7Z3DN6PRZk0altlZW9frOTO72xvfsJIYSokRVQCCGEEEIIIXZEipUQQuyRCGBVlVhRW171I9Obcxytns6vWP1Iyhe3HaY4iop0Kgu+YHvxLI3HbaZUxbTvFlgtaVQpL6iictow3M9hKGwBrEiNCK9g1ckbbz32nPtxxHijRDkq4lg8NW3otXJ4xfrc+NptQRmN7ZnuP5kfds+hjVn3VLzivI1ql2IlhBCbkWIlhBBCCCGEEDsixUoIIfZMvvbMkFfI1xSmWdaeM8WK+9tzVBmNaWe9AsAedItG5arStR19wlWb7L7Vln7tHdrD+lksi06vL7Vo8/YZoyZavWSdr9u/c03VL8prY5SrRy8QPNSf7+cpYKwsrYfnjC0Q7I07JqwH8F8337ey8ZKatW0uQggh+kixEkIIIYQQQogd0RcrIYQQQgghhNgRWQGFEGLPlLFyQymmZPWy86VTy+42hSh4gRcjqt8BINtfJ2RivZcTVPEojKzd54VRWJtr+xsYI9I9vfAIs79VW2xyjT2wHFfLcKyFbii0oqr6v0WvP1v87DVyLPwkP6qvdUIupscnvTaOW7ex2W7IQRZCCCG6SLESQgghhBBCiB3Zq2IVY4XF+fYHgs8W2/u8evpg1D0PZ9sf7D09OR411mrkLmThxOSu87A7ysOMe0B9LGH07LYTR+5SXyReXPA62Yi/F2PJRhQlFcKIiCiriBL99WRBisg8qVKsbNn506INcbg1rc9X9P+aFRLO83ZtsMK/eacYcE1O/89PLAyjCbFomVrcOhcDbnpwIeF0HD2Fh0I2nP93/ALB/aK9ztvX9muKBvfDLjgu3P7f5aFs/eCivVmak1sgmCPQ07WsgLXjtG2x6sas872CKUv0O/bUq6Y/FzJO95/Mj5o2U5vCvK9EZVRIOKa/WzldOzu6vfG+Qggh+kixEkIIIYQQQogd0RcrIYQQQgghhNgRhVcIIcQeiRFYFl2LXJnsw149Kw6nsHCLJQURrPL6eFm1Vi9zfZVk8bO6WNxm94vcLx2bdXBCVuOY9uICh1hEp46VF3Lh2ANjGpvHy/LausYhFp5l0NoG6z7RuJ4t2cIr2D7shTx4bd7c3DpWZb9tzPhD9r9t43XHqceekN1wcnCY7tv+LqZHt/rzSu9ZuWwDK8beVwghnkSkWAkhhBBCCCHEjkixEkKIKybP+ntcpmLlW8JYvDh2D6+XqVicf2NCiYVXwFHROnhBFYOhNXSuqq9hNcmUqqqk8IYUrc7qlB3ztU0/J269KupzHE/uK0V1G8eKW7+MYu4tep2DKjzFyvDu66lulRN+lGVOpH66hqPQh9QkL1K+ctS+yfywvcfBYe/82Ch5IYR4EpFiJYQQQgghhBA7oi9WQgghhBBCCLEjsgIKIcQeiTGirCrX/sdBFTPHAmjWvflk0rumJAvZMtm1vDEKDq9IHsChenncvw2y4Lmn+3JghRdecQFwoMVQjbzGJrjFthab+lRs00v2xLJvO+Q6Vo8aqOHd34MtgXaYT/o1s3juZgvkuTe1tZx7eYEVgeyOnrWRLZJCCCG67PWLVVVFLBaLrf3u37+/tc+rR33vt8fhwfZCsK8+GPdBcXrc96h7nJVeUcwuy5GFc0c9PxGmo8a6SPgfOIP9LtCPP6bwL9AterlxrJHJVvyPjI33U0qWEEIIIcQTjxQrIYTYM2VVdRQmI3dCClh1OivqQIcpfZnPZ7U6xJswq7ShwbHs06Rs5YHUh3RNTtfmSaFqpkLniqRYTQN9dFhoBbfZ8ZZ+IXOizSsLipj22kLgTYxVamvVMdswaTZOKACjd46OOXbcNlM4FMILubAgC2/ziNtyC4DgMIyqq2xldC8kBYpESRRFPxZ+/TXwMQdQ2DW84VQl5c3bFJpQYMXk4HjwfkIIIbroGSshhBBCCCGE2BF9sRJCCCGEEEKIHZEVUAgh9kgVI5ZF9znMpmYVBVrkoW/xs6CKFVnN7HjmhGEsqd9hnPTGyyz4gtpiqI+L1DShQA0Lsph2giqsjhU/g5rOu/3a1x6Ttc+z/fFznJ6Nz65h213XKtjtb3WsPOteJ7DB6lM5NbOYynmWdr0+FV87ZKHz62nR78n53dp42dhnT/m9OzjqnZ8enfTnVfVrigkhhNiMFCshhBBCCCGE2BEpVkIIsWfWgyu86PWzVa0SHM+Gk029EIxmXFKbLFK9InWqCnZMqtRaEikrVi6mSnUuc+bU9KNz6bijOiVlpaIY8arcnMjKsevWz4tbb9SX5XmvrTPzdF9WkVx1ysZb9efWUaxKL9K9q2LxPCrn95k5oSbevYbCLTpR8Umhq6jNFLp83qpZ+XTem5/CK4QQYjNSrIQQQgghhBBiR/TFSgghhBBCCCF2RFZAIYTYIzFGVFWFKRUqMisgWwKtflXlFAnv1LHK+iEXQ4XFK/LsNWOT08xcZyE1UoYC8kFXYLbhONFY9jjQot/PC3uwUIqIVb+fU8dqvc8mzJLHljiDLX6NtZDsgUMFy737cshEeZ7um2x/HE5hoRVsCbTDiVP7nC2Gk/ww9ad5DrwH06Nbg/M0myW/rwXV4xJCCNFlr1+sqqrE/fv3t/abbXmmAABeee3eqHvOJttf4t2TfhqSx2t3+h++HqstH+bA8D98mArj+o0ibn4Wo3PPgecZmqFGpkSNGcvSurbhFbP04AKXm9j2Dy5jzPMEXIxTCCGEEEI8mUixEkKIK2Bx3t9QOFtSW9pgmpGCYJs2p7QZ0cSs0x7AsuxHsEfU8eQZyVO2bcOBFdN0aOoUn5uk40ix5s1ogT5OGhXJUa5YpTLBjDcw0qYNR6c3cesD6hRf456zMfJ+2IOnRGXT/gbfatVu6Nl57168cWP9WAFrxk5tPIbtBVYVBYoUwwpYM/c0Dm8u5dODzusCWpXLm3s+o2tnx2kyCq8QQogx6BkrIYQQQgghhNgRfbESQgghhBBCiB2RFVAIIfZIjBHLVeHWn+LwiiaUgvpZ25Ksa8t0fkp2LQu3WDr3YGvfzKlRZc91lqkW1ZTsd3Ytj5o3+3PcmtrY9mdWwVj0+rHtL8tryyLXpwpOyEUTaBE3Py+ZTVo7X7nshy409aQ4sKHsB1UM1bHa2laOa1unotQQC7fwbHieZbE7Tn2vCfUzqyDPd3rUf9Y4rtUFE0IIMYwUKyGEEEIIIYTYESlWQgixZ9bVqjzv73FZkMXJfN72S1noOSlNZVJ2clJ17DyHV9g9c0/1GJgrh11M0rid/nbfTuroUALpuH6sksSBRFPuV60pLN1z9THHk1eOEmPnS4pg5+CJ5r7OeEYnsrzq91tXgDrzdFTGLBvMuR+F9xpKJ2aeI9jzSf13L4LCKxz1UAghRI1WSCGEEEIIIYTYEX2xEkIIIYQQQogd2asVsH5oe3th2cVicSF9AOB0sb2ob6d2zABji/ouRzyYPBa24Wwk9h+s3qVfsTy9kD4AUBXbf0+elcYd6wIfoN72wHfTb0TNlizViRFiDBG1Lc8rXs7hFXaebYPL9L/w7Vn7d84sgCXZ5by1iscewtacfGDfrbMqDRUe31qU/NH39rzQivVwh23Fx2MKpdi2BplVr6K1fWjmPF5TP4vmtm73y+h3Y6EV3McOJ7wGejW40j06NbNsjM796n6zW3d6c+fAD3uPee7F+bi1XwghnkSkWAkhhBBCCCHEjii8QgghrgBWypsY9aLotbGyZedPSfmfOqrqygITYnutKV/TvN9/OGqC1S9PQbernX06DjqIA/0IC6qw2HWgDaXwYDWFY9vHEPL6/dkWf273iKxEmeq95do89WMVK5+kSPmqrwi1v25SiYp+v0YJyzmqvr44nx/17s8qvSlaWUdFK9MY7fuepfCKXd5jIYR4kpBiJYQQQgghhBA7oi9WQgghhBBCCLEjsgIKIcQeiTGirKpOKIUXoGNWwPWaV0A3IKdKQRUrajtMljAOsRgbXtHMs7EAtvY/swX65sBOPIIzohOjkKyCXatZ3ca1q9ZDKep+yZ7nhFjYtZ3rHMuehVdsY73uFLdxzSoLt+D7etcOYdeWRWv3tDpWmWPj9N6bzjztdbcOv8YWyIFAXL9qnarcHjolhBBCipUQQgghhBBC7IwUKyGEuGJMTcrzdq/LgioOZ23ogAVZ5FmrE1nMOitSWVJ98jBcrsHOTqifDW3XziiAwtqCF3fB5RzsuBO3XvX7VbVSx+EUpjax0lOuFp1z9bFFoLdqSpnKO9i1HBhRLh6kn2ft7Z2YdWvjc5Wjdg1FtHsqEr+edRWL/7wexQ60cew8jzzdwwIrNt23GdeJgOc2U7ZmR7d7/WJJcxpRgkIIIZ5UpFgJIYQQQgghxI7oi5UQQgghhBBC7Mh+rYBVBM7OtnZ7MJ1u7XN0dLS1DwCcLRbb+zgPjnsst9QreRiGDTrcb0TPOG7+qB6M6lYsXruQPgCwOtvezyw629hWa+ZhyEbaWYLzsPg6OdWHEWIMZVV16lOZjS8jOx+fN8weeHs+b68Nm0Mu2ArorSSZYxXM035blq4IbBO0UTgwInr+XnQoAAAgAElEQVQBENXaT5AtsD9PzybnhlKwZS5ZADu2Ors2/axW587cnNl2bH/F5jnRGmRrg9fWtRhuXh/M9md1rVLjxn5eDbKSXmM+PajbeE1NNa28XdTZraea4zgQvMG1rcrlqftahBBCSLESQgghhBBCiJ1ReIUQQuwRi1v3lPLZtF2STYHiUAprO6VrT5Jiyv1WFt4Q2/EsIj3jgHSLY3fkLAuxKCkwYhIcBbc57+zTcXiFq2w5lzhKlduv6issvT4jVe6KYtctDKLaEkCRjVSqLSK9E0Zv1yZli5Vx79Vw9Po6naj6dMzR6XYvvof3vtj5kLevK58e1m0UYBK8vwNCCCEASLESQgghhBBCiJ2RYiWEEHskolaeytKJLCdODuvnqPjZKXvuittMUeJnQI/TMztcIHjsM1ZWGLiK9TmOYrcCwSAFozmutjzraf3i8Otu5uEV5XXULI5qH7rW4tGrTox7/1p7ZqnzjJVXXNibnz2n5BQN9vp5eM9pZU5xZ+vHcevNPXluaZzp9KRty/rPh81u3anHDf17qUCwEEKMQ4qVEEIIIYQQQuyIvlgJIYQQQgghxI7ICiiEEHskoBs0AbQWv05QRbIKsg1s/TqgtftxtHqW7FzcFrGZbix7SGPUf56SNayxBXKJBy9G3WyBHFhhx2QZjGVtu2OrmRejbhHf3X71OJGshUXqV6S4cbbEmbWOo8jNCufZ/tgm2MS3cxS6YyP0sECJyhkvOJY8L5RjyHaI7dVJAADFeVvqpLkvvz/pOJ+2Uf5ZCrJgC6bCK4QQYjNSrIQQQgghhBBiR6RYCSHEHokxYrkqOtHqBitSed7f97ICwSedAsG1isRBFVVScViJykeWJTdVymLZK3AAhilWHEAxFEbhFQjmpqQElX01Z1tQRXQCFdYL+VaOqlRtKfzrtZlSVVWs+21+3fybq5xI9bhWyJij2z11aohOVHxzTyqMnIoG85zstXGB4GYMilu33wGrVF5YiBBCiJo9f7GKQLm9lklRXEyfsf1Kp9L9Lnh2nXW8NC6PyZh+29K4jJEfiGan2bUP0FpyhvCSuTzG/oNjqK5N0ycfZ2cJTuLWo9xPCCGEEEI83sgKKIQQQgghhBA7IiugEELsEatjdXbeV2pzJ6iCwytWSYE/W7bX3j446I2zTCr8ktR4s/RlFEZRxdj5yf3QWAG7cwfWah01lzq1rTqMrF/lWACtrRMy4fRr7lT1AygG70njmgLNqnaWzhdFaz+czua9a9v7t691enC48R5eUAXbAtfnx/081d3OTw6Oev1Gh2FQv3wy799XCr0QQmxEipUQQgghhBBC7IgUKyGE2CMxxiaEYgz8DKipWHy9nefwinzkM5z2rGcnkiH9oQopxp1CL4p0j2lnT66JTGibwmTt3IZ+HgPPUnK0ehty4bRZjLrzTC+HUpQri3une5abwyuYMqlXrCh6/cY8GzqoIGG8StTGwtN46Xh6dNKOl1QsVvSmR7c6/evDFH1fLNprFbcuhBAbkWIlhBBCCCGEEDuiL1ZCCCGEEEIIsSOyAgohxB4JIfRKMtifud0rA2G1rTr9kj2vU7MqHXOb2f0Kss7lydY1ZBzkmTblH5yaVIhku2tKQPTrXcWyrbFkVrOKalKZta9cnTVtZbKiRSoZ0djUyLq2Or0HACjOz3rnzPbGJSC8Old2TUlBFUVhFrvYawP6Nr7JhIIiyv54htkIvTpa2+x/dk0+7YeXsAXSSkasHtyn+5oVsP1dlIv6PZuksI26Y/r7QWEk0Xm9QgghaqRYCSGEEEIIIcSO7Fexqipgeb6926q/s7fOcjmy2O2Ih8SXq/EPko9hzIPjk8E94pbpiGLDKMa9F6vz10b2u7e1z/L+K+PGol3STYyNRB5LZ8d1R3In+ngdxQ+Lh6FK4RWzSbv8ekoUnzdsrTqet20zCyKg8AqLWT+ma23FyWjtaWLWaTnK1pam0eXTvYh1VrYclauJUe8oUct0rh9KwcpWlda9qhMy0Q2t4LWlCbTohDOkcblf1Ven7LhVqYDZbNLrl6U3j/sNYWEX+TZ1aiAAg1UnW4v49QQvGMNi2ecUy+7Nwd5PTtcfWWheCCGeRKRYCSGEEEIIIcSOjPpiFUK4E0L4SAjhcyGEz4YQ/kwI4W4I4WMhhM+nn09f9mSFEEIIIYQQ4joy1gr4kwB+Jcb4V0MIMwBHAD4I4OMxxg+FED4A4AMA/tYlzVMIIR4PYkRZVThz7MzroRbrbRZoMadrF8nuPM8n1FZb5s6orZjW105y8volFxvbCFfJgpch2crIkjdJVreptycX6OPEbIFkDwx2sy11kJrwBg5McGyEZvNjCxuHQABARe+T1W5iMrNRnns1o9q2bN0fCa5fVfXauJ5V5vxO1+tdeXMb3Uav34IscrL4WVAFX2vvXUZWZ7MPZvm0vXY6T20zulZGFyGE2MTWFTKE8BSA7wTwMwAQY1zGGF8B8G4AH07dPgzgey9rkkIIIYQQQghxnRmjWL0NwNcA/MMQwjcD+CSA9wN4Nsb4QurzFQDPeheHEN4L4L0AgMO510UIIZ4YYgSWa6E6pkRxxPpJWi+9EIvTs0VzvLxdKysrCim4PauVC1abWJUyshS00wlFT92KpDDNSXGxtikHVdgxq0ph1muLKSHDAiuYTlBFOvYCGyonqKek+HS7xo9br0MeWNUy5aajIDkx9xxQYVhABatZXpuN7SlXzbxpTqYsjW3LSJWMa+EdQPu7zXNWturfD4dcTI9OevPy4vCDF1IihBACwLgvVhMA3wrgb8YYPxFC+EnUtr+GGGMMIfQ/eepzzwN4HgDC00+5fYQQQgghhBAXwy9+8feaY0uPZcv4YTqekZ14ljaAePNsmjbgZrQ5lCfD22RECjbDrupsIB27AqWyxu3nIrelY2c/DG+7c/chZvtojNl6+hKAL8UYP5H+/BHUX7ReDCG8AQDSz69ezhSFEEIIIYQQ4nqzVbGKMX4lhPCHIYQ/EWP8PQDfDeAz6b/3APhQ+vlLlzpTIYR4gjg73xxu4QVaMKtkGTuk3ckyWexWHUtgqqNEu46221emcxXtKmZ2KQdQmO2v4vmmOTmWwW4dqdpiVq7Oeq+hLFq7Y5XOm8UPAIpkAWQ72+rBvTQVq3FFlrjUj8eonBp6ZucrivZ9XQ+b4H4ek0n7/rQ1sPr1Ga0fvyd2zMESwQmg8OpODdXU8+oFVlQDy85Pj245923nEsvtdSaFEFfHf/+FTwPoKlEGr/V2PHFsytxilnFWmEypcnJ9GqWIlaWGSBcko1untqKjNq23sTpVpM8zvpfZ3gvH/v65l77WHP/Ju8/053cBjE0F/JsAfjYlAv4+gB9A/b7/fAjhBwH8AYDvu5QZCiGEEEIIIcQ1Z9QXqxjjpwC8yzn13Q91t6oCzvo7kz3mh1u7LJ2o4kft5+34Xjb5SF/qZMCD2lCNey+K5emofuWIfqsH90eNZTvIQ6xHJG+CI4R3JeRj9xSEuFgiIqoqYkqqhgVUcFCFF71ua5V3bsmhDJOkOtGOXZ4UI17v8gGFowmxoDFm3roVkyrEcetVUpsolCKk0IpOUIUTUNGoV46yVS1bhcXUJi+MwoIqyvP+5w2rVKZEeeoTq1SmOnGIhR/BHnr9vECL9XMH8zbi3A3tsDnTZ2PTzwuvcBQwfp9MDWN1ymDVKzjR+FU57vNGCCGeRPSvSyGEEEIIIW4oP/2532qObeO+cqxwedlu8Ew9O7Eztln1WBAwK2DuJMsWaRONN/YCNlsHt2H7VI09nV5WkTbq+F7nqW3Jm2PJKshhG7/9Ry8CAP6t17mh5o+MclOFEEIIIYQQYkekWAkhxB6JEVgVBVZUy2o16du/zBaYV+3+11myNrMV8DjVuzqctnayRQptyFft7tzLyRZnNa4YLwK32dnjrch02wNQTaQs1SeMZBGLKeyA7IFWx4rrIGV5PWe2n7FVcAgvjCFb24Gt2P6Wgh86AQ9pR9Oz+HXrTiXLII03yTZ/fLKNcJJ+jzyeF4ZhNIER3oPn04Nev8616TV6ARSd+aX3rmP7S9dOZsdNWzatf7f5pK1BWebt3zMhxPVgQUE9FjYxi3mvjVWqyllrs9BXpyx6fdaJYO/GrfNIq/Sncxo/a4IqaP0fGbduYRVNUEXkczUF9V8k+/NLjhWc4+aPJpezlkmxEkIIIYQQQogdkWIlhBB7JaKqKhSkWFnIDqtYt47qoAIvWp2j2Bfp2rNVG4N9nNSrpRfF7oQj8D0O0s7fMu0KdvYU03AHWTuu7WLmvE8XnI+W1JblHCNeh1xwSIKpWJUzz86cUwBD2Qmj6IY3ZBRPXjkhOZVTQXJsm3fOC6jwMPXKU644Ur1pS7vMHB+fexHs6ZjfE1Pxcgq+yND+DoyJEw5k7yMHHw1FugshxJOOvlgJIYQQQghxQ1l6aaKdVNh60+dw3v9nP1v87PiQrM6ztGE0pX5mAQwpATb3NtO20OxJBWczi5oaW2Jqy7juomMPLNKG1ddTrcN6jHrux5dk/2NkBRRCCCGEEEKIHZFiJYQQeyTG2Kuvlzl1qRbLVTrX7s41gRZsDyz7djKLnuUHkJtAC+deR2h38c7TzqftTpb0UPAss/E5xjY97MwD2nlnF5PrIJWrRa/N7GdlsWjaivQQMlvcirQbyTWbykXdr2rqWdG4yQq4WvbrWHXml7ZKubaVtWW0Y1sUm6+dTLJeG1s/7Xc6SbXMvNpVbLkzS2MnvCLvWwEb29+0b/Wz96bTtmrrgtkcDk6ebtryZMvMp079LCHEtWFR9MMrmHlMnx107vZs3utvyg+7mg+SVXvaGdfW+Kz7E0AWnFAMi1t3Aiu4VmLmxLLb51np2NPjWh8AOC3qNZ7fk9wJ5TisLucr0H6/WMUIOElOPUb0GVsguBhRfHZJb/4QXk2AR8X7i//o/UYWOB75gTjmg3NsUd8xbHuWwpCzXwghhBBCXFekWAkhxB6JMaIoCsxmrapgipWnXK1IOfHUpjyv21590PrJj9PYp6HdgDpOKsaiaEMuTNl4QG0WQZtZxC3538+TwnNAYROVnWd1Kjgu89TWVWLqe3kR691Y9s3KDm/yDEWVN2PRVuhA6rmLF0/cGTtYKEX7njVqlxNs0Spm/QCKzAmxYLyQC4Pfk8wJ+bDf+2x+p+3nvHduVHs5blNTCCGeRPTFSgghhBBCiBsKp8K6G3CzemPH7H8AcMesvuSMsi2hA9pUmSKNXZG7K6SNwWaziW13KQE2tBs8h2k8HrdI157RhlkTikEba2ZPN9fY1Ht99Bpsgyvf4vgqL9CFxii8QgghhBBCCCF2RIqVEELskRhrC5j3nOhk0i7J0xRskNEO3/0HdQDBIdkI75/VIQ9P3zpp2k6dsV9b1kEFt2dtAIL3POsss/vWu338DKS18cPGE3uwmDYHc9vN7IRXJCtg6FvNzBIIAMXitc45xm3bYplr7p7sb7HzejYHS3jWvWJFQR7T+rVtq2Pljbdu+cwobMJsetxmr3tCtaiG6l1Nj9q/C1n6ffO1RQqy4NpV0cJNZsftXCbzNCeqgZVfflyxEOLh8LICcmc9ujtp15VnUhjOhNShl4r6c+Kwaq3lKF7p33D2DfXP8n79k8Y4zG8DAM6p7chUr9XL7fzyev1ZhXZOR2Ytj22wzjz1M2XrVt5+rtjyy6EYq1mthL1Cn3Vlo3a16+Y2RetRkWIlhBBCCCGEEDsixUoIIfZK7MV8m2Jy//79tm1eqwWsYpnScUaKlB3fpjHLdMxxs6tpihv3Ujg52MB28dLO5ox2+M6Sx/6AFJck+iCP7e5fnqUdyIxivy3algpPZvms8xMAsmn9uqtVG7eez2rFhGPEY1JbCioCmSUlL5z3Y8erFOjQCc/I6vfJi0dnVclUu8lYdayjTplilbnn1+fpBXBYzDqrVE2/fPhj3JSojKLap+ma7j36vws7X67Oem1CiOsDK1b2jNXSCT5604xU79XX64Oy/dx5Zv7W+uD0822/WKtNHFwTLI26UaxorU9tdw7e3LY9+H/qn5FcElm91p8cPNe2nX8lHbSfZyH1+4bZG+vXUr7W9k/q2KTzjFX9c3F0q2mzCHZ+rirzQpYuAClWQgghhBBCCLEj+mIlhBBCCCGEEDsiK6AQQuyRGLthCUBr92Pbn/Vh2+A82QM5Ttdsf9zP7IGzSX+JL53iTTMab5nOT83+RudmKXhia7H0aK+P9u4c24UFSXC0bhNuQZazquiHbFitJrbCjSlcPqYAOoCeXRNYq2NVZRvbuuPE9LMfAjKb1UEQFdedsp/0uswC2Kkxlc537JHO67d+3usuyUYZ04PenX4pqMKzBwohrg/eus5ty7Q2fL1oQyGemaQ6dpO2nt3X0vlnjr6paYurlwAAHQPz7Bvrn2YnJOue2fNeIivi3cM/Vh8UZONLFr9XYrum3Jm+Pk2+tSfa/L6a7OF3J204j62Sga2AaaYnk37QzpLWt/ySrID7/WIVI0BZ+xs5P9/apRgzDuAmb/X6OGkqHuWW4pDGmKSRDOPSSEb92uO4+VfluPds7D88Ro014h8615lqxPyD848+IYQQQgjxZCHFSggh9kpEFSNm0/5uGgccmHrFyokdc9y6qVdn5+0X/Hk6zzuWtjHED+/mzTmKEc+suGJ/W8e2gzie14u7bYpGeps+zrjepk9HxUoqCas+dmzhDB7exkhHcXF2ed1xqocrJOn19+LWi7Sjm1MCfvNa6XWNVYkaZcsJ2aiW571+0+P24e6he3DE+tgNOiHE1eIVCj6jNbGc1AsP/58/TXHn9ymM6GR6tz6g9dxWuGABRbzWp7WbAyUexLrtaHKb7pZU/5LWy+YSzzGQSnt0HBP9fiENMqXPkJmzvqlAsBBCCCGEEEJcU6RYCSHEHomolaf156yA9UK1aTePffLJ2vzy/dOmrXmOat6OY/ZmjmU/dRSyRZn21igp9/6azZot0NkkPRsU2l3PSV63nZGaNM3TZPK2AG0Ts5u1E81TtPr0sPX4l0Xto89oUqaYTCk+17MZr9uYuY8V3F3ef7WdZ2qrTls//2TSt2lPUrHmxWJFbaY6ode2rWiwjWd04tZzKwZ8NNyWOZHy9lwc9bOo+nhIal+yL2dULNSYztsdZSsWzOqhnrASQojN6IuVEEIIIYQQjxFsBbRn/w+4dp1tdpVtiM2dvA6GuN9x29kGHdnSbYwq1Rt0bN+3KRTjgW020b1ss+1w8vq2bflKukFbx9B4/bQOzDigvaozx81ne1kV2pN2fFn2v879L/0OQgghhBBCCPGYI8VKCCH2SaztfRXtJmZpN3GxaHfpzArIgRZmFVyR/8wCKjjddLaqj72Hl5dkjzue5p0xgHZns4ldpx3OEv3dvmWyCvKDwqu0K9gxHwb7uKFQikmyqcVXmrZJsp+Vq7OmLZsepqM2qncyP0z9Wrvj5KC2wBWL+tqOddBscgeHTVtxXvdja55ZL7ltuSxSW//99Nt4vPq9mM36H7dm52MLo9n4OICC7X4P2+bZ/hp7IEfA532raDMGBVYM9RNCXA0ddcoCiMiGPE3ryS0q44DyXv2zaNdfFHW0+onFqQPA8l+lA7rHJK27y6+kU2z7Tp8nVbs2H1kABseoJ6v4Oa1DhwdvTmO0156lrypfPn+QXkO7Bh1k/aAMU6XuUwDQ/VU/vXlMgvejIMVKCCGEEOIJIITwlhDCr4YQPhNC+N0QwvtT+4+FEL4cQvhU+u976JofDSF8IYTweyGEf/fqZi/E9UeKlRBC7JEQ6ih1jlufOIV8PaxA8MGsvdZ2JzvR6unYKxA5p3sNFfq13TyOqc1H1t8rkrI1DaSg2I4me/HTsalUQBuUwGpKPqlfN6tNVdqB5YCKqgl56KtZZVKxMlJ1cq9m36r/PIEFUGRZX4liTO3qRqv33zNTqmwuXtR5J9BirT/gR6pbMeBOIWFHFTNMsavvV59npTCkneGmaDOAOLKeo7i2FAB+JMb4myGEWwA+GUL4WDr3d2OM/zV3DiG8A8D3A/hTAN4I4H8JIXxTjPFmF6kU4pLQFyshhBBCiCeAGOMLAF5Ix/dCCJ8F8KaBS94N4OdijOcA/t8QwhcAfBuA//PSJytGw1ZAS4qdOhs2X1+19eyendUJoAcpsAJAY9l+qWjtv3cPv6k3zmvJZnf76HbvHNKG2oo27u6l/k+Z1Q9Ul4qsxnZf3rY6LWuL/IO0OXROG4Yzx+5epM2fl2njyOyBbP+rLskKuOcvVhEYKObY4MTo9vuMGAdwI417Q40sEukVzPTIRvRzNjFdglP8rMcF7yB6O5uPirerus5YP2r2kEUyL4KLfC+EEEKI60II4a0AvgXAJwB8O4AfCiH8RwB+A7Wq9TLqL12/Tpd9Cc4XsRDCewG8FwCee+65S523ENcZKVZCCJEIIbwFwD8G8CzqTbPnY4w/GUL4MQD/KYCvpa4fjDH+crrmRwH8IIASwH8WY/yfh+5hday4PpUdz2ZUu8kJr8ic3bkhug8017s5vJGUJevYlIMS1u6xpM2Fo9Q/c3b6VrTBczhU7YjtgXiQ5tFaG82Klk25MFddt8uzzGWzg+Y4PqgfjDZ7YGczxuyB9+hBbZsS2x2T3bA6a2uFmZ2vKNrXaKEVFmxR9xsOsmjmmd5T+8mbQVXaNGSLo/e67TxfG+1a+p0193DGYGuldx42Ds+PrILi5hJCOAHwCwB+OMb4WgjhpwD8bdRL1N8G8HcA/Cdjx4sxPg/geQB417vedfmZ1qKDG15B67QJA/dIlMiSenXQWbfq/+cLUpu+VvQDiswJ/VKZPldIBChiPyo9Szbyl4rzXhtzHvuqlIVbmJ39tGgt3uthS0CrYnFQk6dYzS5wE57RFyshhGjR8wdCiMeaEMIU9Zeqn40x/iIAxBhfpPM/DeCfpz9+GcBb6PI3pzYhhIO+WAkhRGIfzx8E1MpGQbtphWN/tqAKDrZYLvuRsWepbTZt+91Pse28i/nqg1ppyI/boIjTFNTAARWnWd1m/nze4TtPqhSHPsyCKWttP1OvphmpUxZawW0pbjdQoEU+rT32Wd6PDmdW4eW6/6yvuljIA4czZGmnllUsi2Uvztuilc0YtJs5Se9TRWEYdo/ZrP87YUwBy5zdURuDY+EteMOi4xmLYgfacAtfiTrutXlwjLqFhuRTej/T74DDRWIkJVHcOEIIAcDPAPhsjPEnqP0Naf0DgL8C4HfS8UcB/JMQwk+g3jx6O4B/uccpC3Gj0BcrIYRwuKznD/KnnId9hRBiP3w7gL8O4NMhhE+ltg8C+GshhHeitgJ+EcDfAIAY4++GEH4ewGdQK/rvkyJ//fDrWFFbk/LKdj4n2dQxcTZt5NyzzTPbTztzNgenW6zrZjesKKrC6lFxsETlWBsNyzRYkMXwOG1ALTNKyk3zK+k1lwOpuLugL1ZCCLHGZT5/MHvjN+r5AyHElRBj/DXArZvwywPX/DiAH7+0SQnxGKEvVkIIQVz28wcxxp6lz+x+3G5tlZNa6lkCvXTTJaWi5s4DvVbTakEPNB/H2v6Vpx1J3tV7bZkshmS/y/L632hT2nU8rerx8tCGUhyYBZDnmSVbWWiteGY7K5anvbbVWRs8kae2qmgflJ6l6N+KIoUNs/Hl04PeOcbCHjKy5+HBvXqaju1uetRGFVvdLO5nFsCcgiLWAyU4gCJL8+u29W1/9jvgoApvfmbng5Nwyt2zVCssUJBIUwNLAoUQ1xovqIgVnuO0hrx9RipSldbOxf9HA6X17PCPt23Lf1X/PH+tbTtKEeyrr9c/M2dcXutn35jG+krbZoFHB2+ke6XzGVmOp3cAAF9M+wH/5vGd5pR91tx66pmmzeLWP32//bx4uapt35elUjEPFzElhBCPMUPPH1C39ecPvj+EcBBCeBv0/IEQQgjxxCLFSgghWi79+YOqilgsFm40N7ctUgCFF8HOgRZeUMXJYb3bxyqW9eM2i5udH7TjWaBFme4xn7QKxvGkHyixSuMdUBSv+d5ZQytTzHoeuLZguiZvQxliupZvtVrUb+n0sN2pzJPCsqJd1GJRH1sYREbqVLmoVTEOtMiTF5/VIVOdKlIFOVzCsFj0ksaz+HJTmIA20r4iVdDGs/vm0/77ylHoFinfiUe30JDpFOsEJ/iD4+sttCJSGEccqIfIQSKq7SeEEJvZ/xerMTLcmOK/xbjF3bPR9PtcrDQ4G1HINh8rFsYR78WYPgCqcji96qoYW9B3bL+xhYRH3fMCxxLXHz1/IIQQ4qbRDarIOj+ZMqNk0fJ+OmjtzJi+HgDwNQqDeP2stuoFs/MBeHFVb848O7lbNxRUH9Dum7cbYa9U9cfqnRnZ/hIvle2/we/SPdav/Xqyoh/Sv8uOnX8Xno/4d/9lIiugEEIIIYQQQuyIrIBCCLFHYqywXC5xdNTuHBYpZIItfkg7cV6gBVsGTXE/cwItcsduOKN7lMn+xeEVJ2u2NC8Ug0pWNQ9Is+5vMbqRHAoWqZvzQ8lmP6va8Aoky2CkqNzJrHYosO0vSyELbFNrAi2S1W3iBEZkZGWLKcgjJ6tfSJbBKuvbAzuBEnYt2Q2be9B7aPeYeG0DNasqDqVwXByNdY+zQNJ7EWin2vp5NauyKYeQOJbC1K8zHmQFFOK6MaO1yWLOp7SGWcz6Oa0rR7YWT+hzYlKrTMtla3F+Na0dGfq1CldItfg6NQv7Fu9VUY9xhnadsbtGtOubWcaZ07KeyzLN/Yxew4GFMpGVeTVga+ZADy++/SKQYiWEEEIIIYQQOyLFSgghrgB+/tNUqY5ilZjQTqRdw9eaorRYrrAOq1OrVb0r+OoDUofQV0rmKWzhMP1c5e3u4GmRIst5p8+mx5uEactuQfMsQioGSSrage1oRlLb7JnRQO9FUkwOjtp+ZYpZ56AGjl4HumcSGJYAABDcSURBVIEMeQpvKA/bPquz+1hndqvesa2W7TMGHHjRTilFkTsKWKef8wxAo5QlxYrDKzInDt5i5Ltx68e9fs09A/VLr5sVq2YeTlQ7K4AWc89teu5UiOsHF+M19YqLAZt6NeXnrmx9dp7FOqe186m03vO1VRFTW/os4Gf97bhq1+ssfSgcskiU7nHGKpKTGXCcPotupXVySv2toPCE1DRTsVjFs2OOW5diJYQQQgghhBDXFH2xEkIIIYQQQogdkRVQCCH2SIwRy9WqY+ezMIrXXmvDGU5O6gjcyWEbMLBMEbccXmHBFyDb3+mitrEdUg2sZdG3WFgbWwarZJXwKtSfpf5Pc40l9MMrFslidsDhGemB5gmNe56uPeBAC7OPBPIWWg0sClsYqrtkFraSrYEpnIFtcmZrqwqnFIVTRypuKQXiWQHtWoZthoBfJ4std00oBQVMmI3Ps+ZNyCY4ZN0Lk/Z9t/JrObV5ZE6NLCHE1cK2t3laJ6ZshTMrYGRbc1pD2X5XvAQAuDtp16STmOzjRWsjf8Ps2fpg+ZXuWEC7hlft+vu6gzfXB+df6vW7M3+ubVu+UP+kEIs7k9oKPZnX61reCeepP08mZOubol9b0ayQOX1uZI4F8iKQYiWEEEIIIYQQOyLFSggh9kkVgbMzFBNSEvL+Uryc9ZUBU6o40GKxqHcFOb7d+rFKdT/1e+ap271xOVLdItjtwV7e1TtMyhb3P0+nD0kJsgKOBalT09QvdB48trHptQYLr+jv+8WsVZHyWT1OWLW7qMXytD5HypZh4Q2mzACtOsNx4taPVSyLbbfY9c64q1Z9mh5vVp5WD+4N9jNMiZrO6ffkBEvY65gMhFgwHHZRrRadMXhsC7sAgDA5thfR3jfonw1CXDfmpGbbOn3ofK48QKugH0364UX3q3r9fZECe8q0JkwpPv2lFMf+htk3AAAO4IRXUDFiKzh896BVp+wT449orX2dnScV7SxpQC+lAsFfXbZK2J0UaPH0hMpuOKEUptiVkePWL0db2v8KOSaFY8yLzS4uzSMbOdbUqQnjMXNSoHp9xs6/Ot/eJzo2Fq/bgHXmYfGSrjy8hKtHvufINKoxcxs7VjZirGwia4wQQgghxJOOrIBCCCGEEEIIsSPS9IUQYp/ECiiW6NjfkrVudtJaxKymVUF2vlmyB3LwhVf7yuDwipN5befg4Is8HR9OWxvJdE3N9SrVT2iMeerfKU+Sfh6QPdBCLkpSzu2VdWuXZGs/AWTpdZRUd8qxpJm1b5Wsbh1lOln8MgpnsLpX3C+YFXJ2SP1SnbGDfls+7dv6WMU2a9/s5I4zXm214xpTdm1Vtk6EPK/7sXWvfVmtLcZsgfx6mpALcoJk080BFRzu0TyQTg+Sh/J047VCiKvhkD4HrBbhMdXHszX7HgXwmFW7ouih03R+Sev0S079wlX6DHqlqNeuY3L35KFeX85pXTtv+rfrWkifCQuqp/dSGo9NXadl7dx6NY33GtmvW8t6e8HMcb01dawq+jwb6UJ7WKRYCSGEEEIIIcSOSLESQoh9EiNwfg4UpD7MayVked7uxJmyNJ+TwpJ2/RbUb5ZCKViJOkzXnC3b3UE75mj1+6ivPZm3z0Iu0o6lPdh7WrS7jt4On+02ctztJEWrlxzBm9p4hNwuYfWpefCZFD3bZeVY9qRecVCFKT9ZepC7pPfY1JzVedsWUmBDZHXIQh5oF9XUHlaWTG3y4OdZm3AJVpHK7nOxE3oNYeAZYw6vsNfIbc21dK98INwi8mu0a3J6j+39pt+FwiuEuH7MKajClCpus/V5xc/aO4/dr5KKlZEHwdZ4bltayI7zmTBJpTJYCbMri04ZD6ekRwrP4DOmot1L4UEL+kw6tc9JUsy83ARrYbdFhovLamCkWAkhhBBCCCHEjuiLlRBCCCGEEELsiDR9IYTYJzHWYRUZ+TAWqWYIWRgWybKQOVYLtgcuk8XPC7FgS4TVtMpzqtmUrIX3F6210GwkM+e+Vr/qHtVzOkn9K/JumN1kSQ8Kz9NcKqoj0gRZZDx3x58yst5IY2cbKKfA4QzWrar6oRA8RpbqZ7HFz+7F44Vkz4tkGWxrULUBISEFaFhABdv5eq+FyCl4o7k/j5vmwtc2gRZOrS4Ow2gti1vKR2T92jdCiKuFbX92PMv64UGMrdN8ZmilZbt3ufb5UHTK+fRHyZxSSzZeQZ8ddsjhGXa8Sus029ObsCWq7HOQ5sa2x4nz+mUFFEIIIYQQQohryn4Vq5ABB5tjXhsOD7f3GTMOgKOj7btrJ/NxY3F05RAHI3ZXD8K4ArUoF1u7BCeC1yN6u7LeeCOK544tsJuPfM/GMLYocXCqja8zpvAvMO518i6yEFupKuD0fhNYAQCwv48PqF9SsR5Q3DqSKvXg7Kxpsgh2jmU3levV1+41bW963V0AwNl5qzaVVV8dev1xrVycWewu9TGFyQux4N3RCvXxlNbCsgnFoF3PtLd3SIpVlt8GAAQufG6BCRWvhycAgEj9LOW8WNaR4Nlhu/6YOpTTemnKDf8/bKoUr5em7ORHr8MQjYpEgRGestWeS6+VPzM8JcxRvUzl8sIuOgpYc0wPbXsBIcakjYVv3vdMa5wQ1xmOW7e1eOKs00tHxWE1qUA/gr05F/ttFrs+YyeEc21poRh0L+vHvW1+Jd2rUbHKeh2+T46J48m0N7dz53PNU6c8Fe0ikGIlhBBCCCGEEDuiL1ZCCCGEEEIIsSMKrxBCiH0TQhtYAQBPJfsVPwB8nNrIzjGbtlaw9TYOubBwCy/4grl9XFulubbVas1GcUz3zB3b2WGy3nIdkYnVfSKTh9kCORTDLNH84PMBkk2tIitgYwGk+yc7W6ja4I02DCLZQ5IlEADKdMwWP69fMzz1s1pZ3I9teU2/VAOqUx/KsQCuXxvIutfUuKL3s7ExkmXR2iZkO+Q6Ww3NeBR84Vhw2r972m8V4qYxpzXlIK0dM1qvc8f2Zla4bYY4C5mYcADOmrWPx585679Z+9gebtlKRWj7VVW/X5XOm92xIttflvpxsIZdyXOy4+61sgIKIYQQQgghxLVEipUQQuyTWNVqFT8IbOoEB1oskxJTtqEUS1N7KKhiuapVioqUpvlAIA8HVtw9Oem1vW7eDfw5pDAYi7Z9ekZx70mdOaJ+tg94TKEUpkpxcI/tMh5EDqVIr7F8zZk8tVmk+9nLvW6rs1fqeXB4xqofBFSe3+v1q4pFamvnWZgSxAEdNh612T3yKYdheKFBSWVMSlhH4UrqFV9nYRSmUgFtoIUXSuRdG70Y+0jBKBZUwf0sWj1QyAVfI4S4FhyQwj1Pa8MhrU2eYnMrqVxUlQNnaT1h58JxWtsPnTCvkFb7Y7p/5oVipPWaVTQ7y+Pa/ObUNk3jZPNanf/6vHV7vOn4FgDg9dM2b90+Y+6Rgm+v2gvWuGikWAkhhBBCCCHEjuiLlRBCCCGEEELsiKyAQgixb2KsAyyMVJ+jsf8BQJlsgVyXzep3UH04C6jwgiomFEpxmOpdPX3rpNdv1qmBUo/T1h3pj8t1rI7s/s6DwPxA8STZM7iteUCZLG4uFmRBgRZNAEOn3lSqgbI66/wZ6NromrbVWa+trWPVtw6yxc+CIthGaKEUXogEB1as18qKPLeB2nn8esyxx/caqpnVGSekuXAdK7P7dWx/dj/Z/4S4zvC6arVUT+iz446tP7yuxVQ4kWx/J8kS/MzhraZt5dSgslUv2ELkWYRp3Cb4iMOR0vqzonXttbSeeYY9swnenrW2vzfO6s/J12V0/+os9Wtr8r2SxuUaVxyadJFIsRJCCCGEEEKIHQnRqaR8aTcL4WsA/mCt+fUAvr63SVw8mv/VcpPm/6/FGJ+56kmIqyWtg6e4OX9vh7hJ//9tQq/h0dB6Jly0xl079BouhlFr3l6/WLkTCOE3YozvutJJ7IDmf7Xc9PmLJ5PH5e/t4/A69BqEuHgel7+Tj8Pr0GvYL7ICCiGEEEIIIcSO6IuVEEIIIYQQQuzIdfhi9fxVT2BHNP+r5abPXzyZPC5/bx+H16HXIMTF87j8nXwcXodewx658meshBBCCCGEEOKmcx0UKyGEEEIIIYS40eiLlRBCCCGEEELsyJV9sQoh/MUQwu+FEL4QQvjAVc3jUQkhfDGE8OkQwqdCCL9x1fPZRgjhH4QQvhpC+B1quxtC+FgI4fPp59NXOcchNsz/x0IIX06/g0+FEL7nKucoxBhu4toXQnhLCOFXQwifCSH8bgjh/an9xqwhRgghDyH8Vgjhn6c/vy2E8In0+/inIYTZVc9xGyGEOyGEj4QQPhdC+GwI4c/cxN+FeDzRGne13PQ17qavb1fyxSqEkAP4bwH8JQDvAPDXQgjvuIq57MifjTG+84Zk6/8jAH9xre0DAD4eY3w7gI+nP19X/hH68weAv5t+B++MMf7ynuckxENxg9e+AsCPxBjfAeBPA3hfmvdNWkOM9wP4LP35v0K9jvxxAC8D+MErmdXD8ZMAfiXG+CcBfDPq13MTfxfiMUNr3LXgpq9xN3p9uyrF6tsAfCHG+PsxxiWAnwPw7iuayxNBjPF/B/DSWvO7AXw4HX8YwPfudVIPwYb5C3HTuJFrX4zxhRjjb6bje6g/6N6EG7SGAEAI4c0A/j0Afz/9OQD4cwA+krrchNfwFIDvBPAzABBjXMYYX8EN+12IxxatcVfITV/jHof17aq+WL0JwB/Sn7+U2m4SEcC/CCF8MoTw3quezCPybIzxhXT8FQDPXuVkHpEfCiH8drIKXltpWIjEjV/7QghvBfAtAD6Bm7eG/D0A/wWAKv35dQBeiTEW6c834ffxNgBfA/APk93n74cQjnHzfhfi8URr3NVy09e4G7++Kbzi0fmOGOO3opa73xdC+M6rntAuxDp3/6Zl7/8UgH8dwDsBvADg71ztdIR4vAkhnAD4BQA/HGN8jc9d9zUkhPCXAXw1xvjJq57LjkwAfCuAn4oxfguAU6zZYq7770KI64rWuCvnxq9vV/XF6ssA3kJ/fnNquzHEGL+cfn4VwD9DLX/fNF4MIbwBANLPr17xfB6KGOOLMcYyxlgB+GnczN+BeLK4sWtfCGGK+h8cPxtj/MXUfJPWkG8H8O+HEL6I2p7051B7+e+EECapz034fXwJwJdijJ9If/4I6n+I3KTfhXh80Rp3dTwOa9yNX9+u6ovV/wXg7SmpZAbg+wF89Irm8tCEEI5DCLfsGMBfAPA7w1ddSz4K4D3p+D0AfukK5/LQ2P9kib+Cm/k7EE8WN3LtSz79nwHw2RjjT9CpG7OGxBh/NMb45hjjW1G/7/9rjPE/APCrAP5q6natXwMAxBi/AuAPQwh/IjV9N4DP4Ab9LsRjjda4K+JxWOMeh/Ut1IraFdy4jsb+ewByAP8gxvjjVzKRRyCE8MdQq1RALVv+k+s+/xDC/wDguwC8HsCLAP5LAP8jgJ8H8ByAPwDwfTHGaxkQsWH+34XaBhgBfBHA3yAPrhDXkpu49oUQvgPA/wHg02i9+x9E/QzCjVhDmBDCdwH4z2OMfzmt5z8H4C6A3wLwH8YYz69yftsIIbwT9cPpMwC/D+AHUG+U3rjfhXj80Bp39dzkNe6mr29X9sVKCCGEEEIIIR4XFF4hhBBCCCGEEDuiL1ZCCCGEEEIIsSP6YiWEEEIIIYQQO6IvVkIIIYQQQgixI/piJYQQQgghhBA7oi9WQgghhBBCCLEj+mIlhBBCCCGEEDvy/wObB6S4wvS8YgAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 1152x864 with 3 Axes>"
       ]
@@ -385,11 +361,11 @@
    ],
    "source": [
     "fig, axarr = plt.subplots(nrows=1, ncols=3, squeeze=False, figsize=(16, 12))\n",
-    "axarr[0, 0].imshow(X_tile[0, :, :, 0], cmap=\"BrBG\")\n",
+    "axarr[0, 0].imshow(X_tile[0, 0, :, :], cmap=\"BrBG\")\n",
     "axarr[0, 0].set_title(\"BEDMAP2\")\n",
-    "axarr[0, 1].imshow(Y_hat[0, :, :, 0], cmap=\"BrBG\")\n",
+    "axarr[0, 1].imshow(Y_hat[0, 0, :, :], cmap=\"BrBG\")\n",
     "axarr[0, 1].set_title(\"Super Resolution Generative Adversarial Network prediction\")\n",
-    "axarr[0, 2].imshow(groundtruth[0, :, :, 0], cmap=\"BrBG\")\n",
+    "axarr[0, 2].imshow(groundtruth[0, 0, :, :], cmap=\"BrBG\")\n",
     "axarr[0, 2].set_title(\"Groundtruth grids\")\n",
     "plt.show()"
    ]
@@ -401,7 +377,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAABLkAAAJGCAYAAAC3L1c7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzsnXl4FFX297+ns5EECJCwJyRA2BEkiARFFoFxQdwQAbegP8dldEbHXWcUcBwXhlEHcMRXZ0DHBQVH3HEUAREVQRZRQAhJWMImIWzZ033eP+6tprq7ekt3uhM4n+epJ+m7V9WtW6fOPfdcYmYIgiAIgiAIgiAIgiAIQmPGFu0GCIIgCIIgCIIgCIIgCEKoiJJLEARBEARBEARBEARBaPSIkksQBEEQBEEQBEEQBEFo9IiSSxAEQRAEQRAEQRAEQWj0iJJLEARBEARBEARBEARBaPSIkksQBEEQBEEQBEEQBEFo9IiSSxDCABEVERG7HQ4iOkpEq4noHiJKCDCf1THCLZ97vF3XVUREnxDRY0TUxUd7s9zyX+gjrY2IdpnSTvNzLcaY0v7DT9oRXq7bMSJaq8+jmVueJCIaR0T/JKJNRHSCiKr0ub9GRAN81SkIQnQgop5ENJeIfiGiciKq0GPLN0T0dyIaE+02RgMf4+BxItpIRDOIqG202xksRPSEPpc/R7DO13Wd10WqzlAgoguJaD4RbdPv8Goi+pWIviaiZ4jorGi3sTHSmPoBEcUQUbFu734iig2hrFhdTm0423iqQkTZ+nrlh6m8m3V5r9ShDaxl39Ze0rWTe+tKuO9fY0S/K5iIhrqFR2wMbKjjbZ0HUkEQLPkMwH79fyyADADnADgbwAQiGsnMlX7yWeEt7l0AJ/T/TQG0AzAcwEUAphHRPAB3M/NxP+2eAmCJl7hRUOcRKDeZ/r+WiO5n5mo/ecoALNL/2wBkARgMYCCA64noPGY2rsE1AF7W/+8E8AWAWgD9AVwP4Boiup2ZjTSCIEQZIpoI4DUA8QCKASwHUAqgNYAcAEOgxq7Po9TEhoD7OJgBNQ72AzCFiIYy87ZoNS7aENFoqP6xlJlHR7s9oUBE7QG8DeA8HZQP9UycAJAK9UycC+ABIprPzDdGo50NkVOpH2guBNBB/98WwFgA70evOUIUaQbgTwDuru+KTsHnSKgHGnM/ESWXIISXp5l5uTmAiLoDWAUgF8CtAKysmzzyBch9zFzkVl88gEkAZkIpnHoQ0WgvyrUyALsBXEZELZj5iEUaQ7heA2CQr8YQUUsAlwNgKMVcewCXAVjo5zwOMfMUt7J6QQ2s2fpcjBmCGgD/BjCHmdeb0hOAPwL4O4AXiGjF6fxBKAgNBSJqB/XMxkM9o7OZ2W6KtwEYqo/TGatxMAtKkd8VwPMALo54qxoX9wN4AsDeaDfEG0SUBuAbqMmclQB+z8wb3dIQ1PPwIIBekW7jKUCD7wcmjInBYgAd9W9RckWGnVDPl7+J2EhQA4AA3E5EzzHzzmg3SGjURHIMbJDjrSxXFIR6RitaXtI/R0Sgvmpmfg3KeqwEajb4IR9Z5gNoAqUYc4GIUqCUVpsBfB9A9dfoslYAmKXDbvKe3DvMvAXAY/rn5YYJPzO/ysz/Z1Zw6XBm5mcBLAUQB2BiXeoVBCHsXAIgCcC3zPy8WcEFAMzsYOavmPnJ6DSv4aInMabqn6PJYtm7cBJm3sfMW5n5WLTb4oO5UAquFQBGuSu4AOf7bCUzXwLgDxFuX6OnkfQDQ+E5DoADSgZzALi4MS5Pbowwc43uJwXRbguAcgCvQE0GPR7ltgiNnEiOgQ11vBUllyBEBmOpXVykKnT7OLrLh5+H/wCwQy1ZdGcSgEQoRVggGAqteVBLk+wAfkNEHQPM785a/TcZQFqAeQzlV3od6xQEIby00X8PBpOJTvoOLPKRhomIfYUT0S1EtF77ASshov8SUV8fZSYT0QNEtEb7SKkgop+JaBoRNbVIP03XN42IMoloHhHtIaJaIno+mHP2gqEEiQPQykub04joSVJ+Csv0sZaI7iIij/cOESUS0SP6uhh+DfeR8o/2FytlGhGdoX1v7DH5jvqYiH4TzMmQH19dVn5tiOhrnFzKOopc/Zd9YUrn1TcIKW4gohVEdISIKokon4hmW72jyM2/ERFNJqLv9PU6RkRfENE5QZ57TwBX6p+3M3ONvzzMbDnBRERNieghfZ+NfvoTKV+WyRbpndedlH+fl0n5gqoiogLdf7wqUYloCBG9bcrzKxG9b3UNzNdOX/ffEtH3up1sPEdE1Ff3t2+JaK/uVwe99atQ+gERLdJhd/g4x7t1mgWhnH8QXA/1XC9l5q+hJuliAdzgKxMR9SeixUR0WD/rPxCR5YSivsasn+8YL2ni9HVn3UfNcaH0s85E9CqdHA9nmtJdQ0TL9DnU6Ov5IxHNIaLOFucQcD/ReZzjCKnxcQ4RFeq8i3Qarz6diCiXiGbqa3tQ59tLRAuJ6GyrOsPA41DKruvIxzvKCiKKJ6LfkfLRVEpqfNuuzyHNLa3f54iUf18mN1+ZRJRKymckE9FfLdqxTsf1cwtvSkSP6ntcRmocXa/7VqJFOaON9pB6Jz9Jyp9nJRGtdU9vkb8FES3XZbxrVYdFHnOfaUNEL+m+W0XqXTHdS1v99jVT2k5ENIuUH8YK/Ux9TURen3kiak3KD/EeOvne+quvcyI/frJIjWdvkvKLaoxna0jJMa10mkb93hUllyBEBuOFuCXC9b4BtXSwBbwsNWTmvVA+wQaTWiJoZgqUouo//irSL7QcKJ8i75rKtcFagRYIzU3/VwWYp5v+u6+OdQqCEF526b+jghXcQ4WIngPwIoCjUEuADgG4AsBqcnPUqtOnQ1mtPgMgE8C3AP4HoCXUpMEqUsuyregGpWS/QOf7EIDVEvBgSdF/Hbr97m3uD+BHAA/rtF9CWQl1gVri+BGZFF2klocuAfBXnLQoehfKYjcTwJ+hfMOY67gSatLhWihfaosAbIXy//gZEU1F/fIJ1H0A1Nj+qun4zF9mfc4LdPpcAKsBLAYQA+BOABuJKMdH/ieh3oOVAD6GWpYxCsAyCu6D9xKoJUk/aGvlOkFEnaBcCDwF5bvtG6jrkwpgOoCvSVliW5EJYB3UvVsFdf/bQfUfD+WOru9BnXYC1Lm/D+VHbByAlUTky2fYP6GewQoAH+m6DcX0fVA+iFKglLmLocaLi6H6lbsVWyj9YL7+O8VHmjy3tABCPn9fGPmM+ua5hXtAROcD+A7KFcQBAB9AyV0vA5jhnp6ZfwLwA9Q99rbJ0MVQ/hFXM/NWU12h9LMeUPd6NNR4+BHUOAwiegJKPj0X6r4vhBpf4gDcAeWP1Uyw/cRMG33+kwBsgLp3B3ykN3gayjdWDNR48QGAwwCugnoPXOkjb51g5n1QLk1sAAK2bCaiFgCWAXgBQB+o6/4J1PW8F8BafS8NAnmOluq/7n6YzocawzziiCgVwJlQE1qbTOFtoPrs41BLcpdAKU86Q/Wtr328V5MAfAXg9wC2Q92/Ii9pjfoyofrqcKhVJROYucJXHjdSoe75eN3uJVD+8h4D8AURNfGSz2dfI6JRUNfl9zpoCZS8cSaAV4no3xbn0kGnuR3qfn4I9S15N9Q1DNp4gogehRrPJkPJKO/pOgw5p7dO2rjfu8wshxxyhHhADbgMYIQpLBbqA+IxqI+TUgCd/eULsD7WR1YAabfrtDebwrJ02An9e4L+/YwpTU8d9pH+PUf/nualnn/o+H+Zwq7SYdu95Bmh44u8xP9dx+8M8LqcAeXXwAGgf7T7hRxyyMGAUpgU62e5RgsrD0AJyCk+8mX5Gh90GlaijHU4lN/BYaZwghKqGeojqYlb3Dc6bjaARFNcIpSwxQDmu9U1zVTfPADxQV4ff+Pg0zr+E4u4ZNN75H4AMaa4VKgPFQbwZ1P4+TrsewBJbuURlDN083XpCOC4zvMHt/SjoCwPGMBot7gn3Ov2FW6Kv1nHv+IWPlqHf+HjWr6u01znFv4HHb4XQE9TeCyUEoYB7AAQ5xZn3NdDAAaY4mxQfuYYwKdB3Ou3dJ65ITxPpO8dQykxzf00CcCbXq7fE6bzmet2rn2gnhUGMNgt3yU6fDeAQW5x5+m+UQmgq5drdxjAQC/nMhJApkX4EADHoCa32oejH+g27dPhvS3y9NNxxXB9joI+/wDv4yBd7hHjHkK5eyjV4UO8PO97dfxfAJDbtazQcbVu+e7Q4e94act/dfxtYe5nr5j7mSlvJZTCK9uiLd3d+0Qd+8nNpnZ8AqCpRf5sHZ9vEXcRgLYW4ZdDvccOwjROutX5ins+H/3AaMMR/TsFytUIAzjXlK6d1b3VcYt03AIALdz6/ExYPC/w8xxBKV4YwFq38Lk6/EeoTZ/M9Rky/1te+tcymN75UJbJ3+q4/3hpH0MpQFsHcv+gJtv3QX0H3BPkM2nuMyvc2toOwE867sk69LV0qGe9Bp7vp076elq9u97X4UsAJJvCM3Dy+44BDHXL5+1daHzvHQNwsUU7BwPoGGg/8VNXVN+7Ad94OeSQw/uBkx8Z3o5PAXSvQz7ni88tnxGXFUDbjBfIg6awLLgquRKgXqpO4Q7KkoEBjNe/vSq5oHwIHHIfaN3Ch1vkGwG3jzuc3F1xGtQLlAH8MYDzbAo1Q8IwKdrkkEOO6B9Qzn3XWIxvdqgZxYkWebLcxweLNAzfSq6ZFnExWrBiANeawi/SYd8CsFnkS4aala0B0NIUPs0kkDWrw7XxNg52gvKnWA31gW31Qfh7nfcNL2Wn6/buN4VN9nZtvJQxXadf7iXe+Ij61C28ISm5durwGy3yJADYo+MnmsLNwvZtFvk66LhymJQifq7l5zrPX7zEXwhl1eN+dDKlGafL+BomJYcpvimAX3W/aW5x3Yvg9nGu4/+fjn/ELfwHHT7GS5sf0vHmSTLztXsg2GdCl2HIILeGsR/8TYfPsMjzrI57OtTzD/D8XtT55rqFGx9/L1vkuVHH/QLrMcqYbHRXcrWCUixVwjR26bhUKCVRBVwVFqH2s4Ow/thvDwvlSV0PH/3EGEcqAWR4yetVyeWnzrd1vgu81FlnJZcOu1+HrTSFWSq5oCZ3GeqdZvVcxwD4WafpFehzBKXk/BXqHd3KFL4daoLIUJxeYdGnzZPqXaAUTrWwfoedYYrvYNE+hpvi3dv9g3qHG0rnCXXoS8b9c8BaET7GuFcwTWYF2NeMSfu/eonP1fGrLa5dDSy+96Cs0o1rFKiSy1DU3WzVDos6Gu17V5YrCkJ4+Qyu5pyfQCmOLgQwm7w7E3XPZz7eDLFNxnPu8JaAmaugZpg7QPnQioHazbAEyjTWH5dBCUrbWfmVMMqthjJJB3w7oM801nlDvVALoUxmCUrw9OnXRi/FWQigL5SJsDjqFYQGBDNvYeZBUMtTnoSyMCqFGp/OAbCAiObXQ9WvW7TFDjXeAa6bgRg7F77LzB7jJTOXQc0ox8J6+fcXzHw8hLa6j4M7oazOfgLQj5k9/MaY2my5gy0z7wFQAKAtEXXRwT9AvQ9uIaLb9FISXwzXf1/1Em8ssRimlyc0KEjtUNkJ6ppa9YcqnHzPjvBSzEcW+fZCfVAlQi3zCAd9oZbMuR9mX2zGPV/EWup3a9cJqOVKcQDOsqjjC7bebdlYptbBCCC1M2oO1LO61CIPoCweAGVVY8V/vYQbdTTXfleeJuUnbL4eC87TSbr7yh8k8/Xf68jkn4qUz9Jr3dKE6/w90MudjM1+5rlFG78nElGSW5zxLL5lNUbBi2sJZj4MJcslQCm5zVwDNSH5PrvusB1qP/ufTuOebx/Ux+1AIppBRD2s2uxOCP1kLTPvDqQOizpbE9GNpPxavWKq03DtEc6+aWY21DUaSkSX+Elr3KcPrZ5r/b4z5PKA+6i+519CvaNHAs7lq9lQO/4aPpnMSxZH6b9fmMKGQcnyq6zeYcy8Ceq9GoOT99JMMTOv9tdeIroZaklpNZRVsb9d3X2xjpk3W7T1c6iJrhQAAyzy+eprPt/VUFaTFVDPhbEE0XztiizyLIZaqhwQ2h1DHyiltl83NKHQEN673hxRC4JQN55m5uXmAD1YPQG1POczIhrIbruLWeULI4bDycN+0s2Dmpm5EWpQ7QBgtlZU+cNQYM33Uu4fAFxFRHd6+QgsgzK3BrSFGYBtUEslC31VrIXTBVCKxC1QM2tlAbRZEIQIw8zfQC0JNPw15EIptH8DII+IPg5ROHXH2/hRpP+aN6gwlEB/I6K/+Sm3tUVYqFu+m8fBBKgl42dCCdOvENFVFh+bRpvfIyL4oTWAAmbeRkT3QS2DfBHAi0RUAGVRtxjqY9f8jjKcw3q7lkZ4EpT/R3/vmkhjtH8Pe3f0XuCW1owDarLKimNQy3G9+Whxx/CpZtV/wMwzoSzjAABEtMeiTcY9f077nPOFVT27LMIAdS6A67kYDsBbArD76WNWdbGP+gxfb6/A98dKcx9xQcHMPxPRGigl9W+grOwBJT+0gZtPKoR+/t4YD/WsbHH/gGfmNUT0M9TH6AS4KpeN8crfuGbFPKjlZHlQ1mIGefrvfLf0ofYzX+PhdVBj3f0A7ieiX6EsaD8D8Dq77dIWYj+p07hMRL+DehZ9OSwPW980w8yVRDQdys/ak0T0iY/kxn26i4ju8lN0MH0UUIrdq6EUWe/CpMRi5l/0+DQaAIgoA8ovZYGbQsbf+wNQ4+8gWI+/gdy/TKhr5YD6BvDrmN4PvtpaBOWfKx3Kx5QZX201xpL1AbyrW0Ep03w+78zMRLQTaqwIhEz9t0grmeqTqL93RcklCPUMM9cQ0cNQiqD+UMLUx5GoWztyNAbWTb7SMvMPRLQJwKVQwhfgOcNoVUdHKGERACYR0cUWyRxQH0CToF5E7hxi5in+6rKoOwbKUuxKKCewo5k5qB3cBEGIDtoS4Rs9ZnwPZTFxObzPdLpQD1ZDhmXHCvhxbAtrYTYYx7ZWeIyDRGRcjysB/A7KsbAZo80fQVne+sKpfGLm50jtIHc5gKH6uF4f64houIUVhoc1Rz1RX9ZgdW0/W1my1JF1UO9BK8uXQDHu+XL4/wC0UjB5ter2UdcRKN8wvrBy5u3wNlGmnUO/CWVF9FeoyaqdAMqY2aGVDC/gpJPrcDEf6oN6Ck4qubwpekI9f28YE4OtSO1g5k6qKZ03C8pg+QzKV9HZRNSLmbcQUR8oJ+97cXIXNYNQ+5nX8ZCZV2hLj0ugrNPOhVoeeSmAaUQ0hpk3AmHpJ0GPy0SUq8usBnAP1KqM3QAqtGJhBpSCLtx908w8KKfxZ0BZGbrfHwPjPq2FWpboCw/rJD8YFlmj3P4uNf3N0wou9zh36jqGBnL/9kNNco8B8DwRXeyuKI0Qvtpq3KcF8L+RViDGBXUhUu/wcNQZ8ntXlFyCEAH0i7gIyqqqFyKk5IJ6MRLUx826ANLPh1o3PgbAj8y8PoA8U3Dyo+QMP2lvgrWSK2i0gut1qFmmQgDnazNWQRAaEcxsJ6IvoZRc5plmQ9Br6iVrppdwM1lQu3FZhQOuM4XGMoOFzOyuTIoKzLyYiJ4C8CiA6UT0OjMfNSXZDaArgDnM7He3I7ey90FbcgEAEZ0JNabmQFkeP6aTFkMtUemCk0uzzBgTKeUIbDfJcNzXYDDucToRxXmZVe7ilra++AjKh9BAQ9FQhzKMfrqAmV8KX9N81lVZl4koP4yDslh8m5n/bBGfHeb6DN6CcoNwmd6VzqbbUgnP3SXDfv5auTNS/2yrD2+cR0RdmXmH/m30zywv6b2FG+Ps61DKmSkAHsTJnSb/Y7HCoF77mba4f1sfxi5yz0NZr82GWqoFRKefXKX/PsfMVlZs9dU3nej79Wcoi7fpsB57gZP36QtmfjjMbSjQ3y7d9FLF8wH8xMyGQvcLKAXxaB1nhJkx+mwXeCfU8bcKqp8s1H+XEtEFepluXcgKIC7Ytu7Reacx8y8B5vH5vJMyCQvmfWkoo7OIKKGerbmi/t5tcL4TBOFURFscZOmfAa+fDrHOLCiHyIB6UdcGkO11KGehJVBOaANhiv57AzOT1QFlYl4FIJeIenktKUD09XwNakZ8J4CRdfW5IAhC/UIB2OZD+W4AlCBoYDg1TiUiq2UWVlaj7lzrHqAV5IY/nOWmKMOqY0IA5UaSp6EsMFIB/NEtLmxtZuYNUB+XgLI6NjA+rm7wkvVG/fcrL36C3DEE2p7uEXpsv8BLPkM5FtQErV46s0vnu8aiTrOfouXBlB0sWqm1WP+ca/K9EgwR66fMvBPKQqIdEQ0Nc/GGnzGPd7f2WXWll3x16gcGzFwKZZWVADUOTNL/u/ukqq/zN1xC/M+bzKTlpv/qdGZ/psazOMmLJavHeOeGYZ1/HRHFw8IPmYmIjod6kvJR/dM8/tS1n4SCrzrb4qTVUr3CzO9CWTl3BnCbl2TGfbrC7GcuAAJ9jgzLrD9AbRqw1CLOUHIx1A6KZr7S4ecSUVf3womoL5Rlqx3AykAb745W2IwH8I4ubxn59zfpjRwisno/jYJSSh8DEIgRgJm6PE9f6b9DtUWjO5fC+2SRB6x8dG6GGu+uCzBbo33vipJLEOoZ7TPqKSgrrhqobWDrs754Iroeaq14KtQg6c+/DACAmQ8yc1tmTgvEkoGIhkPNaJXBh3NZLTgaDgR9OaD3ixbs5kENmrugFFyh+sIRBKH++B0RzSOis90jiCiWiH6LkzPnbxtxeubPEHqnm5Vl+oPz8QDrdn6c6jKmQ1k/FUP5GTFYDOWUfTgRzSWiVnCDiNrp9kYMZi6HajMA3K2tTwzmQp3HTUT0GBF5+I8hoi5EdK3p92giuki/m8zpYqB2pwJclyf9P6jJmRF6aZA5z0goX46AsgIOhC+hPnou0cuCjLKMd+VAL/kM5Vi3ID/mAGW5AwB/JSKns2hdzkwo3ycFAN4Lsty6cBvUu2sYgC+IqL9VIh1u9QHzLtQGK6OI6AXtlsA9b3tSjpjDgaF4eJOIRrtHElEMEY2yer79YPi+mmD+GNUfPy/Au4VCKP3AYL7+OwXelyoahO38tfxi1OfP8bMRf4NJofUO1LLIngAecxsThwO41VeBWsn6PZTP1b9BKS3c/ZAZ1Es/I6LORHQTETWziB6n/5rHn7r2k1Aw6swjomRTnc2h5M968cXlBcM6y9LfFjN/DyVf9wDwNikXIi4QUSsiut1NMRroc2RYZt3h9tuwBt4MpWjsALUC5Fe39hVAKZVjALykr6HRrpYAXoJS5r4V6moMLTNcA7XEtx+AFdpCMFhsUL4qzW1tC8Cw6ptbByuoGVAO0x8lteGLh9KIiM4g5aIAAKAtOD+GUhS9SKaNKEg5kZ8RZBuAk7LEs0TkMaFERGe79aFG+96V5YqCEF4eIqIppt9pUI6DO0L5wbiLrXfIcM/nzpvM/D+L8JlEZFiGJUFtMZwDtdU9Qy0NvKceTVINhdV7ATh7/w/ULMv1RPRwgJZlVtyJkxYFBQCmejEU+ZqZX6ljHYIghI84qI/JKUS0H+rD6TDUbHk/nNzNbYbFkrvHoHZcuh1KyfIz1EfNQKhdGq2Wr5h5GUrQ/QrKGioH6mOgAsC1zOz0oaGXlV8O5X/lVgDXENFGqNn8JlA7afWGsnYNy7LrIPgXlH+WblA+Yh7TbT5GRGOhPnKmA/gDKd+Ke6Ecs/aGUuitwsmdbs+E+sA9QkTroPyZJAMYDPUO2QvTxAgzFxNRHtQyrxeI6FYo3y/pUL68CGoJhvsyFUuYuZCI/h/UNf6KiFZCCf85ULtWzQbwe4t8O4joR6g+8yMR/QA1y7yZmZ91T+/GbKhdPK/WeZdD7Zg3GMpKogTA1d78R4UTZj5IROdAKSyGAdhARNuhPhaPQym2euPkzm1fwGRRopcxXQbVT38H9U41+mmiztcL6j6G/A5k5neJ6AEoi8LPiegXqI1hTkApSc6E8uP5WygFSqAsBmDcz+1EtALK4vtcqGtQH/3A4H9Q12ew/m3lk8qoL5znPwpq/CqD/w+7T6DGyXQo68ZPmfmEnsT8EGrDjquJaAPUGHoegH/A09rTnfkAzsbJXajnWyWqx36WCjWevUhE66H8H9qgnGf3hrqXD5rS16mfhMi/oK7PIAAFRLQKapwbDvXumI+TqxjqFWb+koj+h5O+b624HmpnwfEAxuo+UQT17u0K5UokBiedswfzHBmTEk0A1MJz2eRSqPsGeC5VNLgVqr+MgrqeK6Cu50ioZ2c9wrQruu63N0Itn78d6h0zKsjJ8MVQG74U6HdFLJSlWjMA3+HkKplg2lVEagOFhVAuAh4jop+g5IlWUPcoHeo9vdiU9Tao9/dFAAr1tWui27MRykVAwBMMzPwOKV98jwFYovvAZn1uPaH6y3nQyq1G/d5lZjnkkCPEA+plwhZHJYAdULMKA4PI537c7ZbPPd4BZT5bBCWQPAqgs4/2Zul8J4I4xzk6zzT9uxmUoMYAfhNA/jionaUYwGU6bIT+XRREO6YFeM3mR7tfyCGHHM6x4nI9hnwPtSSxWo8fv0B9MAz1kX8olPB8XOf5HkpB5RwLLfIY4wBBfaBthBJ6D0N9XJ7ho74mULPWK3T6aigF2Voo5c85bumNMWlaHa9PQOMg1DIHBnAUQEu3uBYAHoGy4D0K9RG4G0o4ngagryltNyiF2DIoi6JKPTavg1Iapnmpvx/UkvZifU0O6ffNBV7SP6Hb+2eLuBgAD+n7XwUl6C+Asgy+Wed7xSJfZ6iPhANQy1sYyheNEf+6DrvOIi9BWdGsNF2jAt0v0y3Sx+qyan3ckz06jUf+AO/9WKil9/m6f1dDLdP9Fsoy7iwfeROhPu6/MvXTvQDWQM3w5wZ6P3S81+uu4wcA+DeUTFOh27sN6oPs/wC0COba6XTNoZ6pbbof7oX6yOtWX/3AlOZpnBwnng7gXgV8/j7KeEvX91qA/eOfOv1Ci7Z8APXBWA6lJLglwD7bQref9V+f7a6HfpYCpYhbrPv9CSj5dQvUx3/PcPQTf/1Zp8nWafIt4tpCKYUKdZ27oKyO2nk7x0Dq9NGGIz7S5EDJ+V7vLdSYmgelwP0EirN8AAAgAElEQVQVauXIAd035gAYE+xzZEr3o45bZRF3qek5usjHOTSDUqxs0n22HGrC62EASRbpR3trTyD3T8f/XcfvBJAdwL1w3j99//+l+1oV1HP/hJe2BnzfoRTjT+lzP677VhHU+/gBAF289MUXTW0pgBq/kgB8rese6pbH5xgINcGyUJdZDfUOXq3vkbt80Sjfu6QTC4IgCIIgnBIQkdJ0Kd82giAIgiAIXtFLb18G8C9mDtdybyFKiE8uQRAEQRAEQRAEQRAEodEjSi5BEARBEARBEARBEASh0SNKLkEQBEEQBEEQBEEQBKHRIz65BEEQBEEQBEEQBEEQhEaPWHIJgiAIgiAIgiAIgiAIjR5RcgmCIAiCIAiCIAiCIAiNHlFyCVGDiHKJyEFET7uFxxDRBCJ6hoi+JKKjRMRE9FOA5XYgoheJaCcRVRHRXiL6DxF195MvhYhmENF2IqokooNE9B4Rne0nXwIR/YmIfiKiciI6TESfEdEFgbRX8I++/xFbW01EWbrOokjV6aUdf9TtGBfNdgiCIAinDz7ks05EdBsRLSaiXURUTUTHiWgdET1GRM29lGe8U30dk3y0pwcRva7luSot371IRO3Dfe6nI0Q0Rd+D+RGsc5quc1qk6rRoAxHRBt2XE6PVDkEQwk+sn3hx2CXUC8yMQYMGYfv27SgoKHgQwINGXGlpKVq2bOmRp0+fPn3gp09u2bIFqampKCkpQc+ePdG/f39s27at/fr1669LSkq6btWqVTj33HM98u3fvx9dunRBQUEBMjMzMXjwYBQXF7detWrV5TExMZcvXLgQEyZM8MhXVlaGs88+G99//z1at26N4cOHo7S0NHH58uW/sdvtv3n22Wdxzz331OEKCV4Iy5g0YsQIrFixAsuWLcOIESM84gsLC9G5c2dkZmZmhqvOulBRUYFu3bohKSnpg5qaGsTFxUWrKcKpDUW7AYIlIoMJEceXfHbuuedi1apViI2NxYABA9ClSxccPnw4bvXq1QOOHTs2IDMzc7rx/jRjhCUnJ+Oqq66yrPf2229/C8Bb7uErVqxAYmIiKioqkJOTg27dumHjxo2dtm7delvr1q1v27ZtG7p39zmHKfhh3rx5uPHGG5GXl5cHIC/U8oqKigwZCkVFRZZppk6diunTp2Pq1KlTAUwNtc66wMz46KOPMG7cOEybNq08Gm0QBIgMVj8ws69DEOqFN954gwHwo48+6hF34sQJvu666/i5557jlStX8kcffcQAuE+fPj7LtNvt3K9fPwbA9913n0vcrFmzGAB36NCBy8rKPPJecsklDIAnTZrENTU1zvDFixezzWbjpKQkLi4u9sh35513MgAePnw4Hz9+3Bn+3XffcVJSEhMRr1u3zu/1EHwD9bEXtvKGDx/OAHjZsmWW8dXV1bxlyxbOz88PW511xei7s2fPjnZThFMXf7KAHNE5BCHi+JLPrr76an7uuef40KFDLuEHDx7kESNGMAAeNmyYR77CwkIGwJmZmUG15cSJE9yuXTvLd+C9997LADgnJ4cdDkdQ5QquzJs3jwFwXl5eWMoL5H7/+uuvvGXLFv7111/DUmco5OTkcFJSEh84cCDaTRFOT6Ita5yShwhYQlQYNGgQExEXFhb6Tbts2bKAlFwffvghA+Ds7Gyura31iDcEsBdeeMElfNOmTQyAmzdvzseOHfPIN2XKFAbA999/v0t4SUkJx8XFsc1m44KCAo9806ZNYwA8YcIEv+co+CbSSq6GRElJCSckJHC3bt1EkBfqi6gLI3KIDCY0DIKRz8zs3r3b+a7etWuXS1xdlVyzZ89mADxy5EiPuNraWu7atSsD4I8//jiocgVXoqHkakjMmTOHAfATTzwR7aYIpyfRljVOyUN8cgkRZ82aNVizZg2GDx+OrKyssJW7ePFiAMCkSZMQExPjEX/ttde6pHPPd+mll6JZs2YB5/vkk09QU1ODc845x8M035zPSGdQVFQEIkJWVhYcDgeeffZZ9OnTB4mJiUhPT8c999yD8nJlNV1aWoq7774bWVlZSEhIQLdu3fDss88GdkFMjBgxAkSE5cuX46uvvsLYsWORlpYGm83mcV6fffYZLr30UrRt2xbx8fFo3749Jk+ejE2bNlmW/f3332PChAno2LEj4uLikJKSguzsbFxzzTX48ssvPdLX1NRgzpw5GDx4MJo3b47ExET06tULDz30EEpKSoI6LyICkXcr36ysLBCR01x++fLlICKsWLECADBy5EhnGcb1AVzvkRU7d+7E7373O3Tp0gUJCQlo2bIlRo4ciTfffNMy/bRp00BEmDZtGg4cOIBbb70V6enpSEhIQOfOnfHQQw+hsrLSMm+rVq1wySWXYPv27fjiiy8CuzCCIAiCECShyGfp6elIS0sDAOzZsycs7THkE0OeMhMTE4NJkya5pDMwv3P37NmDKVOmoH379khKSkJOTg4WLVrkTLtq1SpcfPHFSE1NRVJSEkaOHIk1a9YE1U6zzFBbW4uZM2eif//+SE5ORosWLVzSlpWVYcaMGRg0aJBTBurTpw+mTZuGEydOeJRtt9sxd+5cnHPOOUhJSUF8fDzatm2LnJwc3Hvvvfj111898gQro3hj/vz5ICJMmTLFMt6QqcxuH6ZMmeKUiXfu3OkiY5n7lPkeWfHxxx/joosuQlpaGuLj45GRkYG8vDxs2bLFMr1Z3vv8888xatQopKSkICkpCbm5ufjggw+8nufkyZMRFxeHl156CQ6Hw+c1EQShceDPJ5cghB1DGBk9enRYy12/fj0AYNCgQZbxRriRLth8+fn5OHHiBJo2bRpQvuzsbLRs2RKlpaXYtm0blEsxV6655hp89NFHGDFiBLKzs/HVV1/hueeew5YtW/DGG28gNzcXx48fx9ChQ1FaWooVK1bg3nvvRWVlJR555BGf18OKhQsXYu7cuejduzfGjBmDQ4cOufh5uuuuuzBr1izExsZi0KBBSE9PR35+PhYsWIDFixfj3XffxcUXX+xM//nnn2Ps2LGoqanBgAEDcO6556KmpgZ79uzBokWL0Lx5c5x//vnO9JWVlbjooouwfPlypzCZlJSElStX4plnnsGCBQvw5ZdfokuXLkGfWyC0a9cOeXl5WLJkCQ4cOIDRo0ejQ4cOTkVZu3bt/Jbx3Xff4aKLLsKRI0fQuXNnXHHFFTh8+DCWL1+O5cuXY8mSJXj11VctlW+7d+/GwIEDwcw455xzcOzYMXz99dd45plnsHnzZq9C2OjRo/Huu+/i/fffx5gxY0K7CIIgCIJgQSjy2aFDh1BaWgoAaN/e2h98WVkZnnrqKRQVFSEhIQE9e/bEpZdeivT0dMv0dZXrDIqKijBw4EA0bdoUw4cPx549e7Bq1SpcffXVePPNN5GQkICJEyfizDPPxJgxY7Bx40YsX74cI0eOxLp164L29cXMGD9+PJYsWYJhw4ahd+/e2LVrlzN+z549uOCCC7B582a0bt0aQ4YMQZMmTbBmzRpMnz4d7733HpYvX+7il/b//u//8OqrryIxMRFDhw5FWloaDh06hB07duDZZ5/FhAkT0Lp1a2f6UGSUcDB06FCcOHEC7777rocPNkMJysyw2+3O/915+OGH8fTTT8Nms2Ho0KHo2LEjfvzxR7z22mt45513sGjRIowdO9ay/n/961/461//ikGDBuHiiy/GL7/8gtWrV+Pyyy/HO++8Y+kTrlWrVsjJycHq1auxbt06nHXWWeG4FIIgRBM/pl6CEHbOOeccBsBLly4NKH2gyxVbtmzJAHjDhg2W8YcPH3aa0pv9Zw0YMIAB8OLFi72W3bx5cwbAmzZtcoZdccUVDICff/55r/kMH2EffvihM8ww4wbAPXr0cPH1tWvXLk5NTWUA3LdvX77qqqu4oqLCGW/4J2vWrJmlbzFvGMvzAPBLL71kmebFF190XuctW7a4xL333nscGxvLLVq04MOHDzvDR44cyQD4zTff9Cjv0KFDvHbtWpew+++/nwFwz549ec+ePc7w8vJyHj9+PAPg3Nxcj7KMtgcabpCZmckAnMsuHA4H19TU8HnnnccA+IMPPrC8jt5M7SsqKjgjI4MB8N133+2yLHbTpk3cpk0bBsBz5851yTd16lRnW2+++Wauqqpyxm3evJmbNm3KAPjrr7+2PI8NGzYwAO7Vq5fXcxWEEIi6WbkcIoMJ0SdY+czMgw8+6PSR5Y5Z7nE/4uLi+JFHHvFYjn/06FFnmiNHjljWuW7dOgbAqampLuHmd+5dd93l8q7+5z//yQA4PT2dW7Zsye+8844zzm6388SJExkA33TTTQGfu/n8OnXqxNu3b/dI43A4eMiQIQyA77zzTi4vL3fGlZeX83XXXeexZLCoqIgBcEZGBu/fv9+jzPXr17v4kaqrjOJtuaK/ZYyGfD58+HDL6+EuQzkcDq6uruby8nJ++OGHGQD/+c9/dknz8ccfMwBOTk7mFStWuMTNmDGDAXBKSoqH/yxD3ouPj+dPP/3UJe4vf/mL052JN+6++24GwM8884zXNIJQT0Rb1jglD1muKEScDRs2AAB69eoV1nINM+/k5GTLeMMCCwCOHz8ecD5z3nDkMzNr1ix06NDB+TsjIwPXXXcdAGXm/eKLL6JJkybO+LFjx6Jfv344fvw41q5d67Veb4wZMwa33HKLR7jdbsfjjz8OAHjnnXfQs2dPl/jLL78ct956K44cOYLXX3/dGX7gwAEAwEUXXeRRZmpqKgYOHOj8XVFRgRdffNF53h07dnTGJSYmYu7cuWjatCm+++47rFq1Kuhz8wUzo7a2FlVVVS5LR202m0eYLxYuXIjdu3cjKysLM2bMcFkW27dvX0yfPh0AMHPmTMv8GRkZmDVrFuLj451hvXr1wvXXXw8AWLp0qWU+41nZsmWL12WNgiAIghAKdZXPvvjiC8ycORM2m83SpUJCQgJuueUWfP755yguLkZ5eTk2bdqEBx98EESEJ598Eo8++qhLHvPSPX9ynTcZy+pdfcsttyA1NRV79uzBhRde6LJzts1mw4MPqs0kly1bFuDZu/LUU08hOzvbI3zJkiX49ttvkZubi3/84x9ITEx0xhkyUJs2bfDGG284LeIOHjwIAMjJyUHbtm09yjzzzDPRpk0b5+9QZZT6gplRU1ODyspK1NbWuriaqKmpcVki+Pe//x2AWlkwbNgwl3Luv/9+5Obm4ujRo3j55Zct6/r973+PCy+80CXsgQceQEpKCvLz810s68z07t0bgHerQEEQGhei5BIiSllZmdPfVGpqapRbE13i4uIwatQoj3BDODrrrLOcpt1munXrBgDYu3dv0HVeeeWVluEbNmzAvn370KdPH+eL3p3hw4cDAL799ltn2Nlnnw1ALbtctWqV0/zcih9++AEnTpxAhw4dLJfcpaWlYdy4cQDg9I0VLqqrq52KLJvNddiz2WwoLy8PyA+D4cvrmmuucVnmaTBlyhQQEfLz81FcXOwRf/7557sItgaGUtHbPY2Pj3cK84bQKwiCIAjhoq7y2aZNmzBhwgTnZJkhK5hp3749XnrpJaeLgMTERPTt2xdPP/200z/WjBkz6iTX+GLkyJEuk0qA8uVl+IZyV4YAoclYAHDFFVdYhn/yyScAgPHjx3vIIYBS5J111lmora11+gTr2bMnmjVrho8//hhPPvkkdu7c6bPuUGWU+sBduWWz2TyWSlZUVDgnI41JTm9+wG688UYA3uXESy65xCMsPj7e6QbD231t1aoVgJOTt4IgNG5EySVElKNHjwJQs3rugkeoGEqAsrIyy3jzrKDZwby/fOa84chn0K5dO0sH+Ua53nxUGPF1sejJzMy0DC8oKAAA/Pzzzy5OQs3H1VdfDQAuTk6feuopDBgwAJ9++imGDh2K5s2bY/jw4Zg+fbqzTANDoLJy0m9gCCGhCl+GsMR80teDlWBlhDNzQNfT3zk0adLEaZlndQ6dOnWyzNe8eXMAvu+pkebIkSN+2ykIgiAIwVAX+Wzr1q0YPXo0jhw5gnvvvRd/+tOfgq533LhxGDBgAGpqalw2VzFb3/uT66xkLMC/HGUVb8RVVVUF0HpX2rRpYzmRBZyUs+6//36vcpahCDPkrGbNmuHf//43EhMT8ac//QlZWVlIT0/HhAkTMH/+fA+ZIVQZJVwYspchi3lTbgFqAyFjIrKkpARVVVWw2Wxe5VV/cmJd5SyRsQTh1EIczwsRxdhlpqqqCtXV1WFVdGVlZaG0tBQ7d+5E//79PeJ3794NQM1QmoWnrKwsrF+/3usM2bFjx3Ds2DEArkoiYybQ18yaUafVLkVWM3nBxNcFb8KXYYHVsWNHvw5nzUsZ27Vrh7Vr12L58uX4/PPPsWrVKqxevRpfffUVnnjiCbz00ku46aabXPLXl7NTQAlUDocDtbW1cDgcAe+SYyxbjIuLs5z9dKeu5xDKPTX6oNkhrSAIgiCEg2Dls23btuH888/HwYMHcccdd4S0BK5nz55Yv369i+KiefPmzs17du7ciX79+nnk8yVjAZGXs7zJWMBJOSuQnSvNsuZVV12F0aNH4/3338dXX32FVatWYdGiRVi0aBGmTZuGlStXIiMjwyV/fcpZZtxlLGblUL66utoZFsg1ttlsqKiocCkv0nKWyFiCcGohSi4hoiQlJSE5ORllZWUoKSnxugNPXcjJycH69euxZs0aXHrppR7x33//PQBgwIABHvnee+89r1tGG/mys7NdZgtzcnIAwGu+/Px8lJaWIikpKegdeiKNISC1b98e8+fPDyqvzWbD+eef79xFsaysDHPmzMFDDz2EO+64A1dddRWaN2/u9MFVWFjotSxjptPsr8sXcXFxqKmpwfHjx5GUlORUbhERamtrsX///oDKMWYZy8vLvc4Im9vlbqVmUFlZ6TSFD/QcAqG6uto5Y23eRUkQBEEQwkEw8tn27dsxcuRI7Nu3D7/97W8xe/bskOouKSkB4Gq9BSg5a+nSpVizZo2lksubXNcQMeSsCRMm4I477ggqb4sWLZCXl4e8vDwAwI4dO/Db3/4Wy5Ytw4MPPog333wTQPhlFEPRaV4JYcY8yVtbW+thQR8oNpsNdrsdSUlJSEhIQFVVFYqKipxLR80EKycGitEHzT7OBEFovMhyRSHiGMqhzZs3h7Xcyy67DACwYMECS99Qb7zxBgBPfwlGvg8//NDSeam3fBdffDHi4uLwzTffWCpujHxjx44N+9LMcHP22WcjNTUV69evR35+fkhlJScn48EHH0R6ejoqKyvxyy+/AIBzG+/i4mJLB+slJSX48MMPAQAjRowIqC5DyNm0aROqq6vBzE6z/6VLl6K2ttYyn3E/zPGBLFs0fI289dZblmW/+uqrYGZkZ2eHVQAznpXevXu7bEQgCIIgCOEiEPlsx44dGDlyJPbu3Ysbb7wRL730UkiWQ/v378fKlSsBAIMGDXKJM+QzQ54yY7fbsWDBAgDe/WA1JIwNehYuXBhyWV27dnUuDd24caMzPNwyipFm69atlvHG8kqHw+Hi99SQsXz5aXXHZrPB4XBgyJAhAIDXXnvNMp0xERuonBgoRp83ngFBEBo3ouQSIs7IkSMBuDowDwfGzoP5+fl4+OGHXeLmzJmD5cuXo0OHDh7OLM844wyMHTsWR48exS233OIiGLz//vt47bXXkJSUhLvvvtslX6tWrXDLLbfA4XDgpptucpnpWr16NWbMmAEi8mhLQyQuLg6PPvoo7HY7Lr/8cufsqJnq6mp88MEHLsLOzJkzncsFzKxduxb79u2DzWZzzl4mJibitttuA6B2zdm3b58zfWVlJW6//XacOHECubm5OPfcc32211iWaAg5Tz75JGpqapwKrs2bN+OPf/yj1/yGT4pt27a5hPvbbXHChAnIyMhAYWEhHn74YRfT+s2bN2Pq1KkAgPvuu89n+4PFeFaMZ0cQBEEQwo0/+aywsBAjR45EcXEx8vLy8MorrwSk4Hr55ZctfSht3rwZl156KSoqKjBkyBDk5ua6xN94441o164dli1bhhdeeMEl7qGHHsKOHTswYMAAyx2eGxqXX345Bg4ciBUrVuC2227D4cOHPdLs37/fZdfA9evX4+2330ZFRYVHWmNS0Ly0MdwyyqBBg9CsWTP8/PPPeOutt5zhzIzZs2c7Nw0AXP2etm7dGvHx8Thw4IBzp0h/GBb1t99+OwDg+eef99hp+9lnn8W3336LlJQU3HzzzQGVGygiZwnCKQYz+zoEIeysW7eOAfDw4cO9prn99tt58ODBPHjwYO7VqxcD4MTERGfY4MGD+eWXX/bI9/PPP3NqaioD4F69evGkSZN44MCBzvwrV660rG/fvn3cpUsXBsCZmZk8ceJEHjp0KBMRx8TE8Ntvv22Z7/jx4zxo0CAGwG3atOEJEybwmDFjOCYmhgHwzJkzPfIUFhY667Fi3rx5DIDz8vIs4/Py8hgAz5s3zzLeiuHDhzMAXrZsmc90f/zjHxkAA+B+/frxFVdc4bwWycnJDIA//fRTZ/qUlBTntb7yyit58uTJPHToULbZbAyAH3roIZfyKyoqeMSIEQyAk5OTedy4cXz11Vdz+/btGQB36tSJd+zY4dEuo00Oh4PtdjtXVlZyeXk5//zzz9y8eXPn9bziiit4yJAhHB8fz5MnT+ZOnToxAN66dStXVlY6j0WLFjEATkhI4IsuuohvuOEGvuGGG/iHH37gI0eO8MaNG73eo2+//ZZbtGjBALhr1648adIk/s1vfsNxcXEMgK+//np2OBwueaZOncoAeOrUqZbX3d89v/LKKxkA/+9///N5/wShjviTBeSIziEIEcWffDZgwADnu/P666/nvLw8y2PLli0u+fr3789ExP369ePx48fzxIkT+ayzzuLY2FgGwD179uTdu3db1rl8+XJOTExkADxw4ECeNGmSUy5MS0vjrVu3euTx9871JxMZMkeg+JPrDHbv3s1nnHEGA+BmzZrx0KFDefLkyXzFFVdwnz59mIi4bdu2zvTvvfceA+CkpCRn2vHjxzvl1WbNmvGaNWtc6qiLjOJLBvnb3/7GAJiI+Nxzz+Urr7ySu3XrxnFxcXzPPfcwAD7vvPNcZKzKykq+7LLLnHLdxIkTecqUKXzfffc54x955BGnnHjs2DHnUVpa6izXZrPx8OHDefLkydy3b18GwE2aNOEPP/zQo52ZmZkMgAsLCy2vva97XlJSwnFxcZyRkcF2u93nPRSEeiDassYpeYiAJUSF3NxcJiK/LyNfhzfhpbi4mG+99VbOyMjg+Ph4bteuHV977bX8yy+/+GxTaWkp33fffdy1a1eOj4/ntLQ0vuyyy3j16tU+81VUVPBf/vIX7tWrFzdp0oRbtGjBY8aM4SVLllimb8hKLmbmFStW8KRJk5zXLyUlhXv27MkTJ07kN954g0+cOOFM+/rrr3NeXh736dOHW7ZsyU2aNOHOnTvzZZddxp999pll+dXV1Txr1iweNGgQN23alBMSErhHjx78wAMP8KFDhyzzGPfcUG6Vl5dzRUUFV1ZW8g8//MCXXHIJt2jRgps0acJ9+vThv//971xRUeFVyVVZWcn/+Mc/+IwzznAKzwD4448/5mPHjvlUcjEzFxUV8W233cZZWVnOazRs2DB+/fXXPYRH5tCUXCUlJRwfH8/dunWzLFsQwkDUhRE5RAYTGga+5DNDkeDvcJc1XnnlFR4/fjx3796dW7RowbGxsdyqVSseNmwYP//881xeXu6zTVu3buVrrrmG27Zty/Hx8ZyRkcG33nor79271zJ9Q1VyMSuZcc6cOTxs2DBu2bIlx8XFcbt27XjgwIF833338apVq5xp9+3bx0899RRfeOGFnJWVxYmJiZySksJ9+/ble++9l4uKiizrCFZG8Sd3vvLKK9yvXz9OSEjgZs2a8QUXXMArV67kzz77zKuSq7i4mKdMmcIdO3Z0KjM7derkV8l19OhRPnz4MP/3v//lCy64gFu1asVxcXHcsWNHvv766/nnn3+2bGMoSq7Zs2czAH7iiScs8wpCPRNtWeOUPIjZp4PA4L0HCkIALFiwAJMnT8ajjz6Kxx9/PNrNERowhq8Hw+zeWJIYKsyM6upqy7KY1Q5BTZs2DWi3xfpk1qxZuOuuuzB79mzceeedUW2LcMoSma24hGARGUyIOCKfCQbMrjtWG35Ww+Ho3263o6amxnI3REPea9asWb3sNO7OwIEDsXXrVhQWForjeSEaiAxWD4iSS4gKzIzc3Fxs27YNBQUFsmWv4IEhWBmOS8Ol3DKXb/jx8hYPRE7IsqKyshLZ2dlITk7GTz/9FHWFm3DKIgJWw0RkMCHiiHwmuCu3DPmrrKwM27dvx5lnnhlyHcZujN7kK7vdjri4OCQlJYVV9nPno48+wrhx4zBt2jSnzzJBiDAig9UD4nheiApEhFmzZuHo0aN45plnot0coQHhcDhQXV2Nqqoq2O12pzPS+hRyrAhkt8X65sUXX0RxcTFmzpwpCi5BEASh3hH57PTFsGKvrq722LHaiI+ULGaz2VBdXe11I6BwwMx49NFHkZ6ejvvvv7/e6hEEIfKIJZcgCA0Ch8MBu93u3N0y3JZbVvVVV1f7tNJiZpSWliItLQ3Jycn11hZBiDIyi9gwERlMEIR6x/BhY7iGMGQvdxns+PHjKCwsRL9+/UKu058lF6DktJKSEmRlZUXNol4QIoDIYPWAjBiCIEQVQ7CqqqpCbW1t1Cy3rCAi7N+/H6WlpS5bcQuCIAiCIDRmjGWJhvW82XLLSgZzOBxhVTb5k/NsNhsKCwtRUVEBP0YZgiAILsRGuwGCIJyeMLNzJg+of8utumIIfZWVlUhKSop2cwRBEARBEEKiLpv6hHO5YqDlEBGqq6sRFxeH+Pj4sNQtCMKpj1hyCYIQUYauTIsAACAASURBVAzLre3btzc4yy0rHA4HYmNjUVVVVa++IQRBEARBEOoTh8OBqqoqVFVVOZcmBiqDhVPJFYx1vM1mQ0VFhVjUC4IQMKLkEgQhIhjKrcrKStTW1qK4uDiqyq1ATd8dDgdiYmJgs9lQXl4uQpYgCIIgCI0K87LEYJVbBpF0PG/G2AhIli0KghAoslxREIR6pbEsS/SG4YPCZrPBbrfLskVBEARBEBoFDocDtbW1sNvtAEKTwcLtk8sfZoWWsduiLFsUBCEQRMklCEK94Eu51Zhm4sxCnc1mQ1VVFeLi4hAXFxfllgmCIAiCIHhSHztWR9qSyyx/GZZnFRUViI2Nld0WBUHwiYwQgiCElYa8W2JdsBKyZNmiIAiCIAgNjfqUwaKp5AJk2aIgCIEjSi5BEMKCYbllCFYAvApW0fLr4N6GQDG31RCyKisr66NZgiAIgiAIQeHu97Q+JhiZOWwWVIHIYIZPVDPGskXZCEgQBF/IckVBEEKCmZ0m8YbyKhAhKNpKrlCQZYuCIAiCIESbSPo9NRzWRworH2CybFEQhECQkUEQhDphttwyZtSC2Ya6MSPLFgVBEARBiBaRsNyyqjPaSi5Ali0KguAfseQSBCEo6mq55V5GQ7DkCkUhJ7stCoIgCIIQSQzlVjh2S6xL3ZGU3ex2u1f5UnZbFATBF2LJJQhCQIRiuWVVVrSVXOGo31i2KL4hBEEQBEGoL8yWW6tWrYrKpj7h8snFzAH75PJWn3nZoljUC4Lgjii5BEHwiWG5FQ7llrnMaCu5AhGK/LVTli0KgiAIglBfWO2WCETHr2m4fXL5K8uXkguQZYuCIHhHlFyCIFhiKLeqq6tRXV0NIHTlVkOhpqYGO3fu9GuBFYgyTnZbFARBEAQhnASzY3Uk2xSO+k+cOIE9e/b4VUz5U3IBstuiIAjWiE8uQRBcYGY4HA7U1tY6Z+3C7fMhWpZctbW1KCoqwoEDB5CSkoL8/Hz07t3ba/pABCxAdlsUBEEQBCF0wuH3tL4IVXYrLy9Hfn4+KioqEBMTg7i4OLRr185r+kBkMNltURAEK0TJJQgCgJM+EmpqaupNuWWuK5JKrtraWuzatQv79u1DRkYGhgwZArvdjh9//BGHDh1CWlqaZb5AlVzmZYvNmjUTIUsQBEEQhIBpyMotg7rKbhUVFdixYwdOnDiB7OxstGrVChUVFVi/fj1atmyJhIQEy3zBTDTa7XZUVFQgKSnplFhxIAhCaIiSSxBOcyKp3DITCSHEbrdj9+7d2LNnD9LT05Gbm4uYmBhnXPfu3bFx40akpKRYWmD52tnHHdltURAEQRCEYGgMyi2DQJVOBpWVlSgoKMDRo0fRtWtX9OnTB0QEZkZsbCy6du2Kbdu2oW/fvpYyYTD1yW6LgiCYaZijqCAIEcHhcKC6uhrr1q1zOjSNhIKrvh2EOhwO7Nq1C9999x0cDgdyc3ORlZXlVHAZbYiPj0dmZiby8/O9lhOMQCe7LQqCIAiC4I9w7lgdKQK15KqursbWrVuxbt06tGrVCrm5uWjTpo1H3tTUVMTGxuLgwYOW5QQjg8lui4IgmBFLLkE4DXE4HE7LLQAoKyuLiHLLoL6WKzocDuzduxc7d+5E27ZtcfbZZ/v1kdWmTRscPHgQJSUlSE1N9SjPrBjzhyxbFARBEATBG4bf05qamgZvueWOP9mtpqYGhYWFOHToELKystCjRw+/sl52drZz2aK7BZbD4QjKz6ksWxQEwUCUXIJwGuGu3DJbbkVy++VwK7mYGfv27UNRURHS0tIwaNCggM3Vichl2WJs7MlhMVhLLkCWLQqCIAiC4IrVpj6NRbllYLTbHfOmPpmZmcjNzQ343OLi4tClSxf88ssvOOOMM1zignEZYSDLFgVBAETJJQinBYZgZbfbASCiVltWhEuhxsw4cOAACgoK0LJlSwwcONCrA1NfJCQkoFOnTsjPz0fPnj2d4XVRcgGy26IgCIIgCJHZsTpSMLOLTGS1qU9dZKa0tDQcPHgQBw8eRJs2bZzhdZHBZLdFQRAAUXIJwilNQ1NumQmlHcyMX3/9FTt27EBKSgpycnLQpEmToPK7K9ratm3rsWyxrkouWbYoCIIgCKcv0drUpz4xrPCNTX2Ki4vRsWNHl0196kq3bt2wfv16tGjRwmmBFcpEoyxbFITTG1FyCcIpiMPhcO7WAzQs5RZQ9+WKzIySkhLs2LEDycnJOPPMM5GYmFjndpjbQETo0aOHy7LFugpYgCxbFARBEITTjVNRuWXgcDhw8OBBbNmyBe3bt8fgwYNdXDyEQlxcHDp37uzcbdGoLxQZTJYtCsLpiyi5BOEUwtitJ1jlVmPwyVVaWort27ejSZMm6Nu3L5KTk0Nqg1X9CQkJyMjIwI4dO9CjR4+QBCxAli0KgiAIwumCN7+njR1jU5/9+/cHvKmPL7zJm61bt3ZZthiKDCbLFgXh9EaUXIJwClBX5VY0CbR9R48exfbt2xEbG4vevXujadOm9dqudu3a4eDBgzh8+HDISi5ZtigIgiAIpzanqnLLfVOftLQ0dOrUqV4n7bp37+7cbTEcE42ybFEQTk9EySUIjZhwKbeiYcnlj2PHjiE/Px/MjO7du6N58+Zhb4PVtSIi9OzZExs3bkT79u1DVkzJskVBEARBOPU4lZVbBw8eREFBAVq0aOHc1OfHH3+s9/MzL1sMVckFyLJFQThdESWXIDRCGqPllhlfyxVPnDiB/Px81NbWIjs7Gy1atIhw69SyxfT0dOzbtw8dOnQIuTxZtigIgiAIpwYOhwNHjhxx+gRtbDKYN5gZhw4dQn5+PlJSUjBgwACXTX3q6k/VW13eymrdujUOHDiAysrKkB3ay7JFQTg9ESWXIDQiGrtyy8BKuCkrK8OOHTtQWVmJ7OxstGrVKkqtU7Rv3x47d+5ERUVFyGXJskVBEARBaNyYd6zeuHEjcnNzT4n3OTPj8OHDyM/PR3JyMvr3729pec7METvf7t2745tvvnHuDh4KsmxREE4/RMklCI2A+lZuRXq5opmKigrs2LEDZWVl6Nq1K1JTUxuEAEJESE1Nxf79+5GZmRnybKKh5KqoqEC7du3C1EpBEARBEOoTqx2rTwXlFnByU5+EhAS/m/oYu0VGgvj4eCQkJKCgoAB9+vQJuTxZtigIpxei5BKEBoyh3LLb7U7rp4agAAoVZobdbsfmzZtx9OhRdO3aFa1bt25w52az2dC6dWvs2LED3bt3D7m8srIyHD16FKmpqbJsURAEQRAaML4mGKM5ORgOjE19YmJi0KtXLzRr1sxvnnAuVwyE2NhYOBwOHDp0CGlpaSGVJcsWBeH0QpRcgtAAMZRAtbW1TqHiVHkhV1VVYefOnSgtLUXv3r3Rq1evBqfcMnA4HGjdujV27tyJI0eOhOwfzOFwIDY2VpYtCoIgCEIDJRDr+caq5AplU59wKbmCuW49evTAhg0bkJKSEvLkoCxbFITTB1FyCUIDIlrKrUgIa9XV1SgqKnLOyCUmJjb4ZXsOhwMxMTHo0aMHNm3ahIEDB4a0bNFutyM2NhbMLLstCoIgCEIDIhjXEI1NyRWOTX0i6ZPLID4+HpmZmcjPz0evXr1CLk+WLQrC6YGYEQhCA8AQrKqqqvDdd9+hpqYGNpvtlJhlqq2tRX5+PtasWYOkpCTk5uaiVatWjeLcjO2rExMT0bFjRxQUFISlPGO3xZqamjC1VBAEQRCEusDMqKmpQWVlJWpra50TjL7klMai5CovL8ePP/6IzZs3o1OnTjjrrLPqbJUeSZ9cZtq0aYPa2lqUlJSEXJZ52aLD4QhD6wRBaIiIJZcgRBEry61ThdraWuzatQv79u1DRkYGhgwZ4pwBbCznaiilAKBDhw7YsGEDjh49ipSUlDqVZ7fbERcXJ7stCoIgCEKUCWVTHyJq0EqS+tjUJ1qyGxGhe/fu2LhxI1JSUhAbG9rnqyxbFIRTH1FyCUIU8LUs0WazRVxwCueMpN1ux+7du1FcXIyOHTsiNzfXY4lftJVcgZ6rsVwRUNeoZ8+e+Omnn5CTk1OnZYt2u92ZzxCyZNmiIAiCIESOcOxY3VAVI5WVlSgoKKiXTX2i4ZPLICEhAZ06dcL27dtl2aIgCH4RJZcgRBBmhsPhQG1trdPs292Kp7GYwLvjcDiwZ88e7N69G+3bt8fgwYNDnm2LNna73eX+JCYmon379igsLER2dnadyjMrx4xli3FxcbLboiAIgiDUI+HcsbqhWXJVV1ejoKAAhw8fRpcuXeplU59ITlBaycFt27bFwYMHUVJSgtT/z955P6Sxb9F+Kc1GkWIXQZoxicdokpPc9/e/l9MsyU1uVKpiAxWxUIRh5v2Q++WOSJkZpmCyP7+dE50Zisxm7b3X8nj6Oj6lLRLEz83z/gZKEM8EKeIWw4hJrn7geR6np6c4PDzE9PS0JHHL6EkuqdcgXldkzM/PK15bbBW5aG2RIAiCILRFi1CfQWlI1ut1pNNpXF5eIhAIIBaLaVpf6VW7tau/hoaGEIvFVF1brFQquL+/x9zcnOF1KUEQ6kHfqAhCQ1hhVavVUKvVJHUOjSiclJxTEAScnp7ijz/+QLlcxrt37xAOhyUVHYMgckmhnckqK7IODg7QaDRkHa9V5AJ+FFksbZEgCIIgCHUQh/qwoBe1Qn2MFrlYqM9ff/3VDPV5TkKNkiYj8GNtcXFxEYlEQpXrqFarODs7oyAggvjJIJGLIDSATW61E7d63diNLpx6IQgCzs/P8enTJ9ze3mJzcxPRaFSWp4GRIpcgCMjn87i9vZX0s+2KrLGxMczMzCCTycg6d6eijdIWCYIgCEIdtBS3GEbVauxx/fnnn7Barfj48SMWFhaezSR4o9HAyckJarVa15/rVC8BwMzMDB4eHlAoFFS5HpPJRGmLBPGTQeuKBKEigiA0o6jZFJBcz4dBXVcUBAEXFxdIJpNwOp3Y2NjAyMiI4uPpLXIJgoCrqyskEgmMj4+jWCxic3Oz6+RZt2tcWFjAzs4Obm9v4XA4JF1Du0kudh62tuhwOJ5NJ5YgCIIgBgUt1hI7obfIxUJ9jo+PAaBtqM8gI/ZttdvtuLq6wqtXr7r+fKfXjgUBff78GRsbG32tLfI8D7PZDEEQKG2RIH4inofsTxDPADa59fDwoFjgAgZvXVEQBFxeXuKvv/5CPp/H+vo6VldX+xK49H58hUIBf//9N05PT7G2tobV1VUsLCwgmUwqPiYrsvb39yWLkp1ELuB/a4uVSkXxNREEQRDEr4Yek1ut6FWr8TyPo6Mj/PHHH+B5Hh8+fIDNZns2AhcTtz59+oR6vY7ff/+9mY54cXHR9fe6CZQ2m63vOg74MRlnMpmaaYs0UU8QPwc0yUUQfcLzfHNyC1AWRS1mkCa5CoUCEokERkZG8OrVK4yPj6tyXL3WFYvFIhKJBMxmM1ZXVzExMQHgh9g0MzODr1+/4vr6GpOTk4qOPzY2hunpaaTTaYRCoZ4/36too7RFgiAIgpAGs4ao1+uaT261orXI1Rrq8/79+2dVFwiCgLOzM2QyGXi93kfXX6/XEYlE8PnzZ7hcrraPq1e9BACzs7O4uLjoq45jzUdKWySInwsSuQhCISwtkZmP9ytuMQbBk6uTOKQWWj++u7s7xONxAEA0Gm27TsgM5L98+YLNzc22XVEp17m4uChrbbFX6ACtLRIEQRBEZ5i4dXl52bxX6i1KaFWrtYpD7969k+V5ajTM9zSVSmFychKbm5uw2WxPfs5qtSIQCCAej2N1dfXJv0sRuaTUcb1oNBrN6xseHkaj0aC1RYL4CSCRiyBkopW4xRgaGtJ9kosVa7e3t83Emk7ikJrnVJv7+3skEolml9DlcnX9+ZGREczPzyOVSiESiSg6Jyuyvn//jo2Njb4L7dYiiyAIgiCI/4lbHMeB53n85z//wcePHw0RI9QWuQRBQC6X6ykODSrM2iKZTMJut+PNmzc9bS18Ph9yuRwuLy/h9Xof/ZsUkQv4UcextcVoNCr7uhuNxqPzsLVFi8XyrMRFgiAeQyIXQUhEa3GLwbyZ9KRer2Nvbw8AEA6He4pD/aL2umK5XEYymUSlUkE4HIbb7e55fsbc3Bx2d3dRLBYfPW6pBRYAjI+Pw+fzIZPJYHl5WdmDEEFriwRBEATxg06hPoD+ITYMtUQutUN9jKBQKCAej2NsbAxra2s9G3TseWNNwt3dXbhcrkcG8jzPS57Mmp2dxefPnxWtLbZ6pdLaIkH8HJDIRRA9YIamHMcB0E7cYujpyVUqlZBMJnF7e4tYLIb5+XldzquWyFWtVpFMJnF3d4dQKASv16vI6H9lZQVfv37FxsZGs9iRI3IBgN/vx/b2Nu7u7mC322VdQ7trorVFgiAI4ldGjcRqrehX5GKJz8lkEuPj41hfX8fo6KiKV6g9xWIR8XgcFosFL1++VGRtYbVa4ff7EY/Hm4b0gLwajIll//73v2WvLTYajSfpjLS2SBDPHxK5CKIDgiCgWq02/1uvwkoPT65KpYJkMolSqYRQKARBEDRdTWxHP8/lw8MDUqkUisUilpeXsbq62tfxRkdHMTMzg3Q6jXA4DEC+yMXEsk5ri6xAlwqtLRIEQRC/KmqH+qhNP1P319fXiMfjqof6aIn4sd7e3iIejzfrnn4be9PT08jn8ygUCs1J/NY1wl6Mjo4qsp/odB5aWySI5w2JXATRgnhy69OnT/jXv/6la2GlpchVrVaRSqVwc3ODUCgEn8+HoaEhnJ6eanK+Tih9fPV6Hel0GpeXlwgGg1hZWVH82rT+3sLCwiMDebkiF/BjbdHr9eLw8BDBYPDRv7WOxEuB1hYJgiCIXwm54pZeac2dzi2Hm5sbxONxzUJ9tIbneezu7oLjOEQiETidTlWOyyaxPn/+jI2NDZjNZkU1WCf7iW60m+Ri10RriwTxfCGRiyD+S6e1RL2LJy3WFR8eHpBOp1EoFLC8vIwXL14Y2hGVW5RyHIdMJoNcLoelpSV8+PBB9YKDdSS/ffuGzc1NRQUW8GNtcWdnBz6f71EBq+R4tLZIEARB/Aoo8T1l01RG3BvlTHKxUB9BEDQP9dGCcrmMeDyOarWK1dXVnr6n3WArqK3YbLamgXwsFlNcM7Wzn+hGtwYkrS0SxPOFRC7il0dvz61eqDnJVavVkMlkcHl5iUAggFgs1vax6bEiKUZqUdpoNHB0dITT01MsLi7i48ePqohbnR7r2NgYpqenkclkMDU1pehcw8PDiMVi2Nvbe7S2qGSSix2P1hYJgiCIn5F+Qn1YU9CIKRspdRNLfOY4TpdQH7WpVqtIJBK4v79HMBhEpVLpS+AS0+41np2dRT6fx/X1NXiebzth1YvR0VHMzs4+sp/oRq+1SFpbJIjnCYlcxC+LFHFraGhI9wKKiRr9oMfkk5bwPI9sNovj42PMzc3hw4cPigQiJSwuLmJ7extjY2OKn7OJiQl4vV4cHR0hEAgAUC5yAbS2SBAEQfxcqJFYzeolJWJIv3QTuVioT7ValZT4PGiIfU9DoRBevnwJjuNweHioyvE7vc5sEuvLly/wer2KUybn5+exu7uLm5ubniuVgiB0rfVobZEgnickchG/HHImt4zoEvYzVcVxHI6OjnB2dqbq5JPadJrk4nkep6enODw8xMzMDH7//Xfdi1dWZP373/+WHUUthqUter1eTExM9CVy0doiQRAE8TPA8zwajYYq0/Mmk0m3NOpW2tVqraE+Ho/nWd2va7Ua0uk0rq6unvie9hKD1GJkZATz8/M4PT1VvNbJPL6+ffvWc21RyutDa4sE8fwgkYv4ZRAEoVlYMZGl143KZDLp3iVUInI1Go3m5NPCwoLsySej1xUFQcDZ2RkymQx8Ph/ev39v6MTS+Pg4nE4n7u/vFR9jeHgYKysr2N/fx5s3b/oSudjxaG2RIAiCeI5oYQ2hhYepVMR1U6dQn+eCePo/EAggEon0nRDdjV6WFXNzczg8PES5XFZ8jrGxsSep2Z2uRQq0tkgQzwsSuYifnnbiltRulBqrg3KRU7TxPI/j42Nks1nMzs7iw4cPhozty4W9DoIgIJfLIZVKwe124+3btwNTPHg8HhQKBdzf3ytOQJqYmIDb7UY2m+1r/ZFBa4sEQRDEc0JL31OjRa56vY69vb2BCfWRi5zpfz0N/oeGhuByuXB6eor5+XnFDcLW1Ox+r4nWFgni+UB/ocRPCyusHh4eUK/XAfwoiOTcpI0YhZeS2MPErU+fPqFWq+H333/H8vLysxC4gB+vze3tLf744w8UCgVsbGxgZWVFN4FLSudOEAT4fD7s7+/39R5YWlrCxcUFyuVy375i4rVFPSfvCIIgCEIOgiCgXq+jWq2C47jm/UtNocSIRiTwY60vn88jk8nA4XDg48ePmJmZeTYCF8/zODw8xJ9//onh4WF8+PABfr+/q3Cjd4rl0NAQvF4v0ul0X8dgE/Xt6ji5diSsPq9UKlSDEcSA8zy+EROEDPqZ3GqFrSvqCTO7b4d4rc/r9eLdu3eqCEN6riteXV3h5OQE4+Pj+O233wZ29Y7neYyOjsJisTwykJcLS1v8+vUrFhcX+74uWlskCIIgBhU9E6v1nuQSr/U5HA74fD7Mzc3pdv5+4XkeJycnODo6ku17qrc/Lc/zmJmZwcHBgSQD+U6w1Ox0Oo1QKPTo33olK7aD1hYJ4nlAk1zET4Mak1utGDEK305wEgQB5+fn+PTpE25vb/H27VtEo9FndYO9vr7G33//jePjY3i9XgQCgYEWaVhBt7S0hMvLS5RKJcXHstvtGBkZQbFYVOXa2Noie58TBEEQhJHoMbnVil7T9hzHIZVK4c8//4TVasXHjx/hdrufzTSPIAg4PT3Fp0+fUK1W8f79e4RCIVnT/3pPcvE8D5PJhJWVFRwcHPT1Oi8uLuLm5ga3t7eP/r8Sz13x2qJRq7IEQfSGJrmIZ4+ak1utGDHJJRbWBEHAxcUFkskknE4nNjY2FEcqd0PLSa7b21vE4/Hm2Ljdbsf+/v7Aj/XzPN/0XYjFYtjb28PGxobi63Y6ncjlciiVShgfH+/r2th7/O7uDi6Xi7whCIIgCEPQc3KrFa0bkSzU5+TkBPPz849CffQO7FGC2PfU4/H0Nf1vhMg1PDwMm83WcRJLKixt8fv379jY2GjWTEoDgShtkSAGHxK5iGeLIAjgeR4cxzVTX9T+sm/UJBfP87i8vEQymcT4+DjW19cxOjqq63X0y/39PeLxOBqNBiKRiOJRc7URBEFSYSoezbfb7XC5XMhms/D7/YrOy/M8FhYWmmmLaqRK7e7uYn19fWCeW4IgCOLXQBAEVKtVVKtVjIyM6CpuMbTy5GoN9Wm31jfIIpcWDVKjRC7gxyTW9vY27u7uYLfbFR1vfHwcU1NTyGQyWF5eBqBsXZFBa4sEMdiQyEU8O9qJW1oVV0ZMct3f3yOfz6PRaODVq1d9T/3oTblcRiKRQLVaRSQSweTk5JOf0btYakev87f6TwSDQWxtbcHr9Spas+R5Hna7HbVarS+xTAybYKzX65S2SBAEQWiOeHr+8vIS19fXiMVihlyL2o1InudxenqKw8NDTE9Pd/WsGlSR6+rqColEQvUGqRGeXOx8bBOgdRJLLq1imZJ1RQalLRLEYEMiF/Fs0FPcYug5yVUsFpFIJCAIApxOJ9bW1nQ5r1pUKhUkk0nc398jHA7D4/F0fG2MFrmknLu1oBOvLSqZxGJj8f2KZWIEQYDZbEa5XIbD4TBcOCQIgiB+TtpZQ1gsFkPSDRlqeXIpCfUZNJHr+voaiUQCVqtVkwapkZNcwI9JLK/Xi8PDQwSDQUXHbBXLlK4rMmhtkSAGFxK5iIGHiVsPDw+PhC09biZ6THKJPaui0SiGhob6ikxWQj/F2sPDA5LJJG5ubhAKhfDy5UtJr82gFwPtupYOhwMOhwMnJydYWFiQdTxWTPUrlolhnURKWyQIgiC0oJvvqdlsNlTkGh4e7iuARexZ5Xa7sbm5CZvNJul3B0Xkurm5QSKRwPDwcNP3VAvUErnkPGet5/P7/dje3obP58PExISi84vFsrGxsb4nsGhtkSAGExK5iIGFeSfV63UUCgWcnZ1hdXVVV3Gk3wKqG2LPqnA4DJfLBQAolUrPIrGlVqshnU7j6uoKy8vLePHiheTXxujCUMp6H0v2aSUYDGJ7exsej0fWGoC4Y8jEsuPjYywuLsq7eBHseWRpixaLhdYWCYIgiL6REupjMpmahvNGoHTaXg3PKqNFrru7OyQSCfA8j3A4rLk3p1oiF8/zitcEmZDHgoCUClR+vx87OzsQBKFvYYrWFgliMCGRixhIeJ5HvV5vFi+sW6j39I/JZEK1WlX1mKVSCYlEAg8PD209q4wqnKSes16vI5PJIJ/PIxAINKfP5J7LiEmuYrGIeDyOUqmEt2/fdi1uOhmSmkwmRCIR7O3tYX19XfLjaB2LF4tlSiawxM8hK7JobZEgCILoBzmJ1YMwySVH5BIEAVdXV6qE+hhVq5VKJVQqFezt7SEcDrf1PdWCfj252EpoOp2G2WyWVT+JmZiYgMfjwdHREQKBgKJrYRP1X758UXyM1uPR2iJBDBYkchEDRau4xdYSLRaLId1CNT25mGdVqVRCOByG2+1ueyM0KtGxFxzH4ejoCGdnZ/D7/fj48aPigkdvkev29haJRAIAEIvFcHd3h4ODA7x69arj73Qr6FwuF8bHx3F6eor5+XlJ19B6PJPJhGg0iv39fUXFXqtoRmuLBEEQhFKUJFY/p0muQqGARCKBkZERVTyr9Ba5xDWkxWLBu3fvdDs3oLxuEwQB+XweyWSyuRIaj8dxdnaGubk5RdeytLTUXFtU+jpOTExgdHQUhUJB8XWIobVFghgsSOQiBgJWWLGOYKvnlhEpQ/ns1wAAIABJREFUh2qdt1qtIpVK4ebmBuFwGF6vt2uhYPQIfCuNRgPZbBYnJyeYn5/Hhw8f+jLq1JP7+3skEgnU63VEIhG4XC4IggCbzYZ8Po+Liwv4fL62v9uraxkKhbC1tQWPxyNpzUEQhCfHczqdsNvtijy+OI57Mu5Pa4sEQRCEHJSIWwwjmnJipNRoLNTHbDZjdXVVsZdTK3rVauIaMhQKwefz4dOnT5qftxW5IpcgCLi8vEQymYTdbm+uhPI8j2AwiN3dXXg8HskeaGLE3qYbGxuKm6Z2ux2Xl5e4v7/v+31Ba4sEMViQyEUYSi9xi2E2mw2b5FIqcj08PCCVSqFYLMryrBqUSS6e53F8fIxsNovZ2dmuUdpy0XqSq1KpIJFIoFwuN5MeW4lEItjd3YXL5WorCPUSudja4v7+PtbW1hQ/HjU8vhi0tkgQBEFIQY3EaqPvMcPDwx2FJvEEdzQahcPhUPXcWotc/fieakG7Rl0nxFNza2trT6bLzWYzwuEw9vf38fr16yePS8rzarfb4XK5kM1m4ff7pT8QETzPw+/3Y39/H2/evFHFhJ7WFgliMCCRizAEZjzJhKtehVW3QkZLlMRT12o1ZDIZXF5eIhAIYGVlRXbRaKQnlyAIOD09RSaTwdTUFN6/f6/6VJBWIpecqTmr1YpAIIB4PI7V1dUn/y7Ff2JychL5fB7n5+eYnZ1VdM1KPb7aTXIBtLZIEARBdEYc6qNU3BoU2jUi2QQ3x3GPQn20OLcWtZrY9zQYDCryPdUCKXXbzc0N4vF416k59py53W7kcjnkcjnMzMzIPhfwo0m4tbUFr9erqN5pNBqw2+14eHjoy+NLDK0tEsRgQCIXoSuCIIDjOMniltHIWVes1+s4PDxELpfD0tISPnz4oKgrZNT4vyAIOD8/RyqVgsfjwbt37zS7QastcintePp8PuRyOVxdXT2Z9pJqshoKhbC9vQ23261o7B744fE1MTEhy+OL47iOa6O0tkgQBEGI+ZnELYa4XiqVSkgmk6hWq03fU61RU+TiOA6Hh4c4Pz/v2/dUC9h7ph3ipEc5U3ORSKRZP4nrTan11/DwMKLRKPb29vDmzRvF3qZqeHwxaG2RIAYDErkIXXhu4hZDiuAkNmRfXFzsuzDRe5JLEASUy2Xkcjl4vV5sbm4qFmv0huM4pNNpxUmPQ0NDiEaj+Pz5M5xO56PJqG4FnRiz2YxQKNRx7B6QVggvLy9ja2sLbrdb0tpitwhuWlskCIIgGJ1CfdTCqMRkk8mEer2Or1+/olQqIRQKwePx6HItak1yiX1PFxYWBtb3tN1rzNLCa7WaoqRHs9mM5eXlJ0FAcpIcnU4nHA6HIm9TJnKJPb5obZEgfg5I5CI0RW1xS+9Cqtskl1aG7Ho9PnGUNsdxCIVCkqeI1Dh3P4+z0Wjg8PBQFWHRZrNhcXERyWQSsVjs0b9JvUaPx4N8Pt927B6QVrCJ0xZ/++23nufuNskF0NoiQRDEr47W4hbwQ6jo1nTRimq1ikQigWKxiLW1Nfh8Pl3rw34bklr6nmqBuG5rTQtv53sqFa/Xi1wuh3w+j6mpKQDyRC5AHW9Tu92OyclJZLNZLC0tyXsQbaC1RYIwlsH9NCWeNVpMbjHBSc8ioN0k13MrTNpxfX2NeDzejNI+OTnR9SasVOTieR7ZbBbHx8eKhcV2RenMzAzy+Tyur6+bnUi519dp7B5obxLfDpfLhbGxMUnR2lL+FmhtkSAI4tdDaqiPGphMpo4ekVrw8PCAdDqNQqGApaUllMvlpjiiN0pELp7ncXZ2hkwmg+np6WdTQ7Ja+Pv37ygWi82kR7nvq3bPWTQaxc7ODiYnJ2GxWMDzvKzaTqm3aaPReCSmBQIBbG9vw+v10toiQTxzBv9TlXhWaLmWaES3UDzJxfM8Tk9PcXh4+KwKEzHMFNRkMj0yBTXC7F7O+0L83M/MzKj+3A8NDSEWi+HLly/Y3NxUNJHHxu7Z2qIYqSIX8MPji60tjoyMdPw5juO6/jtAa4sEQRC/EnqKWwxWm2lNa6hPLBaDIAjIZrOan7sdctcVme9pOp3W3PdUbWq1GvL5PCqVCqLRqOxApV5YLJZHQUCt4pMUXC4XxsfHZXmbtiZGMo8vlrbY72OktUWCMI7n9Q2dGFj08Nxi3UI9/aKY+MPSBr1eryZpg1ojNgWNRCJwOp2GXo/UwlAQhGbHU83nvt0k2cjICObn55FKpRCJRBQd1+v1Ip/PPxq7B+SJXKwjub+/j7W1tY5/R1KPSWuLBEEQPzdyE6vVhNVmWsFxHDKZTMdQHyOCegDpzUFBEHBxcYFkMgmXy/XsfE/Zcz82NoaFhQXFSdK9mJqaQi6Xw+XlJSwWi6LJJ9Yk9Hg8PZuAnXA4HHA6nchms/D7/YqOIYbWFgnCGEjkIvqCiVuNRqMpHGhVWJnNZk0LqVYEQUAul0OpVMLt7S3evn377G5Q/ZqCakWvdUVBEJDP55FKpXQtCufm5rC7u4ubmxvFx4hEItjZ2YHL5Wq+X+SO3k9OTiKfz3ddW5SzHkJriwRBED8fLDiGCQJGhPrISaGWg5RQHyMnY3qJXMz3NJFIwG63Y319XZZflJE0Gg0cHR3h9PQUCwsL+PjxI9LptGord51eNxYEtLy8rOhcUpuEvQgGg9ja2oLX6+27OTg0NIRKpYKjoyO8fv2a1hYJQidI5CIUIQhCs2vIBAutP7i1KqRaEXfdnE4nxsbGsLKyovl51aRcLiOZTKJcLvdtCqoFnUQucVE4MTGhe1E4NDSElZUVfP36VfH6psViQTAYRDwex8uXLwE89X2QQigUahqpthP45EyH0doiQRDEz4N4ev7PP//Ev/71L8M+19VuQLJQn+Pj44FOG+wmchUKBSQSCYyMjGBtbe3ZTFGLPWfn5uYePfdyzeA70e19arPZ4Pf7cXx8DLvdruj4rEl4fn6ueOqsNW2x378t9rdKa4sEoR8kchGyMELcYmg9ySVOGxwfH28KLP/v//0/zc6pNtVqFalUCjc3NwiHw/B6vZJupnp7crUTucRm+EYWhaOjo5iamsLx8bHiY/h8PuRyOVxcXMDn88kSpBhmsxnhcBh7e3ttO5JyjX5pbZEgCOJ5084awmQyyZ4WVhO1PLlaQ30+fPgw0L6nQ0NDT1Ylme+p2Wx+5Hs66IhtOTp5zqqVbt5rvXR6ehrZbBYPDw+Kz8GahG63u+MWQC/RzuFwwOFw4Pj4GIuLi4qvBfhfvUZriwShH4N79yAGCrG4tbW1hdevX+vuKaClyCXuur169arvVBW9qdVqSKVSKBQKWF5exosXL55Np+jm5gaJRALDw8N48eKF4u6dVKSIebOzszg8PMTt7S0cDoei80SjUezu7sLlcikSuQDA7XZ37EgqOSatLRIEQTw/uvmeWiwW1Ot1w0Sufj25nmuoj7jGuru7QzwehyAIiEajiusGvWG2HKlUqqcZvloiVy+GhoYwOzuLTCajuHYym80IhULNIKB21y3l2MFgsDlR309zkOO4ps8YpS0ShD4M/l2EMJR2k1t6rQ22ooW5abFYRCKR6Nl10+vmLpd6vY50Oo2LiwsEg0HEYjFF12nEJFepVMK3b9/QaDQGwgxfjCAImJiYwMHBATY2NhQVI1arFUtLS4jH43C5XIq/gITD4bYdSSXFH60tEgRBPB+khPpYLBZd/UpbMZlMqNfrsn+vNVjmOaUNAj9ei3q9jt3dXdTrdUQiEbhcLqMvSxKtthxSfE/1rINNJhNcLheSySSi0aiiY3g8HuTzeeRyOczMzDz5dyk1lMlkaqYtrq+vK378bJKL0hYJQj9I5CLa0m0tkXUN9cZsNvc1vizm9vYW8XgcQ0NDPbtuTNTTu7PYraDgOA6Hh4c4Pz/H0tJSW0PWQaVcLuP+/h4HBweIRqMDY4Yvhud5WCwWOBwOHB4eIhgMKjrO1NQU8vl8XxNh3TqSSgokWlskCIIYbOQkVpvNZkNqMvH5q9Wq5J8XTw+53W5VgmX0bkSWy2UkEgmUSiXEYrGB8z3tBvM9FdtySEEtTy6p53K5XLi4uECxWFQsHobDYezs7MDtdj8RUKU2Cp1OJ+x2O05OTrCwsKDoOjiOa6Y9UtoiQegDiVzEIwRBAM/zqNfrHT23jBS5SqVSX8e4v79HPB6XNT00PDyse0Q1m6xqLdraJd48F3GrWq0imUzi7u4OVqsVGxsbA7syx4ziFxcXsbOzA5/Pp8hbg4mof//9d1/rC+06kv1M3tHaIkEQxOChJLHaqJqMIXXKvnV6aGNjo/nFvx861UtaIK5jlpeXcX9//2wErmKxiHg8DqvVqsiWQ08hked5mM3mZhDQxsaGoml4i8WC5eXlZpNQjJxpePHaopIwJLGHKvteRWuLBKEtJHIRAP4nbnEcB57nuxrKGylyKR3JL5VKSCQSeHh4QCQSkTU9ZMR6Zuv6YLfEG7XOpxUPDw9IpVIoFotYXl7G6uoq/vzzz4G+sbOOJUvY2d/fx5s3bxRds81mg8PhQD6fx9zcnOJrikQi2N7exuTkZN9db1pbJAiCGBz6CfUxel2xl/F8p1AftWCNSC1rioeHB6TTaVxfXzfrGABIJBKanVMtxJsLKysrin1P1RK5pDToGo0GbDYbRkdHMTMzg3Q6jXA4rOh8Xq8X+Xwe+XweU1NTj84htY5ma4t7e3uK1hZbg4JobZEgtIdErl+cduJWr86hUaPxSsSmcrmMZDKJcrmMcDgMt9st+2bCbkZ6wkQuPQ1Z1fbkYn5hl5eXCAQCWFlZaT73RnqcSXmc4qSqiYkJuN1uZLNZLC0tKTrnyMgIbm9vUSgU4Ha7FR2DrS0eHBxgdXW174Ke1hYJgiCMRY3EaqPXFbtNcukR6sPSJbWgtY5R6ntqBPf390gkEuA4DuFwuG+/MEEQdF1XZOdaWFjAzs5OX7YPkUgEOzs7cLlczRVBub6mTqcTExMTOD09xfz8vKzzt0vDprVFgtAWErl+UQRBgCAIqNfrksUthsViQblc1uEqHyNnkqtarSKVSuH29hahUAher1dxYaJlAdWJoaEhnJ2dIZvNPjtD1la/sA8fPjwpjAbVyJ/R2hVeWlrC9vY2vF6voiKd53kEAgEkEglsbGwoFirFa4tqTPLR2iJBEIT+qCFuMSwWiyxPLLVpN8klNdRHDbRoREqpYwYVcXM3Eokobqy1wr4r6IG4BhsaGkIsFsP3798VBwFZLBYEg0HE43G8fPkSgLLwnuXl5WYQkJxpxHYiF60tEoS2kMj1i9GPuMUwajReisjVuhr34sWLvm/Keq4rCoLQNCofGxtTxZBVCmqkKzYaDWSzWRwfH2NxcXGg/cJ6vSdaRa7h4eFmws6bN29kv6cajQZGR0exsLCAZDKJWCym6LqBH0aqW1tbqqx70NoiQRCEfqgpbjEGyZNLTqiPWqjpmyr2PR30OqYV1ty9ublBOBzuq7nbDr09ucTP+/j4OHw+X19BQD6fD7lcDhcXF/D5fIpELpPJhEgkgv39ffz222+Snw+O49qei9YWCUI7SOT6hWCG8qwYkCtuMYwqqLqJTbVaDel0GldXVwgGg49W4/pFD+N5QRBweXmJZDIJu90Op9OJUCiki8DVL2K/sNnZWXz48EHSpNIg38zb+Xs4HA44HI6miKfkeLOzs8jn87i+vlacKmmxWDA/P4+joyNFv98KrS0SBEFoixbiFmMQPLlqtRp2dnbQaDRUWY2Tgxo1Gs/zzSadFr6nWlKr1ZBKpVAoFFRr7rZDDZGLNdp70a4G6zcICACi0Sh2d3fhcrkUp6a7XC6MjY3JXlvs9PdOa4sEoQ0kcv0CqCVuMQZJ5KrX68hkMsjn8wgEAohEIqp33bSe5BJ7VqytrWFsbAw7Ozuqe2SpjSAIODs7QzqdxtTUFN6/fz/wK2+CIOD6+hoTExNdC9hOJrYsYcfr9cqapGIdQ2b8+uXLF2xubiouou12O0wm0xMjVaXQ2iJBEIT6SEms7hcjPblYqE+5XMbq6qri5k0/9GMpIfY9nZmZ0dT3VG1a61+t/cLU8OTieR43Nzc9RdB2NZgaQUBWqxVLS0uIx+MYGxtT3EgOhULY2tqCx+PpOyGU1hYJQhuexyc5oQi1xS2GUSKX+No5jsPR0RHOzs40HynXapKLxTlbLJYnnhV6TI+JkbOuKAgCcrkcUqkU3G73s/ELY2Iix3Fwu91YXl7u+LOdRC42qi43YUc8Fj8yMoL5+XmkUilEIhFFj4U9hkwm88hIVSmsyDo+PkYgEBjoKTuCIIhBR05idb8YUZNVKhUkk0mUSiWEw2Hc398bInAByuol1qTLZDLwer3PoknH4DgODw8P+Ouvv3RdqexnkkssJvI8j1gs1lXo6lSDqREENDU1hXw+j/v7e8VJk+JaUM7aYidobZEg1IdErp8QVlix6SO1xC2GHAN4tREEAZlMBsfHx1hYWNBlpFztSa7b29tm7HQsFmvrWaGGR5baiFcqHQ4HNjY2+u5g6cHNzQ3i8XjTANdisWBrawtTU1MdR967xZG7XC6Mj4/j7OwMc3Nzkq6h9Xhzc3PY3d3Fzc0NnE6n7MfE4rWDwSAODg7w6tUr2cdoZXh4GKlUCtPT07S2SBAEoQAlidX9oqdvaCffJyO/lMsxnme+p8lkEm63G2/fvn0WTTrg8UolAN1XKpUYzwuCgPPzc6TT6WaIUqVSwdevX7GxsdHx+sUJ1630GwTE/OL++usvzM7Oyv59xuTkJPL5fM9asNFoSHreaG2RINSFRK6fCK3FLYYRxQzzfSqVShAEQbLvkxqoNVUlJ85Z70muXhQKheZ4N1upHHTu7+8Rj8fB8/wjA9x6vY5YLIa9vb2OST08z3d9f7FRdbfbLVnoE//dsLXFXoVeJ1hSj8/nQz6fV2VtkU2b0doiQRCEPNQI9VGKHudgoT7X19cIhUKa+T4pQUq99FybdED7lcq//vpLd88wOZNcgiDg4uICyWQSLperGaLE/k5mZmaQTqcRDofb/n6j0ejqYdVPEBAA2Gy2pq+Wx+OR/fuMUCjUTFvs9H5qNBqS6ilaWyQIdSGR6yeA5/mmoSmgnbhlBOKb+/T0NCYmJrC0tKTrh7/JZOprFaBcLiORSKBarSIcDkuKczZikqvd+cRTUC9fvtQ0BlwtxM93JBJpu0LRa+S9V+qOyWRCOBzG/v4+1tbWFP29jY6O9iz0OtFoNJqdvmg0ip2dHUxOTvYlTDHhjNIWCYIgpGGkuKUHtVoNmUwGl5eXCAQCHUN9hoaGuk5Aa0kvT66rqyskEoln1aQDBm+lUqonF3u+x8fHsb6+3ta/dGFhATs7O7i9vW27zdDrveRwOOB0OhUFATGsVivq9ToKhYKkurwdZrO5Zy3YKVmxHbS2SBDqQSLXM0YQBHAcZ4i4pXVB0+nmfn19DY7jdB3llTMKL6ZarSKZTOLu7g7hcBgej0fy66O3yNV6XXd3d4jH4xAEQbcY8H6R+nyz5zUQCGBraws+n+9J0Svlve12u5HP55HL5TAzM6PomnsVep3gOK55zRaLpbm2+PLlS0XXwY7JRC5KWyQIgugO8z3d39/HzMyMoY0BtWuyer2Ow8ND5HI5LC0t4cOHD12PzVYmjRC5Ok1yiX1Pn0uTDvjfSmUqlXo0BWU0vdYV2fNttVrx6tWrrquEQ0NDiMVi+P79e9uJeimrkYFAQFEQkPgc4XC4OdWvdDvE7Xbj4uIC5+fnbdcfWW0lFVpbJAh1IJHrGcLErePjY5hMJkxNTeleWDFfLrU/gFtNzVv9EvT0nhCfU87qIBvrLxaLWF5exurqquzXx6h1RZaUVKvVEIlEdI0BV4o4PjsUCkl+vllSz97e3pORd6lfFsLhcHNUXcnfAltb/PbtGzY3NyV/QWidNPP5fMjlcri4uIDP55N9HcCPLzWsS0xpiwRBEO1pDfVhk1xGTl2oVZMpDfUxm82S17LUprUReXt7i3g83ry/KjUX1xtBEJpTUHa7HW/evBmolcpO64qsKQp09pltx/j4OLxeLw4PDxEMBp/8e6/3nclkQjQalR0ExGg0GhgbG8PCwgKSySRisZis3xcjXltsFSTlily0tkgQ6kAi1zOidXJLEARUKhVDCiuW5qOWyCXe33c6nR39EowwvZcqrNVqNaTTaVxdXSEYDHYc65eC3pNcHMfh7OwMJycnzSmoQYfjOKTTaVxcXCiOz3Y4HHA4HDg5OcHCwkLz/0sVucxmM5aXl7G/v4/Xr1+3/ZleHcmxsTFMT08jk8l0TXwU065oikaj2N3dhcvlUvRFQ3xMVmTR2iJBEMQPOiVW22w21Go1Q6+t35qs0Wggm83i5OQE8/Pzsk3NTSaTYYFETOQS+55GIhFFoS5GcX19jXg8jpGRkYFeqRTXAmo0Rf1+P7a3t+Hz+RRN2jmdTkxMTOD09BTz8/Oyfpc1C2dnZ5HP53F9fa04IVS8tvj69esnafByp8SGh4fBcRytLRJEH5DI9QzotJZotVpRKpUMuSa1IqvFnauJiYmO+/sMI0SuXlNVHMchk8k0x/ojkUjfnRe9JrkeHh6QTCabE0CDZCbbiUajgaOjI5yenmJxcbHnGkUvgsEgtra24PF4mu89OWsfXq+3q/l7L38vAFhcXMT29jbu7u4kdZ3bHdNqtSIQCCAej2N1dVXStYtpLcRobZEgCKJ3qI/Vah0YkUsuLNQnm81idnYWv//+u6K1LTbJZQQcx+H09BS5XE6y7+mgwHxPTSYTVldXn8VKZbVaRSKRQKlU6rspyibq9/f3sbGxoaj+XF5ebtZwcibfxJNpKysr+PLlCzY3NxUb+rvdbuRyuScWFkpELuCHcExriwShHBK5BphenltGFlZqiFyFQgGJREJW58qodcV252w0Gjg8PJQ91i8FrSe5WqfOxsfHMTw8PNACl7gYn5ubUxSfzUyCxZhMJkQiEezv7+O3335T5G0SiUQ6mr93i8JmsLWKTv4UrXQqmqamppDL5XB5eQmv1yv5+oHH64oMWlskCOJXRWpitZENR4bcBmBrqI9ScYthxCRXpVJBMpnE9fU1nE7nkwkavZCTOsgQpz8/l6kznufx/ft3FItFhEIh+Hw+VZ5vu92OyclJZLNZ+P1+2b+vNAhI/HMjIyOYn59HKpVCJBKRfQ2MSCSC7e1tTE5ONtcWla4R09oiQfQHiVwDiFRDeZvNhoeHB70vD0B/IpfYDFRu52oQJrl4nkc2m8Xx8bGisX6p59RC5Oo0dZbNZnVPc5SKIAg4PT1FJpNRpRgHnhrtT05OIp/PN41D5YpcFosFgUCgrfm7VDPeXv4UYrql9cRiMezu7sLpdMoSpjiOe9IFpbVFgiB+NeQmVj+nSa7WUJ93796pMiWiZwNS7HsaCoUwNTWFYrFoyP1J7jmlpD8PGvV6HZlMBuVyGaFQqC8rjk6wICCv16tocpwFAXUyf29Ha807NzeH3d1d3NzcKBYdzWYzQqEQDg4O8OrVKwwNDT0KCpILrS0ShHJI5BogmLjVaDSanaFBLayUiFxqmIEa6cnF8zxOTk5wdHSEmZkZVcSWTrBpIrVoXfFTc+pMK8QhBB6PR7VivNPflNg4VElK1dTUFPL5/JMpKinrigyp/hTdrs9qtWJpaQmJRAIvXryQfP2dpsNobZEgiF8BpYnVz0Hk6hXq0y961GadfE+vrq4MCeoB/jd13+t9wlb87u/vZadtG4U4hMDv92N8fFyygCSX4eFhRKNR7O/vY319XdExxEFAStIo2XeTr1+/YmNjQ3Hz2uPxNC0spqenFa8rMmhtkSCUQSLXACAIQrNryG6WUr5gazXtIwWz2YxqtSrpZ+/u7pBIJNBoNPoeyzZiJH5oaAjlchmfPn3C1NQU3r9/r/nqlloilxorfnojCAIuLy+RTCbhcDg6hhCojbgD121Sqhti83dW1MgRuYaHh7GysoL9/X28efOm6+dAtwKZCW5XV1eS/TLarSuKr4vWFgmC+BlRKm4xBkXkarcyKTXUp1+0nOQST6AHAoEnvqdyE7DVpJe1RGva9suXLwde3Oq0rZDNZvs+drfnipnIHx8fK3qOxDVcpyAgRqdG4ejoKGZmZpBOpxEOh2VfAyMcDjctLPoVuWhtkSCUQSKXgSgVtwYBKZNc4uSVcDisyli22WzWbUVT3Pms1+v4P//n/+jWRelXwJTrt6H25JhSmE/b6OioZglD3bquHo8HuVwOtVpN0d+i1WqF3+9HIpHAysoKAHkiFwBMTEw0/SmWlpZkXwPw4/WMRqP4/PkznE6npAKrWyFGa4sEQfxs9CtuMYwUWRitk1RyQ33UOL/atZl4kqjbBLpeQT3t6CRy1et1pNNpXF5eIhAIaLLipzbiulHrbYVOLC8v459//ulriiqXy3UMAmJ0q8sWFhaws7OD29tbOBwORddhsViayduCIPT9PNLaIkHIh0QuA1BT3GKrRHpP51gslo4TVeVyGclkEuVyue/klVb0GIlnk0SJRAJOpxPr6+v4/PmzrmPCSo3nBUHA+fk50um0qit+WsMShsxms+EJQ5FIBP/3//5fxWah09PTyOfzKBQKcLvdkj25xAQCAWxvb8Pr9WJ8fPzRv0l9X9hsNvj9fsTjcUlri726jbS2SBDEz4Ba4tYgIW48ikN9Xr9+/eQeogVqTnI1Gg1ks1mcnJxI8j1l9yYjaG0QchyHw8NDnJ+fY2lpqe/0Zz0Q141er1fzbYVujUaTyYRgMNgUh5T8XbIgIJfL1bGG6/a9ia0tfvv2DZubm4pfP5a8fXt7q4pYSGuLBCEPErl0RIvJLTYmr2WHrh3tJrmq1SqSySTu7u4QCoXg9XpVLxy1jqlmnc/x8fFHnU+910KHh4dleZ6JVxJcLhc2NzdlexIYsfoqThiKRqOKu2ZqwgqIeDxZ0jKkAAAgAElEQVT+xEReCkNDQ4jFYvj8+TM2NjYUidDMn2Jvb+9JrLaUtEYGE9ykrC1Kuc7h4eFmkUVriwRBPCe0FLeUpPKqicViQblcxj///GNIs0iNBqTY93R2dlbyJNEgTHKJhbmFhYVn43vab92oBQ6HAyaTSZaJvBiLxYJgMNi1hutV74yNjWF6ehqZTAbLy8uyr4HBmqaNRkOVwCRaWyQI6ZDIpQNariUOgsjV6jmwurqqWVdUK0+u6+trJBIJWK1WvHr1SpfOZzfkTHJ1Eubknk9PeJ7Hly9fBjZhyGw2g+f5JybyUrHZbFhYWEAqlcL4+LiiSUuHwwGXy4Xj42MsLi42/78cfwex4CZlbbHX+4B9KaS1RYIgngt6TG6xWkwP/8hWbm9vcXBwgLu7O7x7986QZlE/k1ziBGUlvqdGilwAcHJygrOzM8zOzj4L31NAnbpRK3iex8TEBLLZLDwej6KpJZ/Ph1wu17GGk9LUW1xcxPb2Nu7u7hQFZQE/vivZbDbE43G8evVK0THE0NoiQUiHRC4NEQQBPM+D4zjwPK+J55ZRhqcmkwn1eh37+/tPkm60RO11xZubGyQSiabht9IbmdpIKdoGTZiTApv2q1QqWFlZGdiEIbGnldhEXg6zs7P4/PkzBEFQHLbA1hY9Hk9zRVDuZJjNZsPi4uIjn7B+oLVFgiCeA3ITq/vBCJGLTUI3Gg2EQiF8//7dsGloJVP2aiUoG+GJxoS5YrGI8fFxXQKJxOdWys3NDQ4ODmCxWAa2buR5Hmazuelp1ctEvhMsCMjpdD55baTUUWxt8fv379jY2FD8/c1sNmNoaKinT5hUaG2RIKRBIpcG6CFuMYwQuer1OjKZDMrlMiYmJp4k3WiJWiIXS3zkeR7hcLivxEct6DbJdXt7i3g8PnDCXDfE0d/Ly8soFouKJqT0pF9xiE1RbW1tweVyKboGk8nUXFt88+YNhoaGFCX1zMzMPPIJa0XuFwRaWyQIYlAxItRHz1qMhfo8PDwMzCS0nCl7tRMf9fTkahXmJicnsbS0NPD3wbu7O8TjcQiCgFgsJlsMVcvOQspx2Nqv1+uVZCLfCXEQUKsvqdRm4fj4OLxeLw4PDxEMBmVfA3u80Wi0mbbY73uF1hYJQhokcqlIO3FLa0NTq9WqW9qg2FDT7/djfHwc8/Pzupyb0a+56SAWh+1ol654f3+PRCIBjuMQiURUFeaUGt33Qhz9HQwGEY1GIQgC0um06ufSAiYOXV9fK3qvjI6OYnx8HBcXF5ienlZ0DU6nEw6Ho+n1ocTji3UkmU9Yq0gmVzijtUWCIAYNIxOr9RC5WkN93G73wHz2SpnkEgShaYqv5pqcHuuKrYFETJjb3d01xM9UKuKU80gkorjhptQEXglib7tIJILd3V3F4lBrEBBDTh3l9/uxvb0Nn88n2+eOnYf5hB0cHCjyem2F1hYJojckcqmAIAgQBAH1el03cYthtVpxd3en6Tk6Jd0cHx/rbrTaTvyRQqVSQTKZRKlUUlwc6nmTFyf2lMtlJBIJVCoVRCKRtpM4g0aj0cDR0RFOT0+fRH8PckEIPL4+No315csXbG5uKvLaGBsbw83NTV9x1MFgsLm2qGSSC/ifT1gymUQsFnv0bxzHyS4gaW2RIIhBwEhxi6GlyFWtVpFKpXBzc4NwONw11EfPOkVMr0mu6+trxONx2Gw21dfktH68hUIB8XgcY2Nj+O233x7d77RqEPZLtVpFIpFo1rz9ppwLgqDb35T4e4XVasXS0hLi8ThWV1dlH6s1CIjVTnJEruHhYcRiMezv7+PNmzeyngdxvcZ8wi4uLuDz+WQ/llZobZEgukMiVx8wcev6+hrVarXpL6RngaFlYcXzPI6Pj5HNZjE3N/fEUJOZzw9CGksnxMVhKBSCz+dT9PqwTqFehqJDQ0Oo1+v49u2bpmmVatPrPQMYV4RLhQnVjJGREczNzSGVSiESiSg6HovEVhpHbTKZEIlEsL+/j6mpKcXvw9nZWVxcXDyZTFMqnNHaIkEQRsHErdPTU9jtdoyOjhq2umO1WlEqlVQ9Jgv1ub6+RigUwosXL7reO5mdgxGfxZ0akGLf0xcvXjwLewVGsVhEIpGA2WzGy5cv207xDJrIVavVkEwmUSwW+6p5W2mti7SktXk+NTUlOSW6HeIgoGg0CgCy0w7tdjsmJyeRzWaxtLQk+fc4jntUrzGfMJfLRWuLBKExJHIphOf55uRWtVrFxcWFIR5DWohcPM/j9PQUh4eHmJmZ6RjjPMgiV6sHVK/isBdsTVIPkatWqyGbzaJQKODly5eaplUy+i3UxOlI09PTXaO/jRS55PhBiJmfn8fOzg5ubm5kr4k2Gg2Mj4/3HUftcrkwNjbW0VdLCp0m0+r1uiKRi9YWCYLQm9bJrWKxiOHhYUMnStWsxcT1SyAQkBzqw2qyQWg4DLrvaTeYfxXwQ5ToNoFtlMjV+n5gXrkXFxey3jNSUatuU1KDiYOApKREt4MFARWLRbhcLjQaDdnfXQKBALa2tuD1eiVPIjYajUd/j1arFYFAQPFkWiu0tkgQnSGRSyZicQv48eE7MjKimy9WK2oWVoIg4OzsDJlMBj6fr2daDCuojKDTDZfd6PP5PAKBAKLRqCof+np4PoiLlKmpKZjNZsU+TnqhNB3JqBsx88zr9r5uN7HHPK2+ffsmexqLiaMsjvr+/l62rwMjFArh06dPin01gB+TafPz80gmk82uZj/df1pbJAhCD5jvab1ef7SWaGQNxrBYLH3XYuL6ZWlpSXaoj9rp00oQe0CFw+GB9T1tR6lUQjweR71el+xfZfQkF8dxODo6wtnZGfx+Pz58+KDJRI9aIpeU7wztGsrd7BakwBp8X79+xcbGhqKmNVtb3Nvbw8bGhqTno3WSC/gxmZbL5XB5eanKcAStLRJEe0jkkgj7csyMNcVriUYWWEo9qsS0ChVv376V9EFplMjFpqrE3ZxWU3yxB5Sa59SCdkXK/f09Dg8PNTlfO+QWasyENZlMwuFwyEpHMmKSi00nZjIZmM3mZlJhp59t994ZGxtTNI3FiilWZLECSena4uTkJM7OzjA/P6/4eZybm3vU1VS6rsigtUWCILSiV2K1zWZTfVVQLv00HMU1QKuHpRyMbDxWKhVUKhV8/fpVFQ8oPalUKkgkEk1DfznXbpTIJQgCDg8PcXx8/MgrV8vz9VNT1+t1pNNpnJycYHZ2tmsN1akGm52d7TsIaHZ2Ful0WrH9iMPhgMvlQjabhd/v7/nznabkY7EYdnd34XQ6aW2RIDSC/hJ6wPM8arUaHh4e0Gg0mh8m4i+XUlJlBhFBEJDP5/HHH3+gUChgY2MDsVhMcifAqIJK3K1sNBrIZDL4888/YTKZ8OHDBywuLqr+Ia/FJJf42oeHhx9du9HdwW4UCgX8/fffOD8/x9raGlZXV2XFf+v5uARBwPn5Of744w+Uy2W8e/cOExMTOD097fg73cIUFhYWcH19LSvsodFoNI83MTEBj8eDo6MjeQ9EhNlshs1mw/n5ueJjMMEtHo+j0WgoXlcUH4+tLQ7q+5YgiOcFW0us1Wqo1WrNBkmruG+z2Qyf5DKbzbJrBFYD/PHHH80awO/3K65fjKjJqtUq/vOf/2B3dxcWiwVv3759NgLXw8ND89qnp6fx/v172deud63GfE/v7+/BcRx+//13BAIBza00lHpycRyHZDKJv/76C2NjY/jXv/6FQqHQVZTuVIO11i1KmJ+fx+3tLSqViuLnLBAIIJfLoVwu9/zZTt5fzFA/kUgouoZW2HeUSqVCNRhB/Bea5OpAt8mtThidaiP1S6ogCLi6ukIikcDExITiGGez2WzYJFe9Xkc+n0c2m8Xs7GxXDyi1zqmWkMnzPE5OTnB0dNTR80ycrjgoMANZk8mE1dVVxSt3evydtMZ9b25uwmazNU3gd3Z24PF42opz3USu1nF1qV9GxD+3tLTUjKNWkjDVaDSwtLSE/f19uN1uxZ54o6OjTUP9oaGhvruJtLZIEIQayE2sHhkZQbVa1fkqlSMOaJmdncWHDx9UqV/0XFes1WpIpVIoFApN39N//vlHN+/SVljNJOWerKZnq14iV+vGxcTEBEKhkObnFZ9fznPE8zyy2Wxz0oxNJ3Ich2g02kwqbHfMbq8jq1vS6TTC4bDsx8GsJ/755x/Fr7nJZEI0GsXe3l7XrQDgh8jXqQncr6F+u+va29vDy5cvZTWeCeJnhUSuFgRBAMdxzUJBalqikak2bExeSpFUKBSQSCQwMjKCtbW1vr6MWq1WVCoVxb+vBEEQ8PDwgJ2dHczNzfX0DVML9gW+H+R4nqmxhqoW9/f3iMfjqhnIai1ysajy0dHRJ3HfwI9CIBwOY39/H2tra0+upVehzKax5KbsMJT4OojhOA5WqxWhUAj7+/t4/fp1X2uLu7u7GB4extTUlKJjiKG1RYIglCJX3GIMwiQX0FtoEYf69ApoUYLFYtH8eWg1OI/FYs3XR0tbh16wSZZu926O45DJZJDL5VTzbNW6VhM37FwuFzY3N2GxWFAoFDQ7Z6frkPJc9QquEgQBdrsdTqcTx8fHWFxcbHuMbq8jCwK6vb3tGgrQibGxMVitVpyenjZ9SeXidDpht9txcnKChYWFjj/XbQBBDUP91uMVi0U8PDzAarXS2iLxy0Mi139RKm4xWJFlpMjVTbAqFouIx+OwWCx9TeGI0bNryNbO0uk0gB/77HqasptMJsWTVWwtNJlMwu12S/I803sEvt35yuUyEokEqtUqIpHIwBvI3t7eIh6PY3h4uON7nD1Gt9uNXC6HXC6HmZmZRz8jpRu8tLQkO2VHjN1uh8vl6ljkdYONv3s8HuTz+baPQSqsq/n333+rUhBR2iJBEEpoF+oj9fNjUJpCrBZrnaIQN7i8Xq9mzTmLxYL7+3vVjws89T1tZ3BupHVHN0uJRqOBo6MjnJ6e9uV51gmt3nuFQgHxeBzj4+OPNi6MmPLv5cklN4QoEAhge3sbXq/3ySZJrxqMrS3+5z//kR0ExLBYLLi9vcXd3R3sdrvs3weA5eVlbG9vw+PxdNyG6bVlY7PZ4Pf7EY/H8eLFC0XX0QpbW6S0ReJXh0Qu/PgQYmt3csUtBhO51BCP5NLN8JR98WdfZpV+mLdDD/8HQRBwcXGBZDLZ7GIdHR3p/sGtpEMpNme32+2yzNn1SHPsRLVaRSqVwu3tLUKhELxe70BGUTPu7++RSCTAcRwikYjkSbNwOIydnR243e5HxZgUkYtNY3Ubue8FK/I8Ho+siUpxWk+nxyCH0dFRWCwWnJycKEotaoXWFgmCkIogCKjVaorErXbHMvJLHauJ2H1e/MVfaoOrH7RoPDYaDWSz2ebESjeBiFlnGEG7RqR4ZW5ubk4Tc3YtBNabmxvE43GYzWa8fPnSkO8VrXTy5FIaQmQymRCJRLC/v4/ffvvt0bGl1GDj4+OYmprC4eEhgsGgosfz4sWLvoOAOj0GhhQrmenpaVXWFtnnH6UtEsQPSOTC/z4Y+imObDabYZ4Q7USuu7s7JBIJ1VbM2qGlyNXNN8yImGy5olO/a6FGTHJxHIf9/X1VfCq6odYXEXEiUiQSgdvtlvX7FosFwWAQ8XgcL1++bP5/qb4eDocDDoej67h6t9eQ+Trs7+9jfX1d8nMi7qhaLBYsLy/j4OAAr169kvT7na6lVCrh5uZGlc8KWlskCEIK7HOv3xqM1UFKPQrVgF2DuDnndDplNbj6Qc2aTOwbJlUgGpRJLq3XQsWoWauJ6/ZoNKpoFU8r2tVtzBpCaZ3rcrkwOjqK8/NzzM7ONv+/1OTDxcXFpr+pEiFwYmICXq8XR0dHCAQCsn8f+PEYxsbGcHp6ivn5+Sf/LkXkYpNp/a4tsnNR2iJB/IBELqgzNTMyMqK7PxVD7I1VKpWQSCRQq9UQDoc1XTHTSuTqdeM0QuSSOsnFOnD9mrPrOcnFcRzOzs5wdXWFWCymik9FL/o5/sPDA1KpFIrFIkKhEHw+n+Lj+Xw+5HI5XF5ewuv1AoAs49xgMIitra2O4+q9BDOn09lMe2xXIEnB6/Uil8shn88r9tVik57fvn3DxsZG391u9oX19PQUfr+fRuYJguiIGvc7Nk1vtMh1eXmJeDzeV6iPUtSoyfoRiIyc5GKm5mdnZ0in0/B6vT1X5tRADZGLWUM8PDxoXrcrRSxySbGGkEooFML29vajEB1xInU3Wifq5Yg57DXz+/19BQGxx8DqwFYxW2oomM1mw+LiIhKJBFZWVhRdR71ebzYV2d8DrS0SvzIkcqmEzWZDsVg05NyssPr3v/+NcrmMcDisS4RzPz5V7RCPaHe7cZpMJt1NZtkNoxNqd+D0mOQS+1S4XC4sLCwoFlrkoPRx1et1pNNpXF5eIhgMYmVlRZUbdzQaxe7uLlwuVzMGXmqx1GtcXYpgtry83LFAkvMYdnZ24HK5FBf1Y2NjmJmZUZxa1Mrw8DASiQR8Ph+tLRIE0RE1PseNNp8vFAo4OTmByWTC+vq6IZ95/YhcYt9TKZ5K7TDKeJ4FEn358gUej6eZpqwH/dRq1WoVyWQSd3d3zbpd6t+C3qIFS5zf3d2VbQ0hpvW5MpvNCIVCODg4wOvXr5vnklqD2e12TE5O4vj4GH6/X/b19BsEBHQPM5LzWGZmZpDP51EoFGRvJgCPRS52XbS2SPzKkMiF511gVatVZLNZXF1dYW1tTXX/JD24u7tDPB6HIAiSBCKz2YxSqaTT1f2A3Sxa0WpyTstJrnZrCFdXV7i5udHkfK3IXVdsNBo4PDzE2dlZR8NbOeduxWq1wu/3NztocooSAJicnEQ+n8fZ2Rnm5uaeXHsvkUsslLVLe2y9/nb/brFYEAgEnqxeSkH8nCwsLPSVWiSGPY+0tkgQhNaMjIwYYhkhDvXx+/09Q4C0RIk/FAvGSaVSTd9TpQKREVP2zNaiXq8jHA4rDmFRihKRq1arIZVK4fr6GsvLy1hdXR3our1SqSCdTuPu7g5ra2uqN9E9Hs+jaXS5NVggEGgGAUn92xM/3ywIKJvNKhLKgB9hRvl8/snqZeu5el3TysoKPn/+jI2NDdlri60iF60tEr86JHJBPZFLzwJLvLK1uLiIer0On8+n2/nFKPVYKpVKiMfjzeJEqkA0COuKlUoFyWQSpVIJ4XAYbrdb1SJFKy+s09NTZDKZJ2sIenqAyYmiZqax8/PzmpjGMqanp5HL5XB9fQ2e52UXF2zk3uPxPPqCIHX1kQll7QokMd2ONzU1hXw+/2j1UgriY4rXFpWmFjFYwUVpiwRBdEOtGuzu7k6Fq5GGONQnFovB4XCgWCzi5OREt2voB7Hvqd1ux5s3b/r2DdNzyv76+hqJRAJWqxWvXr3C8fGxIY0UObVTvV5HJpPBxcUFAoEAYrGYove+XrWa+HuGz+eD3W7XbEskEolgZ2cHk5OTskWu4eFhRKNR7O3tSQoCameiz6wn5AhlrYTD4Serl3Kx2WxYWFhAMpmUHQTUKnIBtLZI/NqQyKUSbM1Ja2q1GtLpNK6urporW4Ig4OjoSPNzt4NNHMkRH8rlMpLJpOLVSiPMTdnjVNMPSi/kRjsbTTcxTg3avV7si8qXL1/g9XplF/ts5H5/fx+vX79unkOOv1c7b4pWevk7sNVLp9MpueCv1+uPjjk2Nobp6Wmk02mEQiFJx+h0XIvFQmmLBEF0RS2R6/LyUoWr6Q6zJmg0Gk9WtrolXetJr0ZSv8E4ndBjyl7sByVODGf3Gb0ZGhrqWfurOY2uB2Ixjn3PuLi40HTa32KxYGlpCfF4XLbIBfzwN7Xb7V2DgBjt6jImlMkNAhLTWgcqZXZ2FhcXF7i+vpa1HVKv19vW9rS2SPyqkMiF/5kkq9Ed0SrCmt108vk8AoEAIpFI8yYwCJHZUr7IM/+B29tbhMNhxauVRpibsq6nWFx8DuKWnGhnoye5jBbjRkZGMDc3h/Pzc0WreuKR++npaQDy/BjE3hSvXr3qGEfd7W/NarViaWkJiUQCL168kHRejuOeCGKLi4t9ry22mqDWajVYrVbNUq4IgnieqHEv1XpdUYo1wSCIXN0aj8ViEYlEoqfvqVK09OS6v79HPB5vKy4C+ob1iOn23UHPaXQ16CbGiVOdlSIIQtc6c2pqCrlcDrVaTdG5mL9pr0Zlp+ajGkFAHo8H+XweuVwOHo9HUb0jbrpubm5Kfs/U6/W25vm0tkj8qtC3jf+ihsjFBB81v5hzHIfDw0Ocn5/D7/fj48ePHT+gtBLYusEec7cbCvMfKBQKCIVCffsP6LmuyHEcMpkMTk9PYbPZ8O7du2dxg5Ab7azn+6b1fSpem5AixvV77m7Mz8/j8PBQ8Zcl8ci91WqVNckFtBfKxDQajZ5FE1tbvLq6kjQl2W46jBVZ379/x8bGhqL3vFjkYo2EUqlEa4sEQaiOVgKTnMlzI6bMW2nXeJTre6oULWozljxYrVab1hDtGCSRS5xQOTMzo/o0utqIfVo7iXHtVvyU0uk4rO749OmT5IRFMVL9TbvVZWoEAYXDYezs7GB0dFTx6z4yMtJcW4xGo5J+p926IoPWFolfkcH91H2GMPN5NUQucfLdwsJCzw4Q66DpfSPtluYjTsPrx3+gFT1Ertbnf319Hel0euAFrpubGyQSCVWinbVCLHLJFeO0ZmhoCC6XC8fHx5ibm5P9ercawMsVuYCnQpmYXpNc7DFEo1F8/vwZTqez52dCpxXI8fFx+Hw+ZDIZLC8vy3oMwNOCi9YWCYJohxp1gRLT9W6Ik+9CodCzCfURNx7v7++bpuyRSAQul0vTc6s5ySX3+Vc77Vsq4nVFo6fR5SLHGkKtJnqvY9hsNlgsFqTTacnijpjJyUnkcrmu/qbd6jImlO3t7bVNzJaCxWLB8vIyUqlUXw3b2dlZfP78WfLaYjeRC6C1ReLXg0Su/6JmwiLzCFCCeLyZJd9J+ZLMupiDIHKJp8+WlpZU9x/QO3nQZDKhXC4bUkBJhY3y8zyPcDgsO9pZT+N54MfNeGtrC8PDw3jx4kVffzNqMzw8DLfbjcPDQwSDQdm/z0buLy8vFYlcFosFwWCwbVKiVCHbZrNhcXGxmRjZjW6Fkd/vx/b2Nu7u7mS/RrVa7YnISmuLBEG0Q617kBI/HzHMd/P6+lrR5DkTPYxqiJnNZtzf3yOTyaBcLiMSiXScftLi3P2KXEqTB42c5OJ5HhcXF0gkEnA6nX0lVMo5r1JYomYymZQsxum5KWKxWFAqlVAsFhUJs8wA3uPxtH1cveoyqUFA3fB6vchms31Nl7LJtn//+9+S1hZ7iVy0tkj8atC3jP9idMJiv+PNTOTSe0JCLHK1Tj91W63sB72TBwFtvSb6ga1SVCqVrqP8gwJL1CyVSlhfX9e8s6wEnuexuLiIvb09+Hw+RdNwsVgMu7u7mJqaUlTs+nw+5HI5XFxcPEpN7WU8L2ZmZgb5fB6FQqHr+4LjuI4FLktbVLK22K7gorVFgiDaoYbIZbPZUKvVFE1PtAv1UTrFUa/XNRc52lGtVlEsFnF1dYWVlRXdp8/68UsV+84Gg0HZk//Dw8Mdtwq0pFQq4ezsDG63G+vr6xgdHdX9GqTSmqgpxxpCDU8udpxeryurO75+/YqNjQ3ZjUKz2Yzl5eWOBvBSmo9SgoB6MTMz0/TxUzo5NTo6ivn5eaRSKUQika4/K+Vx0doi8StBIpeKjIyMyE6WEQQBZ2dnyGQy8Pl8eP/+vaIYZKMMTy0WCyqVCo6Ojp5MPz0HpI6XG9Ul7ES1WkUqlcLt7e2zWKWoVCpIJpMolUqYnp7GxMTEQApcAJqmvbFYDPv7+9jY2JD93FqtViwuLuL4+Bh+v1/RdbCkRJfL1fxMkLOSLDYv3djY6Ph7HMd1FcfHx8fh9XplT7Z16irS2iJBEK2oOU0vR+TqFuqjBFaL6SlyiVOf7XY7JicnHzVH9EJJM1CO72w39K7Rbm5uEI/HwXEcpqene05Mq4kSMbhfawg1PbmkMDo6iunpaWQyGUUpz16vt+lvOjU19ejfpIhBnRKzlVxHu6l8OczNzWF3d1fSZJuU66S1ReJXgUSu/6JWgVUoFCT9bKu48vbt274+bFgHU094nkexWGyuJQ66uaYYQRBwcXGBZDIJp9PZs6M1KJNc4m7z8vIyXrx4oVrhocW6Yq1WQzKZRLFYRCgUgs/nQ6FQ0DQFqxtSHiNbNbHb7U1/rsXFRdnnmpmZQSaTQaVSUXKpbZMSOY6T9QWOmZemUqmO/hbt0hVb8fv92NnZkTXZ1ssEldYWCYJQEyZySUEsriwuLqo2ea5nw7HV93RlZQUnJye6p08z5PiiNRoNZLNZnJycqJI8qJcn193dHRKJBHieRzQaRblclt3c1pO7uzscHBxgaGioL2sItSa55LC4uKjYLgH44W+6u7uLycnJR7WIVBsJlpTYKQioFxzHwel0olAo4PLyEl6vV/YxAGmTbb2SK1uPR2uLxK8Afbv4L2p2EbshFldcLpdqu/tWq1W3G614+mxiYgI+n09Rp6Uf+vG9YOPaY2NjksfL9fasaoWlPOZyOQQCAUSjUVW7amp36FpXD5SufhiB+H0VCASwvb0Nr9crew2BmdifnZ1hcXFRUQHP/L1YUqISjy9mXtqpC1iv13sKTcPDw4jFYtjb25O8ttjtuLS2SBCEGL0sI9QWV1rRQ+QS1wOtvqcWi8WwJpIUeJ7HyckJjo6OMDs7q1pzVOtJLpby+PDwgHA43DQCr1QqhtaGnSiVSs1VOTVCB/Ty5BI/l2waXU7dIYY1CuPxOFZXV5v/X85EPEtKbBcE1AuO42Cz2ZpT+U6nU9GmDvBjsm12dhbpdBrhcPjJv8utDWlt8f+z9+bBje3Zfd8X+0qAWLmT4AKSTfZGsvmanWyQdg0AACAASURBVHGlkkqcqMqxE1cSl6w4cRLHUVWqVImjsquySEmVPJJj+Q/LKkullMcqWevIkhdFI2msmdFo5Pea3f24NLvJJomVKxaSILHvuPmD7wdeAPcC915cAHz97qdqal4TuAuAu5x7zvd8j8RXASnJJSLNAix6L7zRaBS9d1+tVuP6+lq09TFBzCr9fn81QVcsFuHz+Tq6XSbIhEU+N52bmxt4PB6oVCo8fPgQBoOB87K9ugGUSiWcnJzg/Pxc1Gpzp6D7srG1HnTTwFQI9CQXfdLO06dPee+3TCaDw+Hg5KfAtvzc3Fx1UiIfT676dbBVAbmu02g0wm634/j4GC6Xi9O2mx2rUtuihIQEQYx7glarRTweZ3yNbaiM2HQyyUXur2dnZ6z312YTr3uJWNYcbJD7idjQpzzOzMzAZrPVHKu9LoDWk8vl4PV6kU6nq/srBt1SctUXr41GI2w2G05OTjAxMcF7fU6nE9FotFooBG7PI67iAjII6PDwEA8fPuS1bZJMY0u28WVkZATb29uIx+MNw6Vamc4zIbUtSnzsSEmuLyDKgnZuVgqFgnH5WCwGr9cruBeeC50MrCiKwuXlJXw+H/r6+rC0tFTTMtWLgIpPkiuRSMDr9QIA5ufn79UkPzYqlQqKxSLW19c7Um2up91jnzxAHB8ft9zfL1OSCwD6+/thMBgQCoUwPDzMa13lchmjo6M4PDxkDEy4QCYl+nw+QUou4K4KyJRs45M4I9MW7XZ7y7ZFLseT1LYoISEBdE5NTx/qwzRURmzUajVrok0oZOr2yckJRkZG8OLFC9b7wH1IctHv8fRJflartW1rDjbEVnJxnfJ4X5Jc9P0l1hBixlnd8uRi6tCYmJjAxsYGHA4H7+cnmUyG2dnZaqGQTADlE0c5HA5Eo9GGQUCtoKvZ61X5QiAFy93d3YaCJRfbCab1SW2LEh8z0lMFDbFuVuQGT1cOLSwsCJrSxpVOJblisRg8Hg/0ej1jgq5XARUXj6xUKgWv14tSqYSZmZl7a3ROhz7lkaIoPHv2TNCkqG5BqrOBQAADAwNYW1vj9ADRiyRXJpNBKBTCyMgIp+k+dKampqojqfm0FxMT+/n5eezu7mJlZUVQIEEmJXJpLWSDrQpI9pELcrkc8/PzODg4wNLSEutn4dpKLLUtSkhIiAU9yUVXDtntdtGVQ2yIGYvVT93mcn8lBcBeQfflohdH+UzyE4JYnlzEauHi4gIul6vllMdeJ7lKpRICgYDgqZRcEaM4eX19jUwm02AET4cpdqDbJSwtLfHeD41Gg9HRUfh8PszNzQkqFhJ/L/ogoFbQ2yLrVflC4zi9Xo/BwcGGtkUhSi5AaluU+LiRklwio1KpEIvFEAwGqw+E3VAOiZ1soifoFhcXWRN0fIxGxaRZIJfJZODz+ZDJZOB2u2G1Wru8d/yhVzvJlMe3b9/e28pKfXWWbSol27LdhG5+r1KpoFAoeCuyhE7aIcGUWq2uTgqampri/RlIgLS+vs572fp1MFUB+WA0GmG1Wpu2LfIJuKS2RQkJCTE9ucLhMPx+f0eVQ2yIkeRqJ0HXayWXQqHAxcUFjo6OOtq9UE+7Sq5yuYyjoyOEQiGMj4/X+Jw1o1dJLoqiEAgEumZl0U6Si25+n81mYTAYWO1C2ApkJpMJfX19ODs7w+joKO99GBoaQjQaxfX1taAkF9MgoFbUq+SJKt/r9bY1jXN0dBRbW1tIJBIwmUwAhCe5AKltUeLjRUpy0Wg3yEomk0in0/B4PHjw4IGg1iShiJV9TyQS8Hg81Wke97W1jynJlcvl4Pf7EY/HMTMzA7vdLnpVQuxWO7pXW321s5vBE5/PdHV1BY/HI7g62612RVLhpFdk8/k8NjY2eCuygNtJO5FIBBcXF00rkXTowRQJTIROCtJqtVCpVDg+Phbk7wXcVQHpY7mFHGMTExPY3NyEw+FgDFb5BlxS26KEhEQ79zwy1CeTySAWi3VcOcRGO0ku+tRtoQm6Xk6CjsfjSCaTODk56Xj3Qj1CPblIK+jp6akga4huF3qJNUQymayq5ztpZUHfLt8kWr35vdlsxuXlZVUJzhQHNtvO1NQUNjY2YLfbeZ/bpMj37t076HQ6UQYBtYLJCoKo8mOxmOACPHk+o3cHtJPkktoWJT5WpKcJGkIfvOkXcqvViqGhoa4muOgITSC029rXbZ8leiBXKBQQCARwdXWFqakpPHjwoCP7QiqFYgUU19fX8Hg8rNXOblcIW22LqPvUajUePXrEy7i/fjudPFbo5vdjY2PViixFUVAoFIIUWQS3212dtMMloKAHbEImFNajVCqRSqUE+3sBtVVAg8EgaD/q2wfq10GqglyR2hYlJCSE3PPqh/ro9XrMz8/37EFNSJKJ7ntqMpnaStD14tqZTCbh9XpRqVRgNpsxOzvb1QQXwF/JRbeGGBwcbMurrRtxGt0awm63w2QyYXJysuPbpW+f67FFN+t3u93VhFClUoHJZILJZGJVZDVTWZFBQAcHB3j8+DHvY12n02F4eBjHx8eC4ni+LYdMZv1kHTs7O1heXm6rbXFgYACBQADT09MoFottnXNS26LEx4iU5GoDelscmWJydHTUYHzaLYi6ic/DJRmLnMvlMDMzI6iyQIK6biowlEol8vk8PB4PotEoXC4XZmdnO3phFivJRdRycrm8abWz0yOxuZJMJuHxeADcb+N++mhytulZFEUJUmQRVCoVXC4Xryk59GOy3UlB9JZDof5eZB0fPnzA4uKi4OpfX18fLBYL42cRUlWU2hYlJL7a8L1/Mw312dzcRD6fF3V6NR/4fgaSoGPzPb3P0Au8MzMzsFgs2Nvb64mSjKsnF10tR6wh2mnR6rSSiz7V3GKxYHV1FXK5HJubmx3bJtt+tDq26QXn6elpVrP+yclJVkVWK8WYxWJBJBJBJBLB4OAg788xMjICv9+PTCbDW80P1A4Cmpub4708cKvKHx0dhd/vx+zsrKB1AMDY2Fi1YNmOkosgtS1KfGxISS4aQqoU09PTNW1xWq0WyWSyk7vJCpHJc7nQkTHDqVSKcSwyH4gHRLeSXOVyGdfX19UpMp32IiCQZJ7QGwldLUek283odrti/bYymQw8Hk9Vai6WcX8nWj7D4TACgQDn0eRsiiwu33e7U3LIpCC73c5bDSeTyaoVPKH+XgBgMBjgcDhwfHzc1nnrcrmq0xbpn6VYLAoKkqS2RQkJiVY0G+pDzOd7leQitHpYpyujHz58KFgZzYRMJhPUXsaVbDYLn8+HdDpdjR8JCoWiJ8b3rYqCRC3n9XphNptFa2ftZJxGVyjSp5r3wnOtWdxWKpUQDAYRiURaFpyJop5NkcXluJ2ZmcHm5iasVivvOEMmk0Gj0cDr9bY9COj6+hoWi4X38sCtR9jbt29xc3MjOLYmBcu9vT3odLq2k1xS26LEx4b0FEGj1YN3Pp+H3+/Hzc0N60hhjUaDy8vLTu4mKyTJ1SxYon+G6elpLC4utp1wIEmuTgeVdO+Evr4+jI6OYnx8vKPbpCNUWUUUf9lslpdarldKLjapuViIleSiB639/f1YWVnhXJljU2Rx3bf6kdR8IK1+zXwpmKCP8B4bG8Pm5qZgfy8AGB8fx+vXr9t6uJLL5ZidnW34LEKl81LbooTEV5dW5zsXz1D6hMVeoVarUSwWGe9H3fA9Jap+sdUY9fGjw+Fo+M2USmVPlFzNkk1E8afT6fDkyRNR1XKdSHJ1MgEqFKbkU6VSwfHxcbX1kE/B2WKxIBwONyiyuCS5lEolpqamcHh4iIcPH/L+LHK5HE6nE0dHR4JaPukthysrK4zdHfR4rdk63r17x7oOLhgMBgwMDOD09FSU6bFS26LEx4SU5OIAXYI7OTmJ+fl51hO/lwFWM8NTPp+BL52e5lM/Rvv58+e4ubnB1dVVx7bJBF+vDWKEn0gkGhR/XOi2J1e5XMb+/j5isVhTqXm7iDWK2uPxQKfT4enTp4ISrA6HA+FwuEaRxbX6XT+Smi+tfCmYKJVK1UCIPCB9+PBBsL+XTCbDyMgIjo6O2qr6m0wmmM1mnJycVJPO7UjnpbZFCYmvJmz3BeL5VC6XW6qg70uSq1Ao1CS5UqkUPB4Pp8/QLiQmEyvJxSd+7JWSi4l4PA6PxwOlUtkxI3wx47T7bA1Bj9uYYnIhyuuZmRlsbW3VKLK4xiJ2ux3hcBgXFxdwOBy8t00KhQ6HQ9BxodVqq62PTIOAmEzn6yEeYWzr4MrY2BgCgQCy2awo57zUtijxsSAluWgQFQG5YRWLRQSDwarnk9vtbnnxvW9JrnoZMZfPwJdOJbmajdFmmq7Yabgqq8Qywu+WkqtUKuH09BSXl5d48OAB5ubm7m31hqufGReYTET5JHvISGo2uXmrSh7xpbDZbJySdPW+dwaDAXa7HcfHx3C5XJz2uR6lUgmDwdDWOoBajw29Xt+2P4TUtighIcHk+dQKrVaL6+vrLuwdO/RYTAzfU76IFZPR48eJiQlO8WOvlFx0SEKxUqlgdnYWJpOpY9siraHtQI6RfD7P+TjvNuS5KBwOw+/3N8TkQlCpVJicnITH48Hi4iIAflMcZ2dnsb29jf7+fkEeoHRFvZDnouHhYWxvbzMOAuKS5AJuPcLY1sEV0oLp8XgEFz3r1ye1LUp8DEhPDwyUSiUcHR0hHA5jfHyclwS32+OE6ajVaqTTaQC3n+H4+BihUAhjY2Md9a0SO8lFN9pka0PrRSDVSsnFx5eAC51WctGnEDocDjgcDgwPD3dsewQhSq50Og2Px8PZz4wrRJEVCATgdrt5BVgkSfb+/XssLy83yM2bTQkCaicFPXnypOV3QldyEcbHx6ueWEISfqVSCQ6HA6FQSPA6gMZpi+0muaS2RQmJrx7kPGca6sOV+6LkSqVSCIVCovie8kWpVLYVk9VPKOYTPxIVSC+oVCrY2dlBLpeD2+3uSrKond+Ubg3R7WOEDxRFIZfL4d27d7BYLLysIerXU4/D4UAkEsHl5SXsdjuvGEytVmN8fBw+nw/z8/O894cMzzk9PRVkfUIfBFQfA3JNcjVbBx8UCgUcDofgFsx6pLZFiY8BKclVx9nZGQKBAEZHRxmns3FFbHNtLqjVasRiMQSDQZydnWFkZKStz8AVlUqFbDbb9nrqR4HTjTbr6YUknrRR1dNOQNgMMSqETDBNIUyn0zg6OhJ9W0zwHUXt9XoZDW7FYmhoqFpJU6lUvH47nU6HwcFBBINBTE9P17zGZRKnxWJBNBpFKBRqmWBkmmBKr0YuLy/zvuYQ76x2K5rAXQvm6empKIMopLZFCYmvHnt7e4Jb/IHeJ7ny+TwuLy+RyWSwsLAgiu8pX1QqlaD4qFKp4PT0FCcnJ6wTilvB19ZBDEiyKJvNYn5+vqvJIiHFyEKhAL/f35Y1RLc+H7GGyGQyePToUUdiMLoii691wsDAACKRCGKxGCeVZL3C3uVy1ajQ+aLX6xljQK5Jrmbr4Ar5TGTaot1uF6XVVWpblPiyIyW56ujv7287McTkx9BpKpUKYrEYzs/PMTk5KbhHXgjtVg2BuxspfRR4q212O8lVP6JajICwGWKrAuntn/VTCLvp/8V1FLXf769O0GQyuBULeiVtbm6Od5JndHSU0QS+XC5zWtf09DQ2Nzdhs9maXjPYgiZSjaR7YnGFrJO+jomJCV7roDM5OYnNzU3On70VUtuihMRXB5lMhtHRUfT19Qm+3jfzJu0kdJsCm82G/v5+OJ3Oru8HwF9dX6lUqrHBwMBAW/FjN2Oz+mTRzc0N7HZ7V7ZN4FOMrFf732drCLo/2IMHD+DxeDpWbFKr1RgbG4PP54NOp+N17HExgadTr7Anw3OICl3I7zE6Ooqtra2aGJBPkou+jkQiwbu9lijn6Yp6qW1RQgKQjtg6TCZT24kKrVaLXC4n0h41h6IonJ+f4+XLlyiVSujr68P09HRXHwiFVg2BW2PQjY0NBINBLCwscEpwAb2pFpJtku98fX0d+Xwen3zyCSYnJ0VXzImVeCLtn+vr64jH41hZWYHb7RZlEovYlEoleL1evHnzBiaTCWtra3A6nR0PBPV6PQYGBnB+fs77dyQm8AcHBzXBbqt2RYJSqcT09DQODg6a/t5M7YoEl8uFSCSCTCbDa9/pgZjL5UI0Gq22PAtBoVBgdnYW+XxelGOX3rbYqzZwCQmJ7tHf3992m383KRaL8Hg8ePPmDYxGI9bW1jAwMNDRYTyt4JrkoigK4XAY6+vrSKVSWF1dxczMTFvxYzdis1KpVP3OTSYTXrx4gYGBga4P6wG4HW/lchmBQACvXr2CWq3GixcvMDIyIvhY7eRnzGQyePv2Lfb39zE1NYXl5WX09fV1vDtlcHAQ2WwW6XSadwxGN4FvBVNcZjab0dfXh7OzM17bJZBEGz0G5JvkYloHV+j2EEajserVKgbEGzibzUoxmMSXDinJ1QG6IZcnwcnLly+RTCaxurqKubm5nhh+CvHkSqVS2NragtfrxczMDJaWlnj5AfXC+0wmk+Hm5gYvX75EIpHAs2fPOposEsN4/urqCq9fv0Y0GsXTp0/x4MGDrioMmWAKlpiCwOHhYVGDKoqimh4zo6OjiMfjgs4hg8EAm81WE1hwTXIBgM1mg1KpRDQaZX0PU7sigV7B43Ne0NsK6a2P7ZxbRqMRCoVCcMBYDznXxWiJlpCQuN+Icc3vVKs/nVKpBL/fj9evX0Oj0VQTF3K5vGdqMkIrNRW98BWLxbCysoK5uTlRWpI6qeSixwnkO6fHCfVq+27Q7FirVCo4OTnB+vo6KIrC2toaxsfH76UiJpfLYXd3Fzs7OxgZGcHq6mrNQB2Kojq63yTJI3Rq+vDwMJLJJOLxeNP3scVlU1NTOD8/FyxQIIOAiO0H3yQXWYfT6UQwGOS1XL0H6vj4OC4vL5FKpXithw3SttjLxL2EhBCk/o86xAiwOpnkoigKFxcX8Pl8MJvNWF5eZvWt6hZ8klz0SUPdMgZtF+IVdnx8DLVa3bXvvJ2qZDwex+HhIVQqFR4+fAiDwdCxbfGlfhQ18QcbGhrquIdcs/NbLpdjZGQER0dHgqqWExMT1ZHUBoOBV5ILANxuN7a2tmCxWBgfNsrlctOHEOKJdXZ2htHRUU7bLJVKNcGRyWSC2WzG6ekpxsbGOO97/Tr7+voQCoU4T45shdS2KCEhwRWipu9Ee1W5XMbJyUlT39NeJ7maxWTE99RgMODp06eiXJ/pdELJxdUaghQGO+1DS4cpdmpmDXHfqJ8GzuYPJoaSq1WMqdPpoNfrEY1G4XA4eK2bKOp3d3exsrLCmpBji8vog4AeP34s6LOSQUAOhwOlUknQ9Yf4atXbXzSj3gNVLpdXuwva8VklSG2LEl9WpKeFOsRIcmm12pbVBCFwDU66bXrPJcn1ZZkiUw/dK4w89HcrqShEyZVMJuH1elGpVDA3N9fR0dntEgqFEAgERBlFzZVW54ZGo4FWqxWU5CHeDiSw4OtLpVKp4HK5asZp0+ESNBFPLK7JJaaAz+VyVdchJEgjySiXy4X9/X08ffq07XNdmrYoIfHVQMxCo5hJrvqCTDPfql7YKdBhislubm7g8XigVqs5Fb6EIubka2INwdUrjAwr6WZCiZ7koheh2SaD3xfoU+S5TAOvN2xnIhU7g67PBoVKeIys1+uRSCR4JXnoyzqdzqYTBpsVHy0WCyKRCMLhMIaGhnjvO10NTxTtQtfBx1eLaZq10WiE1WrF8fExXC4X7/1g2i9p2qLElw0pydUBxFZyXV9fw+v1cgpOSHDTzUkYzS52+Xwefr8fNzc3TatEQulUQi+RSMDj8UAul+PBgwfo6+tDJBJBMpkUfVts8FFXfVkUchRFIZlM4vr6Gg6Ho2tBIEVRqFQqLRNPlUoFVqsV4XAYdrudd5WbrqZSKBS8gxyn01kzTpsOF2UYvRr55MkTTudG/XuIr9bBwYGgBBUJuMxmM4xGI87PzzEyMsJrHUzI5XIcHBzA5XJ13VxYQkKiO9w3NT090eJ0OjkVZHr9AEhvGSSxDFG6iDF1rRliTL6mKAqRSAR+vx82mw2rq6ucYloxLB74QuK0TivkxIK0UJ6enmJ0dJTzNPBWsXby4hgv/9nfxlVwE47pFTz8oR/D4PzXapYnnrattuNyuXB4eChIhTQ2NlZVUzFZoLSKo2ZmZrC5uQmr1SooNiVDfK6urlpOzGaD7qvFJUFVLBYZj7f67oJ2kaYtSnzZkJJcdRDFQDutW2IFWPF4HF6vtyo95RKcEJl8ry9AxWIRgUAAl5eXcLlcmJ+fFz3wI9VSMduXUqkUvF4vSqUS3G43zGZz9TVSJewWXHzHcrkc/H4/EolEWwq5brQrElVcqVTCxMREW1P8uEI+U7lcrgZYzUZUVyoVKJVK3okiOpOTk9jY2IDT6RRUUZ6bm6uO06Yf21w9Hvr7+6HX6xEKhQQHWSRBxaf1kUCvKk5NTWFjYwNWq1WUoD+TyYCiKEF+FxISEl8NxIjBhCZa6tfRi4SXSqVCLpfD9vY2SqUSZmZmavyVOkk7n5eiKFxeXsLr9Qqy4+iFJ1cikUAqlcLJyUlHFXLtQlEUzs7OcHR0hMHBQd4TNJt5cpUKWXzvF/4H5BIXMNpGETn4DImwD5/81a9j5PGfR6VSqXqilsvlavsbE5VKBQaDAVarFaenp7wnRtPVVMvLy4z+r82SXEqlElNTUzg8PMSjR494bZvgcrlwenralocVaX202+0t/YqLxSJj1wZdFSa1LUp8FZGeEjqAWq1uK8Cit5zNzMzUJFq4bLtXXhDkBkYk0BMTE1hbW+vYhZBUK8V42M1ms/B6vchkMnC73bBarQ3v6XYA1czQtN5H4cGDBz2vHrNBV8UtLCwgEonwqpBlb0K48K7j0ruOTDoDuUoLvXkAuv4B6PsHoDPf/r+2zw654u5YIAkt8h2S369UKrE+rJAEWH9/P3Q6nSDZOl1NJSSRR8Zpe71ezM/PV//ebLpiPVNTU9WWQ7bvutWxTF8HnwQVPclFV4UJSRjWQzzEpLZFCYmPE7GUXLFYTNCyYvmeklis2+1qmUwGPp8PuVwOi4uLjLHMfSQWi8Hr9UKn0+HJkyeCWk27qeRKpVLweDyoVCrQaDR4+vRpV7bLFzGStWQ9bH9f/7X/HbHj9wAAo+22KJaNR/DpL//P+E+//hoyhRIKpaoagzVT1JMYbGJiAhsbG7Db7byPBaKmOjk5aUiScVHE2+12RCIRRKNROJ1OXtsGbo9DrVaLQCAAi8Ui6JpGT9a1SlAxtSsS6N+FGIVlqW1R4suElORioF1Vi9CkTjqdhtfrRaFQwMzMjKCWs14luRQKBfx+P8LhMC8JdLvbbFcWn8/n4fP5EI/HMTMzA7vdznrR7rbPhkwma9geXx+FXpJOp+HxeFAsFjE7O1tN1kYikeYtrskrXPrWq4mt9OVR9TXT+DKO3/0Z43IymRxakwP6/gGMLf0QFv6j/+mLv8uq21MqldUgiynQIUouAJienhYsW7dYLFAoFEgmk4K8HQYHBxGNRnF9fV29DvBRLSqVSszMzODg4ACPHj1i/L5brU9I6yNwG3DRK49EWSZW2yI5D0mQJSEh8fEgli8q30IjaTnz+XyitJx1O8lFVN0klkkkEl+KBFc8HofH44FSqcTCwgKvKdv1dENtz2QN8dlnn3V0m0Kgt1D29fWJMjCJ6dz0/NlvwPPpN6v/jp3swjq6iPjZHvqH5/H7/9fXMP3nfgTz/+GPQq0zQalUolgssirqyd/pHqdCbBNcLhdjkoxrHDU7O1sdBCREkS+TyXgPAqqHa4KqfoBQPcRn1W63i6I0lMvleP36Nb72ta/1vGtIQqIZUpKLAbGSBs3aouhks1n4fD6k0+lqy5lQup3kIlNvkskk+vv7eUug26Edg1MhSqhu+z3Q2xXpE506kUQUs10xl8vB6/WyHs/128mnbxALvPkiqfUKyfAh67pLqQvW1yiqgmw8gmw8gnjEj7l//7+HSlObBJHL5VCpVCgUCoyS+XK5XH0oaVe2bjabq+26fAMBMk57Z2cHKysr1YQuH48vq9WKSCSCSCSCwcHBhtebVf8IQlofmdY7PT2NjY0N2Gw2wYE2/biRpi1KSHy8dNsygj5gRqyWM3Kf6TSFQgF+vx+xWKwmlvF4PD1rlwRat2rSlVCzs7OiDMnpZIwmljWEWDTbNhkyoNFo8Pjx444Vgy6DO1j/9f+z5m+lQg4yhRa2yRXEjrYAqoL9P/5FTDz7z6DWmapFKhK7M8VgJM5px9eTLUlGj/GaQR8EtLCwwGvbBKFqeDr0ZB3bdalVLFdvZt/ucUuUeFLbosR9R3o6YEAsuXyhUGj6QEevvE1PT8PhcLS9bbVa3RVz9HozVqfTicHBwa4+cNLNVblSr4Ryu92cL9C9UHKRJOLR0VHLiU69hh5sT09PY3Fxseko6nzyEr7v/xISYQ8iB9wqoZnYCSwjblyfeZq+r5RL4WTrDzG19p83vEYM4Zkk8/WJaSJbv7i44D3SGgBGR0dxeHiIhw8f8l5Wq9ViZGQEfr8fbrebc9KczszMDLa2tmC1WhsSbVxbfUmgZrVaOSWomAIuMcZz09crTVuUkPh4aTfJxWXiM3CnIlIoFNUBM2JBYsBOUSwWEQwGEY1GMTk5ibm5uZrrIImPujlpkECSTUxFmU4OyemEpcR9tIZgOzeSySQ8ntvYqNNDBvKpa/zJL/5NlEu1yWTzkBvXpx9gG59H//A8bs72AADZeBjmITeA22OzUCgwfo76OIf4etrtdt6qSLPZDIPBUFOk49KuSHA4HIhEIri6uhIkPhCqhqdT37bIpspv9Zn6+vrQ39/P2MLJF6JQrVQqUtuixL3mfj4tfwSQSiLTQyFb5U0MOq3kovf3W63WSUefUwAAIABJREFUan//wcFBWyaLQuCTdKIrocbGxgQpobqp5KIoCvF4HKFQCCMjI5wmOvWKUqmEYDCISCQCl8vVEGw3vD+fwvlnv4Pt7d9BuZCFSt8PmVwJqsItYWmyDrZMcgGA7+U/Z0xyAXdBVn1wwJRIcrvdVSN4Pr9BuVyGxWJBMpkUnCQbHh7G9vY24vE4AP4JeJVKVVWj1SfauCa56K2PXBJUbFVFi8WCaDQq2BC/fqAGaU2R2hYlJD4u2o2HWi1P9z2tHzAjFp2KxUqlEo6PjxEKhTA+Ps4ay5BEXy/iBqKyp99bc7kcfD4fkslkx5RQYsZo7RREuw1JHObzecFWJ3ygKhX86T/5MaQuT2r+bh1/hHjIg3Ixh9DhG4zMv4BcoUKlXITnT38FidABxlf+EhJhD2zTaygWiw2K+noFoEKhwPT0dFPrhWbU+5PySXLJZDLMzs7i7du3MJvNnAvM9OSdGIOATCYTzGYzTk9PMTY2JmgdwN1QJCE+Z3RILCZNW5S479zPK3aPEcsTIpfL1fytWCzC4/HgzZs3MJlMePHiBQYHB0W90XcqsCJmrK9evUIsFsPy8jLm5+erFzaulVMx4aLkIuOS19fXUalUsLa2hvHxcUHBSjeUXBRFIRqNYn19Hel0GoODg5idne14oCqkcl4ulxEMBvHq1Suo1Wq8ePECIyMjrMdzuZCF/wffwPHv/ChCr38V5UIWAFDM3MAytsh5u8nz9xiYWW75vsjBZ0hE/IyvyeXyqj8XPShmSnKp1WqMj4/D6/Vy3kfgrrrmdrsRCAQEnR+kbfHwkL2FsxXEZ+7iorbVs1gscg7aiC9ZOBxu+d5mD1bT09M4PT1tuDZygWlqLGlbbNebT0JC4uOCyZ8pnU7j7du32N/fh8vlwsrKSkcSXID4sVilUsHR0RFevXoFuVyOtbU1jI2NscYyQpTuYkH3Sy0UCtjf3696Aj1//ryp92k7iOHJRY9rlEplNa65jwmuXC6Hvb097OzsYHh4GM+ePet4ggsA9v70N3D27ns1f9MYrEhGAygXv7i3UxRCh2/QNzANgEKlkMb+t38OofffQeDlb0KhUFRNzOkwtbnabDYolcqGGIYL9CIdGY7Fx/ZBo9FgbGwMPp+P8zJMarTT09O2BpK5XC6Ew2FkMpmav/NpSaa3LbajlM3n81Cr1TXTFrs91VRCgguSkosBsdoVyQWNXhFqVnkTg04kuWKxGDweD/R6PWt//31LclEUhVAohGAwCIfDIYoSqtNJLvr3/PTpU6RSKcETojpJpVLB+fk55xbKSrmI089/F97v/SLySeYgRS7nfs5VSgWU4icYf/zvIZOIoVIuo1Iu3v6vVEC5VEC5WEC5mMO/+Qf/BVZ/+KcwsfIXANyOuk5FvTANPagxoSfnI1tL4MDAACKRSI0RfCtIMKVWqzExMQGv14sHDx5w/pwEvV4Pp9OJk5OT1m9mgUmNxreVZWZmhpMRf7Ogi68qjA5TkktqW5SQ+PgQMwbT6/XViYOZTKZt31OuiBWL0e+3g4ODnC0LehGTEZRKJfL5PM7PzxGJRBjbKTtBO0ouvnFNLykWi0in09jc3OxqCyVFUcgkYviTX/lJDA5NIhkNVF8zDU7hwvd5zfsr5SJkSj2sk58glwijUqkg/OH7yCVu48BWg4DouN1uwUbwxJ80Go3yTnIBt4OA+MR/9Sp5LoOAWkGfVE33GOM7YV4MVVixWKzGYtK0RYn7zP28gn8EaDQaXF9fIxgMVs3C19bWeF9c+SJm9Y4+9WZxcbHp1BuVSoV0Oi3KdrmiVCobFCFECeX3+2GxWPDs2TPRZLRimrPTYfueM5lMR7YnFHqrqt1ub5k4pCoVhHb+AJ7v/DwyseYJmsT5HnSWIWSvQ6zvURttMDomgXIeqYgXmbNNmMeXcX6wDqpSm3zU6PpgHJyFxmhGaPO3cbX3B4gF3qBSzAGgoDUPYuyTH8bY2n+FCu4SUmxJLiYj+FbQgymn04lIJIJYLCZo4tXIyAiCwSCSyaQgnw21Wt1goloqlXiZwCuVyrbaBghWqxXRaBThcJjX5EmmJBcgtS1KSHxsiKWmTyQSCAaDnKYni027SS56oY7L/baeXiW5yuUy0uk03r17B5fL1ZVJ2wQhnlwURSEcDiMQCAj6nrsJKZiHQiEoFAqsra115bulKAoURaFUKmH9d/8Bcqlr5Ep3RvD9w3MNCS4AkMmVAChc+l7DObOKbCqJ8Ic/Q9/AFF5+40fxyX/zj6rPLEQVxHZ+tmsET/xJVSoV7+cwEv+9e/eOU/zHlHhqNQiIC8SInz6xUUhLMpm2aLPZBMVMhUKhZjmpbVHiviIluRgg6gChCYZKpYKbmxucnJxgamqqqxUhMQI4Yl5JURTnqTe9kMbTJfH145KXlpbaHpdcj9jBcavpQvTpip2m2fFOURQuLy/h8/lgMpmwsrLS0gD00vca+9/6GSTDB9x2gKpAqzMin1CiUr47jowDU9Aa7SikrpC5CiJxslWzWPx4E7bBCfQNuQGKQi4eQSF1gWLmBuXkMTIsMxhy8TA8f/wPcf72W1j4y1+H1uaqGv2zBY1arRbDw8MIBAKYmZlp+ZHoCjESJL19+5Zzkqx+XUajEQcHB1heXhYU2NabqBaLRd7j2m02W7UiOjAwwLifXD4bV1UYnUKhwLq/0rRFCQkJQqFQQCKRQDQaxdzcXE/MwoUmueiFuv7+fk73Wya6HZORITknJydQqVSYn5+H0+ns2vYBMLa/sUHiGq/X29b33A2I7cbp6SlGRkawsrKC3d3djie4KIqqWjqo1Wqc+t7h7be/AQC4OPXAatKiUrpV0dej0vbBMjSJWHATMrkShVwG+UwcKgWQCB0iETpE/Hwf1okn1WmLrT5PO0bwKpUKk5OT2N/fFyQ20Ol0nOM/NnWV2+2uxj1Ck0HEiJ9MbBSS5CKqsP39fVYz+2aQdkUCvW1RmrYocZ+QngZEhC53ttlsMJlMmJqa6sm+CBkdnU6n4fV6USgU4Ha70d/fz3nZXrYrXl9fw+v1dnxcsliQ1olsNouZmRlWZQ9JuvQS+mh1Lt9tpVTA/rf/IY5e/Tbk4Lfv6csAbFPLKGazUGo0yFweIXd1+z8m+gZmoOuzIncVQProFWRyBXTOeaSjrUzpZTAOuKHUGpG5OsLBt34KT//6P0W5XG45wXBkZARbW1tIJBKckr/0dWk0mpppiXwgY68NBgOOj4/hcrl4LQ80mqgKnbxFbxuoD9S4BlxCVGFsSi5AaluUkPiYEHr+komDFxcXMJlM1anPvYCvvYHYhTqVStWWBxBX6JO2BwYG8Pz5cxwdHfUkduHqyVVvDaHT6bqwd/yhf7f0VtV8Pt/RexxJbhEFl0wmw/z8PP75T//VakKrXMzDPPYcCrkMl/43Ncsr1VoYLfbqZEXr+CNcBW8LlBrzAPKpKwDAzdkerBNPoFKpqoOAmiHUCJ7gcDjw4cMH3NzcwG6381oW4B7/sSW5lEol6yAgrtRPbBQ6XMJsNsNkMtWowrhCb1ckSG2LEvcRKcnFAh8lF13ubLPZsLq6CpVKhZcvX3Z4L5khCSeulYJsNgufz4d0Oi3Yr6IXSa58Po9wOIxcLtfxcclikM/n4fP5OLdOdKo9kgvJZBKHh4eQy+WcR6unr06w+Rv/K25OdgAAtqlniB9vtVjqFqXaAPPoAjKXfuhsk7j0vWF8n848iD7HBIqpCPLxE6QSd22QVKWMTHgXlvEnuD5+W7ugTIG+wTkoVBqkL/xIRe6M3AupK2TCu9APLrasJhJF1ocPHwQpqujTEvkYHpOgaWJiApubm3A4HDAYDLy2DdSaqPL1ciA0m9jIJ+Cy2WyIRqOc5fvNklyA1LYoIfGxwPcBqd73dG1tDbFYDFdXVx3aw9bw+Qx8i0lcUKlUSKVSba+HDbp9AYl7yfW5G0N6mGjlycXHgqOXEDWfz+dr+G47vV16gos++fBo41uIeT6teX88FkXy8hwO5xAy1+fVvzumlnD1ReLLNrmMq8Bm9TVd/1A1yZW5uo3f5HI5FAoFisViy5hXo9FgdHQUfr8fs7OzvD+jRqOpqiT5xj8k2be7u4uVlRXW+K9ZbGW326tqeKFKR4vFgouLC4RCIchkMsHttZOTk9W2RT6JXtKaWI/Utihx35CSXCxwCVDIxEGfz3ev5M7EcLXVRYaedJmenobD4RCcfe9mkiuVSlXHJRuNRiwtLXVlu0IpFosIBAK4vLzkZRIq5jjsVpCEWiaTgcfj4a3mO3/7B3j7uz+BUu4uqM7Goy0VhWqjFX0DM0iFPiB+fBsIFTNbsE0uI3lxjELqEkqtEf3D80Api8yFF6mz66b7kr/yweicRvrqBKbBOUCuQCrqQTK0x7rM8We/isX/8mc5BeYGgwF2u12QoookyVoFSfWUSqXqNCJiPipEZg7cmqhGo1Hk83nBrX1sgRrfqiKR71sslpbXTi7rltoWJSQ+DrgUecrlMo6Pj3F+fo7R0dEa/yemCde9oNk9kCRdFAoFFhYWRE26dKpdsd6+YHl5uUFx1qvJjmwxE90awu12d2yiphhcXV3B4/Ggr6+P8bsFIHrxk6yvXC5Xj1fyv0qljE9/6+v4/F//o4blVHozcpl9aE0L1SSXWm9G/Owu1sonL2u3JVNCbxlB5voM2XgEueQVtH22qv0Il5hmaGhIULEQuL2ukELf3Nwcr2WBu0FAwWCQtVOnVQFxdnZWsIk+YWpqqjqxVGhSnN62SDezbwVFUazetVLbosR9QnoKYKHZyV4vK2eTO7fy+OkURPrLRqFQQCAQwNXVlWiTWboR1GSzWXi9XmQyGbjdbhgMBrx7966j22SCaysovbo8MTHB2yS0m0qufD6PVCqFnZ0duN1uzmq+UiGL3d/7aRy//u2G1zJXx+gfW0QqtN/wmsY8BKNtBImz94gfbTS8njzbgd46DrvrERLBN8iEuP/OSoMdGpMDuZsw4mfclot++B6oN9+HQs1tcuL4+LhgRZVer8fAwEDTIKmecrlcDZpMJhNMJpPg6Tgk0ba+vt7Wec8UqPFNcpG2RaIKa7U/XF6X2hYlJL78NLv/0f2fhoeHGYf60Cdc9wo2VX0ymYTX6+1o0qUThcdYLAav1wudTtdUcaZUKkWf8s2FeuP5+phRyNCXbnFzcwOPxwO1Wo1Hjx4JUmoLge67BQDZxBX8W9+BUq2FUq3D++/8MoLb321Yrn9oCucHrwEAxYoMMrkCVKUM2/hCVcVlHX+M2PFOzXJXwW3YXE+A6zNkbs7x6f/73+E/+PHfw83NDbxeL6xWa8vnJqHFQgLfaYn1jI2NYXNzE06nkzEx3WqoT7sm+kDtxEa+9hd0iJn9+fk5RkZGWr6/1fOP1LYocZ+Qklw8ITd5LrJyEmR1u99fo9EwBhilUgnBYBCRSAQulwtut1u0BFwnL2RsbX7lcrnr1UISeDf7vPUmoUKnC3VDyUUSnpeXl1CpVHj+/Dnn3zIRPsTGr/8tpCJe1vdkbyJQGSwopm/VV0bnNEqUAqVrP+LpKOMyaqMdRvs4UufvkUieo2/0Kae2R519EjKlFomz98jETmAcfoib+rZFVigoLj7Ho7/wt1EqlVoGWXK5HHNzc6yKqlbJSRIkpVIpTtV7ouQiEJm53W4XdH3RarVQqVQIBoOCAyQSqB0eHmJxcRGAsEk/pG2Rzcwe4Fe5ltoWJSS+/DDdh+i+p8T/iU0x0Ss1ER1iPk+SXJlMBl6vF7lcDm63W9ADNlfETHLR2/y4KM7oQ4G6CYmZ+FpD9BL6oKdu2m7UJ7eoShlb3/5l/Ntv/j3kM4nq+2yDzIU0Cgrgi/ty8P1LuJf+XWSvjnB9tF19T7nYqKSslEvIJ2MAgFhwG1SlhNeffg/aPjsWFxehVqurSqhmMZiQYiFdpTY3N4f3799jeXmZtxG9XC7H/Px8Nf6r308uVhBk4vbl5aUgfzDgdmKjTCZDMpkUvA7gzszearW2jCe5fDapbVHiviAluViovyHyvckDvUty1U/1oUv6x8bGujrSuR1aKc662c5HIF4TTN8fPQCnm4QKpZNKLrrKzOVyweVyYWdnh3Ob7vGr38b73/s6KqXmlfJ88hIGxxRM/SOQyypINZm2qNKZYRp0IxXaRersrvqXPN2GeXyJMdFFURQMgwuolPNIRmoN56kSv1aV64PvQPEX/w4qNPPaZudJX18fq3En10okW5BUD13JBTSajwoJ4IlnixDJP8HpdCIajVYDNSETG4G78d5MZvbAbfKMz7kktS1KSHw80H1P7XY7J4+i+5DUUKvV1bZwr9eLVCpV9T3t9P6JkeQS2ubXK0+uSqWC6+trbGxsiNalwBW+w566mfCkU5/ckslkSF6e4Y//6f8G75s/bHi/0TaK5MVJzd8G3c8Q9nxe87cz/x6m5h7iKnCrzDcPzyN+3qjiB4Bs4uK206VchMo2A7uhgqnHjwGgum9cpi2Ojo5ia2uLc7GQ/hvpdDoMDQ1xnpZdj9FohMViwcnJCSYmJmpe4+p3Skz0hfiDEXQ6HSKRCIaHhwXb5ZC2RS7xZCtvVEBqW5S4P0hHHgvkJE8mk9jc3ITX68Xs7CyePn3K+SGuV54QJMlVqVRwfHyM9fV1AMDa2hrGx8c7dsERaxpgqVSCz+fDmzdvYDAYsLa2hsHBwYYLby+CWKbEGgnA19fXkclk8Mknn2B6errth+tOJLkqlQqCwSBevXoFpVKJFy9eYGRkhPN3WcwmsPHr/wt2/sVPtkxwAYBSY4DW2A+AYlV8KVQ6WCdXoZBVkDzdBsUwjjoT9UCpv/MHoyCDcfgh1P2jSJy/RyrSOFExHfXCYHdx+lwAkE9Gcen9rPq7cfnuJycnEQqFGs7zcrncsjpIgqTT09OW22EKmvr7+6HT6RAKhVouXw99YtLh4WFbDySzs7Pw+/0oFouCJ/0QM/uDA+YkKJfAig69bbFXwxskJCSEQ+5/kUgE6+vruLm5wcrKCmZnZzlfC3qlKCLI5XIEg0FsbW3B6XTi+fPnXVMVyeVywde+TCaDnZ0d7O3tweVyYWVlhVchRKlUdjXJRWLG9+/fQy6Xs8aMnYJPrJbP57G3t4e3b99iaGgIq6urXUlwURRV7X6oVCrVRMTl8Qf86v/xQ8in44zLXZ37Adr3OOR+hqh/u+F9coUaFUoGuXkSkMkgV7DHv4VsEirHIkwTnyATPoAsF8fxxu/frkcur8Y6rZ4n6B6lXL7/+rhsZGQEiUQCiUSiyVLsuFwuRKNRZDKZmr9zTXJpNBqMj4/D62XvhmhFuVyuxk7txDr9/f3Q6/U4Pz9v+j4ufs/A3bNSNpuVYjCJniEluVioVCrY3t7G/v4+pqamsLKy0nRkLBO98oRQqVSIxWJ4+fIlCoUCnj9/jsnJSd6SXL602x5QLpcbEjCjo6P3qgpAr1CSwQPr6+uIxWLVAFyokWQ9YirViIfJy5cvUS6X8fz5c0xMTPD6bm/OPuAHP/eXEdr5I07v7xuahUZvQvzkLRJnuzAMzgOyu2NQplDCOvkMGq0OqdMtVIpZ1nWVC2kYbC7IFGoYR59CabDh5uQtsrHjpvugMfKbFHq++a8gl8uhUqlqqp1sKBSKqi8C/UbOJckF3AZJkUikIUiqh21909PTOD095X2dIevT6XQYHBxEMBjktTwdtVpdDdTYpu5wwW63Q6FQIBptbGPlm+QC7h7ysln240pCQuJ+kk6n8erVK1xeXmJpaQkPHjzgrVToVQxWLBZxeHiIUCgEtVqNtbU1OJ3Oe6Eua0Yul8Pe3h52dnbaSsB0K7lYqVRwdHSEV69eQaFQYGVlBVqttusxI5fflRwTpC1sbW2trWFPXGFLbslkMpzsvcSv/8R/glQsjEIuzbh86joC2+g8KAoYXfxzCHk+R6Xc+Nv2D0zAu7OOsP8drJOfsMZOCuMA9M5ZpEMHQOW2oHl1tI3Pv/kTqHxR4CRDdsrlcssYrK+vD/39/ZyKhfVxFFHUHx4eCoq1iW3F/v5+TfzHZ3L1wMAA8vk8YrEY7+0Dt5/J4XBAqVQyxk58mJ6extnZWVNxBp9YjLQtdmsomYREPfcne3DPUCgUmJ6exurqKucJc/V0O8CiKAqhUAi7u7vI5/NYXV3FzMxM19p1hMrjiYfV+vo6KpUK1tbWeCdgugW58V5fX+PNmzcIh8N48uQJFhYWRJ+s2U4llkBXmaXTaVaVWatK5KV/A9/7ub8Cpa51NVcmV8A+/RzZywDyybubbvxkBxrzCDT9ozCPPYVKa0LqdBulXOsqGgU5KgDUljHcHG0gn4i0XAYAUpEDyJXcfhfDgBvpmwsUMolqkMUlUCeTASORu33imuTiWolkC5qIcTvfKh59faOjo4jH44KrmcBtoFYsFpHJZNryYHC73QgGgw2egkKSXMBd22KvvXkkJCT4QXxPFxcXm5o4N0Oj0XRVTU8URa9fv4Zer8f8/Dx0Ol1Pk1tc7guFQgEHBwfY3NyE1WrF8+fP20rAdFrJRS/alUolPH/+HC6XC0qlsusWFkDzLoZyuQy/3189JrqlMmuW3AKA491P8dt/969U/bcujnahNTInNDV6M1RGBzLxC8bXtUYLov6tLzy4ZAifBEApGs/ZvpHHKMTDSIb2UcqnUCrkQFEUEpEAzMOz+MOv/8e4Ct7aUnBVcwG3xcJwONyyoMUUl9GnZQvBZDKhr68PZ2dn1b/xSXKRRJvX6xV8zshkMrjdbhwdHbU18IHYYNQn7egwDdJotl+kbbEX56WExP3LItwj+Cq36ulWuyJFUYhGo1VJ/9LSEjQaTdcN//gmuSiKwvn5OdbX15HL5fDJJ59gamqKl+JMrBZJrpTLZezu7iIYDGJhYQGPHj3qmLl1O5+NjPl+9eoVYrEYlpeXMTc3J0hlE97/M3z/H/81FLPJljdQXf8gTIPTuDn6HKAa950qF6HWGVFMhoACt6SKzjkHhdGJ6+AGlGp+E4cqxSzU1knW1/X2SRhHn6IoMyLkfYvz/U8R2vsegNsgiww4aMXMzAyOj4+r3w/XJBdQO92GjWbrs9lsvKt4dI+rdquZ9HW0UqS1QqVSYXJyEoeHhw37K+R6JrUtSkh8OVGpVG3fW7VabVcKjWwqdLYhQN2ilRq8VCrB6/XizZs3MBqNePHihSgJmE4pueqLdqurqzVFO1KE7DZMRUK6XQhpoexGZwJJbhEVVH1yCwBOP6zjd37mR1AqZL9YBlAo1bCPzzeszzG+gFPvDlI3F1CqmJPNevs4ysXb82x4fg03YT+yqdr4Tm8bx83JDihaXJhNXKFUqqCQucGF9zWS0QB8n30TAKqKeuIh1gy6R2mz+zxbHDU+Po7Ly0uk08xqtlZMTU3h/Py8mmRr5claj1arxcjICHw+H6/tUhRV/bxssRNfLBZLUxsMru2KBKltUaKXSEmuJrR7o++Gkuvq6gqvX79GNBrF06dP8eDBA+j1+p7c6Lkmueg+G4lEAs+ePYPb7RaUgOnWBKVUKoXt7W0kEgkMDw9jaWlJkME2H4R6ct3c3ODzzz/H+fk5Hj9+jIWFhZaVcLZtnb79I/zZL/0NlL8IhmLH71l9rqyuZVQKaaSjzDdq29QqyrkY0pEDKPWWlp9NbRqCxjGL+Nkucte3UvRkaA8KDb/vXVNXUNNZRtE3uoSyyoJwYBfnHz5DNnGXIDrbvjVfJd4QXNoWlUolJicn4fHceoNxMU2lMzU11VQm3qoyyLeKVyqVas43g8EAh8OBo6Mjzvtcj0ajgUqlgt/vF7wOAFUFAT1pJ1TJBUhtixISX0bEULp0OgajJzKYVOj1Q4C6DVtMVi6XEQgE8OrVK6jVat7enK0Q23ieWEPUF+3q7wm9GEYE1MZPFEXh7Oysxi7E5XKJbhfCNNGZGLbTB+fUv+/s4HP8i5/96yjS2hNHH6yhVCqjUixDplDBOjyD0QcvMDD1FJHgHgq52+KVXNWoileqdUhFbmM+ndmBi8Dt0KDIkQeArLpvcqUWVOXumNDbRlEpl0BRFBSqu+FcV8E7vy8+inriURoOh1nfw5bkok/LFhJzkyTb4eFhdXm+59Lw8DDS6TRubm44L1P/eZhiJyEQGwymeFRILCa1LUr0CmnsVBPaveGLOcK5nuvra3i9XqjVajx8+BAGw53CpVfS+Fafl6IoXF1dwefzwWg0YmlpSXAbAoEkuTqlWstms/D5fEin03C73dDpdB1TbtXDN8lFRlEDEGUUdeDV7+LNb/ydmsAEAKCsnRaq1BhhHnIjfvqWcT3qPjsM/QNInt5NR0yFD9A/vozUWeMyCo0RWts0ro+3Aap225VSHubhRcSPNzl/jnTUA+PgPGQqHZKX54gcHwJgr3aF93+AYi4FldZYbbvgkrRyOBzVkdAAeAW1dG+vx48fN5zDrZRhpIrn8XiwuLjYcntMSbOxsTFsbW3B4XAITuAqlUrkcjnEYjFYrVZB6wBuzezJtEWVStVWkguQpi1KSHwZaXf4ikajaasNmw2iQg8Gg3A6nfjkk08Yi3T3JclF4izS5ndycoLh4WGsra11xKtVzBj0+voaHo+n2r7aLP4Sw+JBCER1H4lE4Pf7YbFYOE0AFQr9M5L/LpfL1YEybL+pZ+M7+O6v/CSsw7M4S7wEADgmFnG6/wZUpYzTg9cAAI3BguPdlw3Ly+WN6x2cWUJo//a9uv4hxI5uk1ylQhoq6xLkhSsYbBOI+u6mMcpVWpTyOeQSt+2P9OMlE6tVtJNCIxd1/PT0dLXllsk+pNk6+vr6YDabcXp6irGxsabbYcJisSAajTZNsjWDDAJ6//49lpeXOZ2XTIN+6mMnISiVStZ4VIiqXpq2KNErpCOtCe3eqDuRbEokEtjY2EAwGMT8/DyePHlSk+Cib7vbFa1mSa7r6+uquujRo0dt+WyMOIYQAAAgAElEQVTQ6dSo6nw+jw8fPmB7e7saxFqt1q6OxuZ6/GQyGbx9+7Y6JGF5ebntBJfnB/8Mr3/txxsTXAAuAtvon1gBcGsur9YbET/dYVyPdWIJCqqIdKRxal4i9AEqw50xPAU5DCNPUCyVcX200ZDgIuRuzhj/zoTePgW1fQ5FSoWzvU+RiAZaLlMpFRDe+5Pqv0mgwOV3J5MGC4UC74cHq9UKtVpd4+1FKJVKLdfncDiqbaqtoLcrEujVTCHXjnK5DKVSWfWXaEdhWS+9bzfJJbUtSkh8+RBDTS+mZQRpl3v58iVSqRRWV1ebqtC7pTRng2yfSV3UjWFE7UCPdRcWFlomuHpJsVjE1tYWLi4usLS0hPn5+a7YhdB9t0iCi+2cefu938Rvff1HED0+QC57q+IyWocRj541xHmpa2bP03P/HizD7uq/LcMzuD69je0Mg3PVBBfhzLMFvWMa8Uht3GWbeFJNcAFANnld/e9iLol4yIPsze0+kLZFLib0xKOUrWWvVRzF1duLjenpaZycnAh+9iKDgAKB1nEq0KjIB25jJ5fL1XbbotVqhVarbUjaCR0uJLUtSvQCqaTdYYinT7vBRCqVqj44ut3uluOcScJJbDP0VtusDygTiQQ8Hg/kcrko6qJ6xA4ii8UigsEgLi4uMDk5ifn5+ZqgoVdyeCZyuRx8Ph+SySRmZmZgs9nafiigKAof/s0v4N3v/2zT950fvMT4kz+PG9+nABpvWEptH8yD00iev2NdR6WYhdI+iXzyAjCNQ1Ep4Dq40XIf84kIDAOzSEfYb+LGwXlkszmEA+8BAAb7RMv10jl9+0cYW/6LAG5/c+IxQipSbKjVaoyNjSEUCsHpdPLaJnDr7bW1tVVNeBEoiuJU/ZqdncX29jb6+/ubKpaYgiMAMBqNsFqtODk5wcQEv++MVBWJv4Tf78fs7CyvddAhyriLi4u2pjYSiF9LNpu9tw9LEhISd7R7PxPLk4sUD7xeL8xmM5aXlzkV6Xo9TZF4NX748AE2m62j6iKxSKfT8Hg8nGPdXhKPx3F4eIhcLofHjx/DZuM3zVkopDWRxL7NklvFfAbf/82/j5f/6uerf0t9YSCvNVqRuGz0Ak3FmNVIuXQc4WwCBr0BA5MPEfVvo1IqQO9wIXvBbFNQphSolO7UjDrzIC78tUr867N9DEwvIRH2olzMIvj6X8LoGMf0v/PDAGqnLbaKg2w2G8LhMC4uLuBwOGpeq1QqTZ/F6N5eT5484X3+KpVKuFwuHBw0FnW5Mjo6iq2tLSQSiZa+0ExKLgBwOp3VrgK73S54X5iUcVxjUSZI26JKpbr31yGJjwNJydUEMQKUdoOsTCaDnZ0d7O3tYXx8HM+ePeN00++FTF6pVFaVXMTD6vDwEDMzM1haWhI9wUW2KUaSiz4BR6vVYm1tDUNDQw3HQDeVXGzQJyHZbDY8f/4cdru9reOVKP92/vXfa5ngkitVsLueIvThBzAOzjW8bh5ZhEanQ/L8fcvt5hIR9E2soXQdRD7ObrxeT7HC8FllchiHHwL6QZx7NnF9uld9KX15BL11hPP6wx9+gHLhLmFLD7JaMTg4iEKhIOi8p7cd0uH626rVaoyPj8Pr9TZ9H1uSCwAmJiZwcXHB24SVHnANDw8jk8nw8pdgYnZ2FoFAgLeRKxvStEUJia8OYkz5I76n9EnKfFXo3VYuEA+rUCiEeDzO6mHVjf3gSjabxfv37/H+/XuMjY1xjnV7QSqVwtbWFrxeL+bm5qp+UJ2GKLcUCgXy+Tyy2Syj7xbh8M238Ys/9rWaBBeA28SWTM46ebpcKmBoZhkafWPMbh1yY3BuDel0BpVSATrzACrZOCrlxi4OiqIQuwiD0g9AJru9f+v6B2uSXoRsKg5d/21hMPThTxE/ry1i8lHUu91uBAKBhs4SLoIDLt5ezTCZTJDL5YJ9scgQHy6KerYkFwDMzc3B7/e3ZZlDb1skJvftPmdI0xYluomU5GpCL41Pc7kcdnd3sbOzg6GhIayurvLyuOlFkosoud69e4fd3d2uBCrtJp0qlQqOjo5qJuCMjY2xPlD3UslFxpO/efMGBoNBtElIAEBVyoi9/gb2v/tLTd+nNvTD5Jy8nZJTLuLq9AB9Iw8B3JqS2qaeIXtxgGI61nx7FAXD0CPkMinkUq3b6+opxvyQq27VODKFCn2jT1FWmnF+8BqJCHNF0Wgb5bz+ciGDyOGn1X8TyTzQeqS1TCaDzWZDJBIRdGwytR3yeVgYGBhAPp9HLMb+GzQzspfL5ZidneVtwkoPuOgTG9s5P9VqNSYmJkQzj5baFiUkvjyIpYQSOsDlzZs3ODk5weLiouBJyp30ZmXi+voab968QSgUwvj4OJxOpyjWEHzhGpsxWUN0SxHFl0wmg3fv3mFvbw8ulwsrKyswmUxte8e1gt6WSNRI8/PzTe/R//Zf/gK++TN/DTfR44bXhmefoVAsI5tNQ6VlnlidvonC+kVrokKlwdjCCxitg7g6O4R347vIxGOQyeSwjriRS10zrsM6ModI4B3OvDsw2MZgHpnHZWCL8b2JiB8qXT8AIJe4QD5XO6mZKOq5tC2yFfu42D4Ad22HQuKOUqkEs9mMYDAo+Lw3GAxwOp0tBwE1S3JxLXi2wmq1QqVSIRKJtByAxAWpbVGim0hJrg7DN8lVKBSwv7+Pzc1N2O12PH/+vDoxo5PbbZd8Po+joyNcXV1hcHCwa4GKUCUX3aOiWCxynoDTCyUXScTVjycX6wGgUi7i9a/9OFLe7zR9n8E2BrVWjxRtemK5mEPEu4G+sRX02ceQPN1usoZbFGoj9IOLuD7aQjmfRvL8A9SmAV77TJWL6Bt+AOPoEnJlJc4+fIZ0rLlXVyF1xWsb5+/+uObf9LZFLiOtrVYrgsEgr20SiLcX3WuDKyTB5PV6WY9VJk8uOiaTCSaTCaenp5y3Wx9w6XQ6DA8Pc/aXYMNqtUImk3HyGuOCNG1RQuLLgRj3OL4xQjKZxObmJvx+P+bm5vD06dO2Jil3q+AYj8cZPax6pVptFSsVi0V4PB5sbGygv78fa2trcDqdPW/xZCKfz2Nvbw9v377F4OAgVldXYbFYqq93KslVn9wiShiZTNZUcXTw+XfwB9/4CYzMrja8ZrKNIBz4gGIZCAc/oH/AxbjtxOUpVBot7GMPYDA7cLL3sqaNMZuKQa42IN/k2FYbrbT/tkOlt7C+FwDkChXMIw+QSadxefQexWyq5nU+ivqBgQEUCoWaYl+rdkVCK2+vZpBBWC6Xq0GRz4exsTHEYjGkUinW9zRLcgG330GxWMTVFb/Ytx63243j42Mkk0lR1KDStEWJbiEluZrQrM+dK1qtlpPxKbnhf/755zCZTHjx4gUGBgYEb59MJOs0xWIRh4eH2NjYgNVqhdFoFJSUEwrfAJbJOHZmZoZzdaKbSi6KolAoFPDy5UuUSiU8f/68Zjy5GBSzKaz/yt/Cyeb/1/R9/aMLKOfiyMUbDUmtE08QP92BXN3cPwAA9APzKFMKxE9qDUq1pmEeey1D38gTpJMpnH/4FPkktxt46iIAfT/37Zy//26DISvXIKtcLsPpdOLm5gbJZJLzNgn0KhzX6iMdui8WE83aFQmTk5O8TFiZAq6RkREkk0nE43FuO85AoVCA2WxuW3pPR2pblJD4asC14JdOp7G9vV0zwKWVJw4XOp3korfOEWsIkpTrtoqMDltsxtUaoh3ESjjR41uLxYK1tTXG+FbsJFez5BadmZkZBIPBmuPrKhTAb/39vwmKonB9VRuvqXVGyJRaFHJ3VgRsSi6ZTA6FxgBKqUHisrHY5Zx+ingijjLLPZSCDFdnd0mi63gciXjzSadlKJHN5pBLXSOXvMKrb/7fyMTv2v74Kurri318/JFtNhsUCgXvtkOidnI6nSiXy4KLc2QQ0P7+PutnbZXkIt+Bz+drK9YhSb9AINC2NyrZL6ltUaIbSEmuDtMqwCqVSg03/OHhYVGmCnUysCKtc69fv4Zer6/ud7cvWFw9N0j716tXr3B1dSXYo6IbSi56Iq5SqWB1dRXT09Nty4TruTzawe/91A8hGWvuheWYfoZ01IdSvtGjyTnzHKnQHirFHC58b2AcXGBch0yhgWH4CW5O3qOQbkxKJSMeQNY6+NDbpyAzDOFs/yVix9tQG7i38AKA0c59NHQhHcNloNYIXy6XV3+HZsc6mTRIWgqEnBf0tkMhv/3w8DBSqRRjgomL7JyYsO7v73MK4JnM4cVoWywUCtBqtZiYmGhbek/fL6ltUULiftMNy4hsNot3795VvaBWV1fR39/f9nYJnUpy0f1aSetcvTVEL5Nc9bFSpVLB8fExZ2sIoYhRiKQn4nQ6XctEHFEHtwtJbpGWPLbkFkGlUmFqaqqqGIpfnuPX/u5/jVz69p6fy9ypgORKNUz2CVyd+2rWIZM3xl0avQnGITcON76Hq3M/1Lpab67huVUc736GCgWUS8zHl3FgGtnEXaynUBtxFWJRdctksE09Q3D7u8hnb9sUc8krRLyvkYwGa97KR1Gv1Wpr1OR8C4Zut5t32yE9tqIr8oVgNBphs9lwcnLC+HqrJBdwe/0bGxuDz+dr+r5WkMFWYl3LpLZFiW4gJblaIEayiSnAKpfLCAaDePXqVUdu+J0KrOj7TW+da3Yj7iTkZtcM4lFxfn6Ox48fY3FxUbBHBVeptFDqE3FarVZ0s1iKorD7x/8E3/rpv4RExI/z/VcwOiYZ3+t0r+H6aAtUpfY7limUcEw/w83RBkC7QcWjR1CoayuDWpsLMo256eTEYjYO49AD1teVun7oBh8iHHiPeNhLPgj6nMz7zUYh09wrDAB0/cPon1gB9MPwvv7Dhtfpai62IItUDA0GQ9MgpRkkQRQIBARdF+gJpvr95DKlCLg1YTUYDAiFQi3fWywWGY9VvV6PgYEBwa2bhUIBarUaTqcTpVKpbek9gQRZQpR2EhISnUes4T9ManrSgra9vd1RiwWxY7FcLoe9vb0av1Z66xwdsadP84EUIOnWEIVCgbM1hFAUCoXgJBc9ESeTyXjF5e08qNcntwBwjqmdTieuzn34jf/nf8TP/o0lhIN3A3eyxCtLJoNz4iHCwd2G5f3v12Gw3NlFGK0jKECFy+N9VMol5NNxGCwDcE4/hcP1EAbbME4PP6++v1TMwzG11LBevfEuUUxRQOLyDKmbS2j6as8xuVIN8/AiTnZvPVDjYT8UKg2KuRTiIS8SF42+VHzaFkdGRpBIJJBIJDi3KxJUKhUmJiZ4tR3Sk1xiJJiaDQLikuQCboch5XK5pj6tXLBYLIjH46Jdz6S2RYlOIyW5WiD2COtKpYKTkxOsr6+jXC537IYvdmBF3+9KpdKR1jkhNAvikslk1aPiwYMHVY+KdmgngGoGMbk9OztrOxHXjFzyCt/9+f8Wr3/rJ+8m3FAUSmUZgLtjXa5UwT61jFjgTcM61HozLENuxI8b/bfyqRi0tunb1UIO48gSkuEAcjetJyemkgxSdpkCxtGnSCTiiHheN7xczLH7FTBuI+qHrn+w4e966yj6J1ZAaQdw4juAd/P7uDw5wMnO9xjX00rNRU8ikSAlk8kwvrcZWq0WNptN0LIAe4KJTyv29PT0/8/eewZHtqfnfb/OOaORGxkYzAwmJ9xd0kEyaZdJ2lWSKFolu0zbsimZLlkqaz+YlCzTlkSWlpRIWizKRalKNEkt05Jcksu0u+SmeycjDWaQcwY653iOP/QcoHOfboS5ZPXz5d5Bn/BH43Sf9zzv8z4Pu7u7dceuaxVcHo+HUChEOFx7XKESJJJLoVAwNjZ2bul9IVKpFG/evGmNLbbQwqcUF91olNKJJYuFaiNoF4WLqsUKU5WdTqcsv9YPqeRSKpWcnJw0bQ1xnvM2WqOJosj+/n4RETc4OCi7Lm9WySUl1jVDbkmY/rMvMv/HP8+bb/5mkapKFMHi6kGl0WFydHO8U9lfShAEbO4+AHTWdqLxGMmwt+g44ZCfVDLJ4cYbwt49xIL3N5WMcXK0h8HadvozpVrLyfbC6b+dPSOEjvNkldHZW6Qes3tucbBa3AA1Os5sJeKBcs+xRscWx8fHWV5ebsr6odHmWqlKXiKYAoHK5vz1UCsIqJ63qoTC0c3z1Dq5XI7u7m6WlpaaPkbpulpjiy1cJlokVx2ct/BRq9VkMpnTm+izZ89IJpM8fvz4UkbQJFxUYVVp3UNDQ1XXrVAorvTLqhLJFYvFmJ2dZXFxkeHhYe7du4fFUh6F3Awuelyx0OR2fHycO3fulBFxFyXlPVx6ypd+7LvZmf1K2Wv+3UVsfflunMZkx9YxRHB7tmw7k6sfncFM9Kh6Z+tk7SWW3rtobb34N14iivLer2xgE43prMunMPeQ0zjYX/iEbLK8iwUQPlxueGTR4u4HwNTWj63vATmtm+2VBVanvo5vr3gczr+7QNRXbmgvFVmiKFa83gu9H5pNK5TgcDjIZDJNEUSQJ5gCgUCRYqmRdahUqqIY6WqoRXLVUpXVg0RyQf6Bta+v71yGrqXH1ul0rbHFFlr4lOKiSK5sNsvq6iovX77EbDZfaDpxLZy3FjvPuj9EUI5kDXFwcEAoFGraGqJZNKK2F0WR4+Njnj17RjgcbpqIa8aTq9B3SzpGo9fi66/9Ov/+83+b/c2lIuJIrTXQMTCBd3+LWDzJyeEuHQMTVY8TjkbRW93kBIFUQVKiKELfzUmiQR+xcGUVUOB4l7DvEJPzjJhy9U8UNSDVxjNVl9/nw9F3FwBn/212331Sdky9pQ2NwYLe4iKdCFd8bxtJWzQajbS1tRGLxRomuRr1tSoluaTm3MrKStOfRavVis1mqxgEJFdoUM+nVQ7S6TRtbW1NeZVVQ2tssYXLRIvkugJkMhmePn1KOBzm4cOHjI6OXoh5Xy2cV3FUevOXu+6r7hwWFnHJZJL5+Xnm5+fp7e29cG8NuDjjeclPY2FhgcHBQe7fv1+RiLsIQ1NByDH9pZ/ijz7//cQD1cfODhafYfbcR6XWETkq9z5yeG4hJAOkwuXm84WwdN8kmUgSO2n0ZipicPShNbvQt18ndLBCzFcef128S2MjixqDFVGpI6N2srU0z9r01wkc1F7ndhU1lySZr1T4lMrirVYrZrOZ/f36irZKx3K73U0RRHBWpEneYHJHFQvhdDrR6XQcHVX/29fz+TKZTLjd7obHFgtJLri4xCDIK7n0en0rbbGFFj6luIhGo9/v5/nz52i1Wj766CN6enquzF6hWZIrl8uxsbFxrnVftYVEIBDg1atX7O/v09PTQ1dX16Uo0mtBbo3m8/l48eIFx8fH3L17l/Hx8XMRcXLrtEJyS0pObobgevmVL/CFn/yfEQWBRCSIxdEO5AkuR+cg++vzAGRyIAiAovo9X8hmEFASC54U/bx/4jNsvX0GQDRwhFJV4f7+/vdWqXX58+uMRHxndaYI+PfOmlLHOysk0xn0FhfencoemwqNCYXOjvfokJe/8zP81v/9X1TcTqqx5JBHfX19ZDIZWUFgpdDpdPT29soiiCrVQReRND0wMNBQEFAldHd3E4vFCAaDTe0v1WKSV1lrbLGFTztaJFcdNFskiKLIyckJz58/J5fLcfv27XPfRK8KhTf/e/fuNbTuqya51Go16XSaxcVFpqenaW9vvzRvDTh/Z7TQT6O7u7umnwacn1SL+ff545/868z87k8hijWOo1DQNf4Rkb150JUTg+6RJ8SOFsmlq4/NiaKIvf8BgZ15fBvT6KwdVbetBgEV/pMTjteq+3eVIpOo76lksHdj7rmD1+tn7dUfk4rL92Hanv1q1dfUajUKhaLsmhAEoYxIGhoaYm9vT1bSVyGy2SxGoxG3283WVrk/hRxIBqbb29uykhUrYWRkhO3t7aqFjVSw14KkKqsVi12KUq+vi0oMgjMlVyttsYUW/mJBEAS2traYm5sjk8kwOTlJX1/flVssNEpyFfpCAR9s3Y1AsobY2NhgfHyc27dvYzAYrlxFBvVrplAoxKtXr9jZ2WFiYoKJiQkMBsO5z1mP5JKbmFjvGIuvvsbP/W/fw6/+5A8XjQ0azHbUWj06SwcHG++K9wOyVQzitXoT8VQWjd5c9PPe60/YnD9TWYkimB0dKFVVagelClEUcXpuEvGdNfPaB26RiReH3/gOt9GY3SRjxSN8WqMNW99dFl78CQpdPkRBEEQC+5U9rRoZW1Qqlej1epaXl5tqHHd1dRGLxeomRVdr9hV6gzUDlUrF2NjYaRCQdA01gsLRzWY+m1ItptFoGBwcZHm58ghso2iNLbZwWfj03jU/JWiG5PL7/bx8+ZKDgwNu376N0+n8IKbsjY4OSgbtu7u7pzf/RrtwV0lyZTIZNjc3CYfDWK1WJicnaW9vv9T3ulnSKZPJlPlptLW11V3reZRcO7Nf4Us/9l0cLj2tuZ3J1UNb33V86y/JpZNEDxZRu66BQolCqaZ9+BGhEoP5MihV2Dz38K69PN1OZ++RvVadxY3GMcTu228U+THIQfhwGZ3VXfE1S8cYevc4e5srbM9/TC6d7+I5e8ZkH3/v7TfJZSs/pEhFViXJfOnfVu7YXymk0UePx4PP56toQCoH/f39eL1eIpFIU2PSarWaoaGhc/kxyInFLkWpkgvODF3Pm7aYSqXQ6XSttMUWWviUotH7uSAI7O7u8vTpU7LZLJOTk2i12kszOq8HuebvlQzaG/GFqobLtJAotYYoVKR/KNP7alMM0WiU6elpVldXGRsb4+7du5hMpgpHaBy13uOLILek43zx//kcv/Cj38/6fHlNp9WbEFESKkkjlJBMlDcoFUollo5hjnfXicSSuPvz6dhdI3fZWSz3Y7W6PbQP3Kx4fEEQEZRa9pbOvFNF8v5cZWs1OQkelTfszO0j7C5PAZB9X6sJIsRDJ6f/LkUtRX2lba1WK3t75RYU9SCXIKpGckn7N5u2DWCz2U4nAuSazpfiPKqywkam5AfYGlts4dOMFsl1gZA6RNvb29y4cePU6LxehPVlQW4HMRwOF3Xh7ty50/TN/ypILknG/+LFC/R6PSaTie7u7ishEhtVcmWzWdbW1njx4gUmk4nJycmGfECaIdWS0QCvf/vzfPVn/9sif4VK6Lr2EUIyRPigoCMjikT35lFbB3AP3Se4PV3zGGq9FaNrCP/mVNHPg7tLFeOpS2HtvUMo6Cewm+8+as2NeWwBmF0DZ/9QKLH13kZh7mVrcYr95VflBJ0gv/jOJGPsL3xc9fVGkn6cTicajaahwkAqmgoJomaKAMkbbH19vekHp2p+DIUeZPVQqCqTg0qqOMgbuqZSqXMlBqVSqVMCTerGt8YWW2jh0wO590pRFDk4OODZs2fE43EePXpU5Hv6oR6c6q1fFEUODw8vzaD9MmoyOdYQH8IPDMprpkQiwZs3b3j79i39/f08ePAAq9V6oees9De+KHJLwqs//Q12Vsp9UqVzCUodiXgMo6XyZIDvYAtjgTk8QNfYYzaX8sf0Hu4QS6Rp6x3laGuxSCUm4Wh7FbW2cnhTIhElEE5heJ+e6Oy5hqN7lIOVclW+UmvA1DWOxnBm0WFu87C1dJbamIwVK56i/upWD9UU9ZUwODjIwcFBU2OLBoOhblJ0LdsGo9FIe3t700nTcDYREI1Gm7a9kVRl9VRphaik1B8bG2uNLbbwqUaL5KoDObPykUikrENkNp9Jfz+tJFcsFmNmZobl5eWyLlyzuEySq5KM3+PxXKlKTi7pJI1KPH/+HJVKxUcffURvb2/DIweNKLkyqRjTv/fT/NrnHjP/1X+D1lR9DNLk7MY9MIFv4yW5TPnN3uG5STp6SDTorenlYHD0gEpP+KBc3ZOOB1FY+qruq9ZbMbRfZ3/peZGxfORonUYfR1JRLyqtAXvfA9IKMxtvPsG3s1h1+8DeIiqN/NHhjdd/WPN1qdiQU2SNjIywtbUl+3NSSCBZLBbsdntFA1I5sFqtGAyGcxE5kh9D4fob7SpKqrJ6qrRa177UGT1PYpCk5JLQGltsoYU/Xyj0Dw0Ggzx48ICxsbEi9eeHTBmUUPpdJhm0P3/+HL/ff2kG7RepqGrEGuJDKbmkhlMqlWJhYYGZmRk6Ozt5/PgxTmfjDTQ5KKzTLprcAoiFA/z2z/8o3oNNLM5yG4iBic+wOP1tcgI4OvsrHiOViNDWm1ewG60u3EP3WJopVoQd7qyitXRWVU3FQl7SqXztoNbo6L3+GWzd10BnY33xDQJgsLnpv/tdeHeXiry4JKh1JnZXZ1mc/hh7z/XTn2stHUXNyLBvD43urNmeCFf34KylqJcg/X2aVdRL8Hg8BIPBoiCf0vPUqvM9Hg9+v78hy4ZCSOvf2NhomghvZmyxUpKjNLZ4UUFArbHFFi4aLZLrHCiUag8MDFTtEOn1+qa6BudFNZIrkUicduE8Hg8PHz68MIP2yygmzxvvfJGQ05WVRg4ymQxPnjxhYGCgaT8NOaRaLptm/iv/hl/73BNeffHHSSfCpGNBjK7eijfxrmuTCKkIof1yEkihVNIxNknkYAEhFSF8uIL9fepiKSxdN4iH/SRD5RHPEjSayjdhS9d1EqksJxvlKrFU1Ieta7zqMUuh0uhRG5xkVQ7WZ79B1Fff3D2XSeLy3JB9js2pP6rY2ZTQSNKPRqNhYGBAdmFQ2hk8rwGpy+UiHo83vb+0/kI/hkZJLrmqtFwuV7OQkwxh19Yqe3bUQ+nxW2OLLbTw6UKte26pefj169eLSGsJH6rRKKG0LpKsIfb397l9+zY3bty4NIP2i6jJstksKysrvHr1SrY1xIdScomiyO7uLq9fv8ZutzM5OXk6WnVZkMYVL5rckvB7/+bHiAa9RIJeXF0FQTsKBf03P2JlLk9WZQXIZqvft4KBE3rGHxOJZ1h/NzdYq+YAACAASURBVFX2urtnhJkX30BnrN7sDp7s0T32CIXexurcJxxtLZGMnimC9LZ2QjWMzfWuAXKZ/HNJTszXxWZXL9uLr4o3FEVsnYPoLXliMh72kopVVx7VU9QXKpEcDkfdIJ1qKA3yaRRKpfLcY4tOpxOVSnWu50qj0VhXlVaISrYRkB9blDyoLwKtscUWLhItkksGSm9ShSSRJNWuZR7+aVFyFXa2Lsug/SJJLlEUOTo6khXv/KG/DKW1XvTIQS0llyDkWPn4N/iN//2zPP2VHyURLr7JeDdmcI8+Pv230d5J++AtfBuvyGXKCQ6DvQN71yi+9RdFPz9YfoHB0Xv671OD+d135FK1lTihvQV0ljO/LKVGj7nnDger06Si1cfMVFUk8YVQKNXY+++TSCvYnv82+hIpfj1odPLNZuOBQ443ZmpuIxVZmUymbmHb3t5OLpeTlRCYzWaLCF2VSsXo6GjTY4uCINDR0dF0JxPy65eUCJAvgBqVzlssFhwOBzs7O1W3qVZYFaKrq4tkMkkgUHs0txTVfvfW2GILLXx6UOm7tJJ/aC3z8A9Nckm1mGQNsbm5WWRpcZk4T01WmPCo0+mYnJyUbQ1x1Uouaa37+/toNBomJyfp6uq6dKW/KIqIongp5FYiFubNJ3/AJ3/wi6c/U7xX1tvaenD2jLE8+0nRvez4qIbKW6EiGIwQj1Ymi7TGvNm7ze0pe61jcIKe8UliKYF0FjJVPk+xeBK/t3rjM1tQM8bjeTWT1tZVMRgpk81h6xgCYOfNN3j5O/+y6nGhtqK+1FKhXpBOLUiWC7Vql3r716t96sHlchGJRM71vebxeAiFQrLM8GvVYmNjY2xsbFzYs19rbLGFi8LFDP3/BYd0s0qlUqyvrxMMBhkeHpbdHfrQJFcmk2FjYwOv18vg4CDj4+OXduPXaDQXUtj4fD5WVlawWCzcu3evZpdT6hhelIdFoyhc6/379y+0I1sptUcURbam/4hXX/xxAnu1TcAPl57RMfIIjUZD+GCB4N5Cxe3aBu8RPV4jclTejRGFLDnFewJDocLuuZ03mJcDUUTv6CMVOcHkHiIaDnG4/LzubsG9BZRqPUK2UqdKgb3vDv79LbbfnHllpcKNdZJChysolCpEQV63efPVH9AxfL/q65JkPp1Oy1LujY2NMTs7i81mq3ntVrq27XY7JpOJg4MDursbM+rPZDJYrVZyuVxT+xeuf2ZmBrvdXpaAKBcDAwO8fv2atra2ig97ckguqbM6NzfHgwcPZCs8a6VMSmOLWq32g32vtNBCC3lIzZ5wOMzKysqpGkKuvYJOp/sganoJCoWCd+/eoVQqGR0dxWazXdm5myGbJPP+nZ0duru7mZycbFg5f1VKrtK1DgwMoNVqLz2NUiK3BEHA4XCwurrakN9qKRLREKtzH7P25inr889RqlSszz8r206pUpPOiih1Zg42y9X4ibAPk15bFpajUmsIReI429ornt/W1s36Ql5NpTFY0RktpynU3SP3WClQfoUsNhydAxysFXuEOToHWH3zHLVWj63CrdXRPcL+1pmC/Wh3jZ72XrYrmNwDHG+9Y+DmRwCsPPsS3eOTFbeTICnqs9nsKdkoobRZqFarT0ftbt6sbKZfC/39/bx+/Rq3231auzSSeCjVPoX7NwKpWbm8vMzExERT151UOy0sLHD//v2an5latZhWqz2dTrhxQ/6ERK11SWOLkh9tCy00g9aVIwOZTIbl5eUi+XMjKX56vf6DkFwqlYqjoyNevHiB0Wi8ks6WWq0+F/seDAZPO7S3bt3i5s2bdUmjD+X90MxaG0Vpas/+wsf87j/5Hr7ysz9Yl+ACcPSMI2aTZFNRsqnydB2VWkvH6GOCO7NkU9U9AoJ7i9gGHmJ0D+PbKDcSrYXwyTaW3nscbS0S88tLtcml49gK/BokWLpukFLa2Z5/WmZEGj5ax+SSn+iYjgVp669/Qza3eXCPTrKz/q7utgqFAp/PVzXhqRDSqN36+nrN7UqLMwlDQ0Ps7u42/N0ikTvDw8NN7S9Bq9XS19fH6upq00k/9cYW5SrE9Hp9w2OLhabzpWiNLbbQwqcHsViM6elplpeXGRkZ4d69ew35h36oGiwej/PmzRsCgQBOp5OHDx9eKcEFjSm5Cq0hUqnUuawh1Gr1pZJc1dZ6FeeVfLek89hsNiwWC/v79a0SCpHLZnj6h7/Ev/6R7+dH/toov/B//E3+9Df+FZsLL1GUPNibrC76bkwy/+rbZHKg0ZsrBl6LokjvWLnFRM+1RxztbaJUVb6f2jv6EIX8AWPxBG39EwAYzHb294qTEI93VtHoyokZndmBKIpkUvn9pWOcvV48OZKKR1G7xmpaQfD+eSUR9hLx1vciValUp0RXIQRBKLuOpVE7SZHeCCrVLo0E8Jw3SCiTyZyOLZ5nVNBkMuF2u9naKk+7LES9hmN7ezuCIDT1XlZCa2yxhYtAq0UtA+Fw+JQkaoZRrvSFe5nI5XLs7Oywvb2NVqvlo48+ujImvFlpfCQSOfUoaqRDCx9GFj89PY0gCFy7du3Ck3oKoVAoSEUDLM/8Hnvvvs3qs9+StZ/J1YPV2Y1vM+95pTXZ0VvbSYbPUvHMbX2oVEp8G6+qHeYUalsvwZMjhEhjhucaox1BoScWDkAFOXotZArM6C0doySTWfaWahNsVncfMZ/8eGidofw6U6l12HvGEdV6jve38p4F730L/AcbOAs9Md5D8iTY2NjA4XAwNjZGLper+7nr6upiZmaGUChU9eGnUnEG+et+eHi44U6e5PEl7b+0tMStW7eaIr87Ojo4Pj4ml8vR0VFuiCsHVqsVq9XK7u4uHk/xmIQcJZeErq4uZmdnCQQCNcfHJZSazpdC8vdIJBKXPlLUQgstVIfP56O/v79p43CdTneuFNZGkUwmWV9fJxwOMzw8fOXEViE0Go2sgI/j42PW19dxOBw8evTo3Ab4chOHG4V0r11bW8Nut5etVbIMuAxIyi2pgVUYTDU8PMzLly9xu92y37sv/7t/xtqbp2y8LVe3RwPvyQKFgsGbH7G1/IbVuTNl18a7V+iNxorNy2ikeBzRaO9gYTZvQ3FytE/30AT76/OnrxvMNjYXz+wYVt9NM3IjT5RZO4fwviuvu0ppB1fPKDtLZ2qvlKDCaDgzjldrDeyvvy3aR6018Pb1t+hytxMLVk6czr7379IYzCiN9e1VlEolarX61CNNqp2qTXsUKtIbVW1brVYsFgt7e3v09vZWNGdvZP9GIDUWR0dHmZ6exm63N/2Z9Xg8TE9P43a7i0LTCpFOpzGZTBVfkyC9lzabrenkx0KoVKrTQLda4+gttFANLSWXDLjd7qaS8SRcVfqfIAjs7Ozw7NkzBEHgwYMHVyLbLkSjJFc8Hj817x8aGmoq4fGqZPHxeJy5uTmSySR9fX2XEkUtIZ2IsPb0i6x86R/x5X/8nXz8i59j8+WX6ByrLdfWmmx0j39ENuo7Jbggr1oSUWJ2DwDQPvKIdPSEmG+75vFEwDn4iKR/l8jhMrq2Edm/g6l9iGQ6h2/nLUpN4wq38MESNs9tDO3X2VuZw7dTX0mVCjXWRQodroBCgdHRiXv0CaaeO/jiAktvp1ie/YTgSTFhtvrqT8qOEQgEePXqFV6vlzt37jA2NlYkX6+FwpSbWttW+w5xuVwNd/IKjexdLhdqtZrj48oFZj1Icne/33+u75nBwUEODw+Jx4sL9kZILmktKysrsr4P0ul0TZILWmmLLbTwacDAwMC5kvGuyjIinU6ztLTE1NQUTqeTJ0+e4Ha70el0TXn/XATq1WSSeb/X6+XevXuMj49fSMLjZdS9fr+/LGigdK1yVNSNQk5iYmHTSQ4WXv0pX/3Vn2Z3ZbbyBgoFCqUGZ9cISzOfkIyXp/lZHe4KO+ZTEtXafM3l6h4kg47s+2vgeG8DQXlGQGi0euydg6epiRL2t9fw3PiI9QoEF0AmncLs6KDv5mfoGLpD0Ffsw7W1NEsyeXY/dw/eIZUo/h0snSOkUwkM9uoNstDJPq6+CRKCgcXXX5el6pHGFgsJyWw2W7FG0Wq1eDyepsNrhoaG2N/fJ5lMNmWbIu3fqAeopMi/iITDQlVZtc+OnFpMq9XS399/oWmLXq+XVCrVSltsoSm0lFwycBE3a0nNdRn+LqIocnBwwObmJm63m8ePH6PRaBAE4col+nILjGQyydraGpFIhNHR0XMZ4F+2kiuVSrG2tkYoFGJkZIR4PH4pndlsOsHu3NdYf/Eldue+Ri5T7iES2H6D2eUh6is2rFRpdHSMPCC487aqX1bMv4fB2k73xH/M8eI36q5Ha3aiNbdzvHLWZUxEw8j5NNj77rK/8hrhvS+Ed2serUaHkJV3PSqUKmyeuyTTOU5Wy1OAqiF8vF7x/akEvcWFuX0EbINsvHkKO/UVYMuv/ojH3/dDQF59uLq6ilKp5MaNG0VdrsK0Renf1WAwGE5TboaGhsper1fUSZ08h8Mhq3tWOlpYuH8zDzc6nQ69Xs/h4WHTD6IqlYqxsTGWlpa4e/fu6XduIyQX5N/Lnp4e1tfXGR0drbltKpWqq9AqHFu0Wq1X1rBooYUWznDez91le3Jls1k2Nzc5OjpiYGCAsbGxojVXS7q+ClSrj4LBICsrK2i1WiYmJuqqND4kQqEQKysrqNVqbt68WVVtAvISqeWilnKrEtrb29nf38fv99e8F4b9R/zyT/xtADLpJHqj5ZTEcnUNkM2K7KwvoVJSNrZYCH2VFMRsJoPn1hNSqQyLcy/Kaoj1xWkcVgsqlQqDrYPNxfKU60w6TTRe+TOj1upRqA2cHB+i0pnx7q2WbSMIAslEnuRyD0ywPv+0bJtYOK+uVGkq34eVKjWGtj5i6QyxSBAhm2Fz/mPa+8Yx2WoHDUnPIpKivpoiHqCzs5Pj42PZKvDS84yOjrK0tITH42n4GU/af3l5mdu3b8v+rius49xuN0dHR3i9XtraGgtgkiCZ6W9vbzMwMFD2upymIOQ/A8fHx/h8vgsLNpOCgIxGY6sGa6EhtJRcV4TLKLIkifmzZ88IhUI8ePCA0dHR0y++SqblHxrpdJrFxUWmpqZoa2vjyZMn5/4ivCySq9CLzel0nkZRX6TnQzaTYmf2K3zjF/4XvvD3bvNnP/8/sfX6yxUJLoBcJolGo0EtKaMUCjrHnmCyOvGuvihKrilF++gjEDN416fQ6Gsr0Gw9N8imswR3i9VTCd8mRld/1f0UKjXW3rvsLjw9JbgAsqnKHluVYHT2ojR1sf3mW8RlengVwuKuLvtWKJQ4++9g6p7g8PCYlZlvNfQluP32E4K+Y+bn51leXmZoaIg7d+5UfEAolMnXg8fjIRAIEI1W90WrBo1Gc2r6KQelvhEX0QmU4qyDNaLD68Fms2E2m4t8TRoluQC6u7uJRqN111JvXFFCK22xhRY+LM77YKNSqS6lFipMH5SsIXp6esrW+yFJrlIlVyQSYWpqivX1dcbHx6vevz4NiEajzMzMsLKywujoKHfv3q1JcMHFjEnKUW5Vw7Vr11heXi5bw9HOCiuzHzP77d/nF//Z/0gkeKa+NlnzNXD/+EN8x8ecHOT9kXICnOxvYXF2VTyXVl/57+bs6COeVbIw+7zidS8KIr1jD1BozexXMK8H8Fy7h1Jdfn90dPShNztZmvkWmZyIrgrRBhCLBFGqtcRi5SOVnUO3CbxPggxFyuseS1svWucAK7NPOdldxWC2A/DJb/8cW2/LCbNSSEFAkCfcaokMFAoFY2NjrK6uNnXtOBwOdDodXq+3KSGDw+E4bRTKRWkdNzY2xvr6+rlGdfv7+/F6vRXHm+X6rkrv5dra2rmfy6S/WSttsYVm0VJyyYDUvTlPkSQZn9a7QcuFz+djdXUVk8lUN33wQ0AUxaKCoLTTee3atQtj5C96XDGXy7G1tcXBwQH9/f1lXmznOV82ncS/NcPx6nOOV18Q3Fsgm1UQCxzIPkbkZAtn/518ek4uhb+OEbzJ1YvebMe/caaIcg8/JLBVQSGlUOEcuF+k3ipFSqh8o9OanKCxsL9UuQBJyyB57f332Vt8dUryRU62sLYPEPXWHqssRNxfbv5qdHRhcPRxsLXI2nyx0i2wnx9ZrOjiWgIhl+Wbv/9LfPZ7fxCn01nzGpaKrEwmgyAINdVc0qjd0tIS9+/fPz1uvf0ktLe3c3R0JLt7Vrru83YCc7kcN2/e5O3bt9y/f78po2LIS/clUtlgMDRFckkjoPPz8zXXUst4vhSttMUWWvjzj9K6pFk0mj74aSC54vE4q6urpFIpRkZGGlasNItm3vNEIsHa2hqxWIzR0dGGFMLnUXI1qtyqBIPBQFdXF5ubmwwPDyOKIn/yKz/Fiz/5Aif75SEzbd2DCIKA3mRna22BdInHVjadpL2nn4i/vEbcWZ1HFE+92QGwu3sIRhIks9VrpvaeIZbezWExVr7/qdQatjdWcXd00j18m/21OSCvrBIVKgInZyblao0Os91NNFhumRAJ+rA7nOjMTqB4HDArnC36ZG+DgeG7+DbzvmCWtl5OvH5SiTzZkogEsXpGiAaOWJ36GgO3Plv1dytEYdpitXFFCQaDgc7OztO/W6MYGRnh+fPnTadVDw8Pn445y2m+laIwCOj6dXkN5VIUji0W1qHQ2OdYp9PR19fHyspK02uBs0ZkK22xhWbRulKuCBflCVGY6DcxMcHExERNguuyzD9roXBkUW6n8zy4KCWXIAhsb2/z9OlTlEolk5OTFb3YGimiMskoBwvfZPZ3/zlf/Rd/jS9+7hZf++kf4M3v/wuOFr9NKuLD7Kzsq1AJGoOFjrEn5FJhDGYbkeONqtsqVWo6r31EJuoltLdQ9NrJ2ius3cU3H72tE529tybBBRA9XkWpKTaBNHeMEE8k8e9V7goC+Lfny9J1JOgsbehco2y/+XaZis1gr9zFrIaYbxd75zBKtRbX4H0MHdfZ295idfZbxCoUYomwj84B+bHHmeO3uFwuWdewSqVCqVTKuj7NZjMOh4OdnbNRy2rJipVw3u6Z1AlsZn9RFDEajXR1dbGxUf2arIdC6b/0wNEMYSY9aNRai1z5PbTSFlto4UPiIuqFZkNxCiEl+j179qyh9MEPlQAN+XtIJBJhbm6O7u5uHj58eGUEV6MNwXQ6zcLCAjMzM7S3t/P48eOGR+Cb8eQ6j3KrEjweDz6fj1AwwK98/of58r/7p5zsr+PqOlPB9127j71jgMOdDbz7W8QjQTp6K5Mr1R7qU4kYVteZn5XV2U48mSPkP+Zob4OhWx+V7WPvGODk5Jhw0Iuza6DicfvGHxDyH7O6MIdSZyr4+WNO9kqIOoUSo6O4RjM72hm4+RH2zkGOjo5QKIsbQ9a2HraXz7zIUokoPn/w/eFU5JTGU4JLgpTmKAo5gke1kwALUViD1fuc9vb2EgqFiETK/c/qQa1W43Q68fl8De8r7S/X060a4dTR0UEmkzlXyIbFYimrQ5shqqW1NPt+QN7WRqrRWmmLLTSDVktaJs6r5DovydVs+qDUQbzKZAq1Wk0qlcLn87G9vS2r03ne851nlEgqXDc3N+no6GBycrKmWkMq3ERRJJeOk01GySTDZBKR0/8er89ysvaCwM48olC7yAvtLdB17QkHS1XIJYUCS+cYKFQkvKucrOZTcmK+Pawdw4SPyg0zHd3XEHJpvGsvqp43HjpBpTWRS8ew9twisL+EWGVMshBiNoXCPAqB/PVo77/H3uILRKF2ES+KAib3IKlo8U3P3neXw/V50vHKncd4oLFYbp3FhdE9zMHBAb6CNKJaMJrkBwisvPoT0okoWoM8VWalpJ9qGBgY4PXr17jdbgwGQ0NGpjqdjt7eXtbX1xkbG6u4Ta3vsMJO4Pj4uKxzSseUCqCenh6mp6cJh8NNhzI4HA6Oj485OJCvbqyEnp6emsmVclVyElppiy208GFwESSXXq8nmUw25TtYmj748OHDho7zIXxk0uk0Gxsb+Hw+1Go1T548ufJ1SORevXtYJpNhc3OTk5MTBgYGGB8fb3qtjTQhpUaKKIqn97Hzvkdzn3yZ3ZU59reW+f2fnSF4uHn6mtXZSfDkEGfXIOvvzpT0wns1ViZbed2pZPm4nwSL3U3Ef4S7Z5hAKELAe3bfXJp7hru9m+BJvoYauvmYlXfTp4mFiUR5vadUqtjfPSORttZWUSsUtPddZ/XNJ2XbH+6sEg0HsFtdxMI+POOP2FqcIug7C7MRpLpDoaBn9CHe44OyWiQSPMGihI7RJyzNfFx2Hm1BUmMsKD9gSEpblJN2LSnqFxYWuH//fsOKIb1eTzwe5+TkBLdbfvNagsvl4ujoiOPjY9rb26tuV+0zJa1/dnaW+/fvN606l+rQtrY2jEZjw6mRpWux2WxNraXUUkIaW9RoNBcSjtHCX3y0SC6ZOO+NT6/XEwqF6m9YglgsxsrKCplMhtHRUex2e0P7XzXJJYoimUyG169f09nZeWqCf5lo1iNLKlzX1tZwOp2nUdS5VIx4YJNU+JBUaJ9U6JB0zE/g5IB01Ecs7GMvmyCXilYlsKx99/FvVUnNqYDw/jtMzm5iBaN2BlsH1o5BoiebxI7KuztCLkM2FUdrtJOO57tgap2Ztv6beNdf1T1nMnSMc/A+yUSyobUC5JIhFEoN5q7r7L4rL3yqIeI7K8DUegs6Rz/b87X3j5xsYescInK8WXM7vaUNQ1s/u4uviCenSEbl+0OFDtdkjyxm0wmWX/0xE9/5V2UdWxpbTKfTpx3iWtuOjY2xuLjI3bt3Gw6r6OrqYnZ2lmAwWPG7ot7xOjo6ODo6qmucW4hCskgaFXz79i0PHjxoWlYuSffP870rFVmVRiibHVtqjS220MKHwYdoNIqieGoNYbFYzm0NcVHjkrVQyQT/6dOnH4Roq6fkyuVybG9vs7+/j8fjKbOGaAZyphckUksaTVQqlec+ryiKfOXf/xRf+9Wfpnf0LitzxUSNzmBGREkqlSKZKPc8EkUwGivX6Se761XrE73RimfsHmvLi2VkmJATcHX2Ewv56R29w8JscdPv5KA8oKfv+gPeTp81XAO+I8ZGhoiGAxU/f0HfEQAGi4O23hHW3z4vW2Ymk6Hn2hP83kOW31RuvKbiETqHx1meq2x3kYhFsbb1Ym33oNYZiYZ9mK31rRmy2SxbW1uyrRhMJhMul4udnR36+6v7z1Y7l8fjYWNjA7vd3tSzj5wgoVr+WHKanfVQOLZ47969pmwjpLV4PJ6GG6cSSi1+WmOLLTSKVpUuExeR7tNIgVXoSTAyMtK0OftVeUGIosjJyQlra2sIgsDY2BgdHdVjgS8S0sy9XIiiyPHuKpvvnqJJndCuy5E9PGHx2QHJ0AG5ZLjifsbOCU723so6R3hnGvfAHU425ZFHuUwSm3uAROgEV/8thFyawO47TiLHNfeLBw+wdo6QyyRxem4Q9+/KIrhEUcTgHsO3t4a+KVWKQNbYy8Fy7dHGUkSON2jzjKHUGPEfbONfqq40K4Te6q5Kchls7ShNHRyuzSAe5I1MY4EDXD2j+PbkmanHQyd0DU1wsPZG1vbvvvVbskkuOJPMy+km2mw2TCYTBwcHGAyGhhSQErFTzY+qXkdO2n9ubo4HDx7IOrfUWZNgNBprpkXKgVqtPo31Ps+DodFopLOzk42NDUZGRorW3EzR1kpbbKGFD4OrJrkCgQArKyvo9Xpu3759bvWmNC55WQqEXC7Hzs4Oe3t7eDwePvroo6J7zVUQbKWoNqYpCAJ7e3uXovSvpeSSrh9Jia9QKC7kvIIg8KX/9x/yzd/61++PfzYWq9Ya6B25w9HOKqvvCRyHu5vAUTnBlE1VVtInYmE6+sY42i5udiqVKlRaA3OvP6n62UjEo7T3X2NxtlzVHgqc0NXZQdifrzPNtjb2tjfLtrN2DHGwPlfx+JAP9HF0DRI42a/YJ0ynUxwcHCImqo+uKRQKBJUBscLfTqnWgNpAJLbD3vQnKBWfMP2t3+P//I11VKrK9Yzkm7e3t0dPTw+PHz8mm83KUtT39/efKuob+dxns1kMBgN9fX2sra01RewUBgnduFHZRqOeCXy9ZqccWK1WrFYru7u7mEympr+3pOTKRhqnElKpVNmzrzR62kpbbEEOWiTXFUFugZVKpVhfXycYDDI8PIzb7T7Xh/gqSK5CE/y7d++yt9d4It55UM/vIhPzEfeuEPeuEj5YIH6yiiIbRQfo7IOcLMgjauKH83SOPeFQDrEjiihyMRQKJaJYWzqvMdiwtOfJgM6xxxwuyVdG5U8l0j42yfHiN2VdK2qjE1FrJXqY988ydQ+TCsocDVMocA0+ZH/5Jc6+iYbWCaBUadDYPGxN/XHF1/XWdnLZNJl4sQorsLuAQqVBLCgeDfZOdLYedhdfIOTKRx3NdrdskgsgW2VUoBJ2l1+TiAQwWOR7m0hqLjlF1tDQEFNTU01FUtcyUM1ms3W7i3q9np6eHtbX1xkdHa17vkoFl8fjYWpqimg02nTYhtFoRK1Wc3R0RGdnZ1PHgLzPRukIZSOm86VojS220MKfP+j1elnpteFwmJWVFZRKJdevX5dtDVEPUi120SRXIWHU1dVVkTCSaqTLVtWXolTJJYoih4eHbGxs4Ha7L0XpX43kkny3JDLoPKOJ8UiQZ3/4/xE42uVwe4mw75DjnbNaI5tKIorQP/6InbV5Vt8UE0yHO+UWEwDRUPUxPIvNyVHBv9u6B8gp1Mw8/zoqteZ0BLEUaq0BRY3Gmr1jgLD/GLVGh8bi4mi93Fd1d2eL9u6hMnN5tUZH79h9vPubeI8OyFR51hAUOnyHOwyPXed4u7Jva/fIXRRqPQqlqmhCQme0oHf0sjT3DJsz3zgXRIjHovgPNnH3jhQdR7rGtra2cLvdPHr06LSGroxDjwAAIABJREFUUigUshX1hUomudeJpJRvRhFfCClIqJr6rB7JJTUr37x5I7tZWQmDg4NMTU3R0dHRlBm+tJbx8fGmRiirJWC3xhZbkIsWySUT52WL643UZTIZNjY28Hq9DA4OnsuToBBarfZCDO8rIRQKsby8jEajYWJi4jSG+iIMXhuBVMCJokA2ESDuXSNxsnpKbGXjxSaMhe9qKriJpWeCyN68rHOJiWNQKKEOcQWQDO7TOfaozGtLBKwdI2hNDlJRP+GjNVLvkw+1RjtqnZlsqn4xbnYPojPZ8G3OEDlao33kIf7N6eo7KJRoXKOkvGsI4bNixbs5i97oIB0P1Dyf0dGFqDay8/bbAAR2FtBodIjZ2teX89pn8Tz4XkwuD2qDFaVGx43v/8eotAaUGh0KlQaUqtOIIEUuTWDuD/n4F3+UbDIv60/Hw7iHH+DbmMbo6EZr7WBn4QXi7mbV80YLEoDkIOndQK3Rk63gS2Z192J2dJMVFfiPD9g72OLdi6/w4C//ddnHL0z6qVdkSSak6+vrNb0ZqqG3t5epqSkikUjRQ5rc8cfu7u6aflaFqKROkIosKaWnGVl5Op3G7Xazvb19GtHdDCqNUFYrnuSiNbbYQgtXi4tQ03u91UmEaDTK6uoq2WyW0dHRut97jeKiG46NEEZSTXbVJJdU9xYq/e12Ow8ePDjX928tlBrPX0RioiiKpFNx9tfmmf3m7/D8j36ZdDKGo8ODr2Tkz9U9iFJrRKWATDZNJlXuFxsPebE42okEipX6If8Rao2ObKa8pkqnkthcnbi6+slmBVYXpsi9b+6OTHzE8mzlMb+Q/xizvfokyMHeJgBdw3dYnCtv4HYPXGN/cwmEDAql8lRppTda0Ftcp+q0oPcAi62ckOm7/pil2fxxNbrqtinxZIbNuSlu3X3AwepZHdvWP8HCdH70Mx4ttnw52l4+Jbmk0eL19XWsViv3798vq0saUdRbrVYsFgv7+/v09PTU3FaCVF81o4gvxdjYGLOzs9jt9rIaQ85n2WAw0N3dXaZibwQqlYqxsTHevXvX8OhmIaQRyrW1Na5duyZ7v2pNgdbYYgty0arOZeKiJJGlknFpXvzw8JD+/v4L8SQohFarlZ0UIooiCGkQkoi5JEIumWdk8q+ebpdOpfD6vIiiyI1BFzq9nlxmj3RIhUJtQKtMkUpfnDxeFEWEbIJsIkg2GXz/38DpvzNxP/rwMcsbCdR6Own/IdlEbcKm4OiojfI7tZnoCbaeG4R25ZFi2egxCqUKtc6CpWMIURSJHK0ROqisMErHg7QNP+ZwqXLBAmBw9IDGRPR4mWhBYy2wu4TW7CIdLZeEq20eyKVJHL4re03IprF0juBbf1n1nK6hh+yvvCKXPiu+suk4bQNPCG5NlW1vcPYy/L1/H/v4d6I0WFArFOgUyuKs6yoQVVqcd7+XH5j4S6x86wu8+I2fyCvjFCpsfXfZXXyJuFMexV2KqH8fZ88I/r3Vutvmf58E3SMP2V2ewdk9jNbkIJFMcry7ydbWDmwVF7Pzn3y5IZIL8kWDKIpks9m6HSiXy8XGxkZToQoSsVNqoCqX5Cr0s6rnrVWt4DKbzbhcLra3txkYGGj4d0in0+j1+tPEoYmJiXONLXZ0dLCxscHw8HBDyYqV0BpbbKGFq8VlWUYkEglWV1eJx+OMjo42pbyQg4siuZohjK668ShBpVIRDAZZX1/HaDRy9+7dS/eHlZRcF0FuAWwvvuLrv/mvmPv275WNBIa8ByiUCkRBzKuart1nee4p4vY6GhXEQtVT7pwdvWUklygIDN28z0qJL5VSpUalNZDMiLydLh87lMYNSzE4fo+NxWlSierG9UHvEfc/+58y9XFldb3Jkh93OznY4cadB2wvvEShVGHvGGB3tdjeIRLyYjZqEXJZHB39WBztbCyd1cpKdWVixtU9xPry+2Opzq5ls6ODlfmzujSTTmJ3thEL5WvcSDD/e4dCIVZXV9HpdNy6davmNdaoov7169e4XC5ZXnyFCY6NKuJLIflZVSKG5I49S0FAcpqV1SCZxjeTOFmIrq4uTk5OCAQCDaW7VvvMtsYWW5CDFsl1hSiUjBf6J/T29pb5J1wUKhVWoiiAkIJcElFIgpCEXDL/s1MyS0UuGcq/VgIV0CHxQpldchlAZSDmXwUxix7QA8HFb6NQG1CqDShU+f8q1QYUagPpWADEDEIujZjLIAppxFwaQcgglvxMobYS2qidkie9c9mED4Ori9hREiEjjxxI+jfQmN1koif1Nwa0yvJiUaFSozW50BisqDQGUKpBFBGELF22Pnbnv05q/bWs4/s2prC0DxI53ij6udHVi9HajndzuqIBaSYZwdI+QCriPf3CV2jN6O29RPZre4kFdhdRqrUI2eJrxWDrRKGzsPO2PO0GIBU7GytUqnUMfNcP0fnkr6C2thcRWllRRIGABnmx3IJCRVqt5cZ/8j8w9h3fz+I3f52Pf/Un0Jrb6qZVFsJid8siudRaA20DtxBURkJJ8C0t1N1n8dWfkkklanYnSyEl/chNW3S5XOzv7zfVhZcMVAtJpkaOU0oMVUOtY/b39zM1NYXb7T5VespFOp0+JcqkxKHz+Px5PJ7TscVUKtV0+qOE1thiCy1cHS6a5EqlUqytrREKhRgZGaGtre1SH5Skh+vzoNQaQi5hVM/S4TIQDofZ399HqVRy+/btpsfWm0Eulzv9fc8zlni0vcS//Uc/QDwSQGe0kIwVP+gLuSw2Vy8arY5EKsXSzJnVRDYH/qMd9CY7yVh5CI62St2QSRfX3FZnO1qjg3fTn+AZuUnQd1i2z/HeJjq9scx4XhphDPmPMFsdRMPlzd/O/mv4fJVrX4PJwuZS3lc2l8txsL+PyeairXuEtfnKth02dy8qg53txSnYLq5hFUo1nvFH7CwWN1S15jYgP8IZj52Z8uvt3WRLUpbbPWMcZOZJxiMEvUfMzs6e+gDLGS2WarBMJlNXUa9SqRgZGWF5eZlbt27VvY5EUSw6Xnd397lIJsnPqpQYymQysuqpiwoCMhqNBAIBkslk08Ebjarbcrlc3fe7NbbYQj20NH4ycRHRwjqdjmQyye7uLs+ePUMQBJ48eUJ/f/+FEFz5xBgBUcgg5pKI2Th6TRaHRYWQ9iPE1hEibxHDs4jRRcTEJqQOIRN8T2YVEic5NAYXxcN9NZBLYHSWJnkIiNkYuaSXbGyHdGiZpG+WxNEzhNQx4e0/I7r3MbHDF8SPZ0j43pEKrJAOb5GJ7ZNNeMmlw2Tju1j77st+H7JxL6KlT/b2iAKWrtoSWqVaj97hwdR5E4XWirnvIebO62htHlCbSCWSRLx7+HcWOFmf4mT1BSdrL/FtTJPwb8lSMJ0tJ4tarUKhzN8EjI4u3MMPSfj38W5M1UwA9G+/wea5gyiCoeMGopCrS3BBXkHm8Nwu+plr8CGhwAnereqqtcDeEn3f+d/w6B/8Ft/xz6fo/e6/g9rWUfH3zTRoGpxW6MgKWdR6KxPf/bf4r3/y64xNfk9Dx/DvLKCq0j0EaOu7SdvwYyIpWJp9ztrct1Gr5d0s08kYK9PfbGg9cDa2WNhlrgZRFOno6GB1VZ4arRT9/f14vV5i7wvHRtMaPR4PwWCwZhevFslV6G3RqGl0oVR9dHSUra2tcz0kSkXW0tISyWTyQsZlpLHFq36AbKGFFhqDUqnMj52l0ywtLfH69WucTieTk5Pn9j6VA51O1/T3VygU4tWrV+zs7DAxMcHExERDiqirVHLFYjFmZmZYXl6mo6OD7u7uKyG4JM+tXC6HRqMhHA6jVMprqlVC8GSXX/iRv0o8kieGTNZihZ/BYmdg4jPE41GUWhO+w2Klt3S3c3VUHnVLVUhYBDjaXjldc//4fWLJNDsbeR8rnaHy+ygIOfrGiuu37oFr7Kyd1X7O9t6y/RzubsK+g4rkF4BnZKKIdPMe7tI5dK8iwSWKIoO3PsPmxhpqTeV768HOBoFA8bmMVier786mAQLv0xpt7R42FsvtN6KRIB0DeUP2nY0VVr71Be7du9eQd55ara6b/CnB6XSi0Wg4Pq4dAgXlRLxEMi0vL9et9aod79q1a6ysrBSttZlm5ebmZsPnLzzf8PBwU3VcIfR6/enYYj3IsZQoHFts5v1t4S8+WiTXFUEaUZqeniYej/P48WOGhoZqPnCeklZiDlHIIgp55ZOQS5HLJsilAwjJI4TEPkJiFzGxjZjYQUzuI6aOENMnqMUI3e0myEZAocmPI8pedIo0DZiu5mKo9W55h84EMLZdl3/otA+lTn6hZDY2NgOfSXhRqvXo7L0YO29g6r6DvuMWSusAaYWJcCiId3eFo7XXBLenIZPAv/2G6MkG6Xio5rETgX3ah+STdJBPIewc/yzu4UckQsecrL2qa2AvIR4NYem+QWTvDdlkfW8vCVHfLiJ583dTxzV23n1MNlVZ5m5weXj4d36B7/6Ztwz8lR/B0HM971VWB8lctmJ6TjUkRMUpp2dy9vKZv/EP+Rv/9Pexdw7K2j8dD+HsLb7OzK5uOq99BkydrC3Nszb/nPT7DqiQzdA7fFP2+t48/bLsbQuhUqlQKBR1i6xsNovT6SSTyeD3Vx99qAalUsnY2BhLS0un30GNkFyFxFC1IqJewWWxWLDb7ezu7ja09sLURo1Gw+DgIMvLy3X2qg2TyURHRwfBYPBCSK7CscXzFH8ttNBCbZyXhMpms6RSKV6+fInZbGZycpLOzs4rG3NpZlwxGo0yPT3N6uoqY2Nj3L17t2FFLFwNyZVIJJifn2d+fh6Px8PDhw+xWCyyiITzQCK3stksgiA0TSzsr73hN3/67/IrP/G3+KV/8oP8/Oe+j5B3//R1vfFMidN/4wnxRIaFqY+JhALEouVKLQk6Y+W6dXf1DRZ7eb2cSkTpHblF3/VHLL55TTR0Rgqlk9XHDo/3zlRTJqujTGFuMBfX8hqtHp3eQDwaJhb0YnaUryVwvF/8A4WCZDJeRvgBDExMsjjzMYKYT1OsBP/xPkclpvttnnGyBdem/+QQk60NnbW9Ys27v/6OcDifgO7fnuPNN3+rqXuvVAfJuT5HRkbY2tpq6jNkNBppb29na6sxj1gJer2e7u5u1tfPLDoaVfZ7PB4CgUDTI4eZTIa2tjYMBgMHBzJDqqqgq6uLeDxeRnaWIpVKyVKNSePJiUSiVYO1UIYWydUAmimGRFHk+PiYZ8+ekc1m6e/vZ2xsrOIXlJBLkcvEyGWi5NIRhEwEIRNFyMQQsnGEbCKv0MqlQMgAakQhA2IGRDmFhACqxpKC9HodjVwmWpM8kgtAqWrg/RRzmDvlx/Fm415MHdXVWRqjC4NrBEPbNTTmXtKREApzN769VY7XpjhaeY53/SXhg8WKHleZ4DpqvXzSTUzLJ5sM9m4c/fc4WXtN+GhN9nieUmfGOXCfpHe9qhS+FqL+PTrG/wMi4QAnm9XjorsffR//4Y99FfftvwRVOnbVICgUIJaMwAppSIUhFUErptGKaXRiBp2YRalQkxPSkEuDmC9gXf23+Zuf/zO+7x/8W7SG+tezmEuj0ZnoHHuMuesGe7t7LM18TMhb+WatEOQXMvOf/EHVVKNaUCqVp98BtQpxqSt97do1VldXm3pgkKKg9/b2mhp7LPTWqgQ5xxwYGODw8JB4vHqRXopS01FJbXFyIm+suBo8Hg+ZTIZksnJce6OQFCLNeKe10EIL8tAsGZXL5djc3OT58+eo1Wru379PT0/PlZsVNzKuGI/HmZub4927dwwMDPDgwYNzjVdf5rhiOp1mYWGBmZkZ2tvbefz4MS6X69LPW6jcksgtSbllMplwOp01GytBb/61XDbD177weX7u7/9lXn/llwkebTP37d/Ff7hFz+hd2gcm6Bi8RTIZR6M30j54m8WZZyTjZzWd72ALZ3flkX5llbEsQcjh6io28zbZXAzd+gwqg42luRdl+2yvvKHTU9lEPOQ74vZH303/2B3i0RBbSzNFrydKRi37r90+TXlMxiPY27qLXr9297OcHBQTM8M3HnJysE17/1ktLooi/TefsDx3ZimSjFdWqUG+3tG9r9usri7WFt+UbWPvvcnqfHWP2PxzCZzsrmJv91T1JKuFRhT1Go2GgYEBVlaqp3XXMrP3eDz4fD5Z6a6V0NPTQyQSOSX3Gq3j5DQra0HyVx4eHmZ3d/dcYWYSCV2qTitFI+FA0tjih/AdbOHTjRbJ1QAaLbJ8Ph8vXrzg+PiYu3fv0tfXV/NDrVBqAfF9cp8cRloEdYNz3qrG5qlVShGVsQEfnFwMlb48YaUSxGwMvVN+6ocoxJE9PgloTDYUKi06Wy+GtjH0jmFUWhfpaIzw7gL+lU/wL3+L8PYUqdABeoN8Xx0xl8Hpka/4iXk3cfVN1NzG5OzF0XeXqG+Pk7VXZFMxbJ3yDCudA/dRKRT4N/Oyb+/mDHqLzL+DKOLou43e0UvEd0AmUf1GfOu/+r+4/d/9S1CpEQFlA38PAI2YRQFoxQyqbBINoFHp0ejtaPQ2RKUeUalHUOoQlFpEhZoM6lOPM94TvAqg/+538d//3Av+8//1Z9Fbyo0slSotps5raPRmBJ2Tpdnn7K3XH9082nyLTi+vWx4P+1l6/acNvQen6ytIW6xWeEhGpjqd7tTEtBkMDg6yv79PKpVqKg2wdOyxEHIKLimlR1KUyUEulytb6+joKBsbG+cqZhQKBTqdrukRgkpojS220MLlo5EaTBAEdnZ2ePbsGaIoMjk5icPhuNCEw0ag1Wrrfm8lk0nevXvH3Nwc3d3dPHr0qKpJsyjkSEf3iXvfEN79BtGDpwi5yr/bZSi5stksKysrvHz5EpvNxuTkJO3t7UV/I7kjYY2glNwCKo4lSve8Ss2Mo61FVl79KV/8mb/Lz/zwd/DVX/5xhFz+u1trNKPW6rC4+1lfmGZn9Q3bK3Mc76yQTsQJ+Ss3WezOyg3ek911tLrKtaVGq8VgttF//RG944/wBYK8ef0xIV910sbmKk9bVqk1jN6aJBmPsrU8W1Etv7v2Dv37kKXR25Osvikm0Y62l3F2DuRfv/WEpZlyH9ZELEzAe8jW6iJ6owVRFBm6/ZkiggvA793HYK7+bKI3WdHqjWTQEY+Gy173BUK4e6r7gIZ9h1hdXXQO32dpfopYuHGVO8hX1AO0t7eTy+Xw+cqb3lDbCkKybWik/imERAxJJJUcP9dSmM1m2traqjYrq6EwQEytVjMyMtL07yGh0JS/GhohuVpjiy1UQ8t4/hIQDAZZWVlBq9UyMTFxKi9PJpM1JZoKhQKl2oiQaYTtFxGVRhSCTIWEmAONAzKV1yGKIig0KJRq8oSSiFLMkUFVohYTpR0K6Lj8/6n1diKBI4wWO0qV9myMTcyBkEbIJkHMFxQanQm5egohHcbYMUb8aCl/OIUatd6KSm0gGk9iNltBFBCyKbKpGPHjTdLRKPETeWNS8eNl1DoT2VT1LlTRepKN3Vi1Vb6wTa5+NCY73o1pOC7unHk3p9GZnaSilc9l7hglk4qdkluna8umsXQMkYxUj0wHsHVdI5vLcrR2tr/J2UPMv1e0nVJr4Ds+95sYe4vVdAohnSegapBdSjGHTpEDhRpBqSMLqIQUSo08UjGn1CGICZQKZX7k9v3vJ2TCKNR6hh7+l3SOPODf/b3/CBEFpvYh1FoDx9uLRLbzXhbd45/BfyTv5p7Lpukdu8PafHkntRJef/XXuDn5n8nathRSJ7FaF7CQ6Onu7mZmZoZwONxwV1+lUjE6Osr8/HxTcdaFY4/37t0rS4iVQ5zZbDbMZnNDkdyl0Gq1px3VGzduNHUMQRBQq9W0tbWxtbXF4KC80ddaaKUtttDC5UOhUNR9uBJFkYODAzY2Nk6VRRIJXy1h8TIhigJiOoRaY6tKNKXTaTY2NvD5fAwNDXH9+nUUCgXZpJdUYJFsKoRKY0KltSOKApnoDpnYASq9k4R/6fQ40YOnWLq/E2P7/VNPT7hYkiuXy7G9vc3+/j4ej6dmaNJFKrmkv3sulzt98JZrGn779plf1c7iS3798z+Eta2b9fkzo/ie0ftEQkHevfg6KiUQKyZfBBFERNq7BwielIzxwWniXylCvkP6rt1jeynvL2V3d2N2dpNIJsmIKnyBEF5/ca2xv7lIz+A4e++9uApxuF3sz2m2OrC7u1h584yugerTC4KQo2fgGtGwn42Fcq+rTCqB1eFCrdGw/q48JKl3+Mb/z96bRzmyoNWdv1i1L5kp5b5nZVbWvr21aAy428Y+MGCwjRkMPsZgD2AfY2bAA8ww3oYBG7c9jN0sXo4N9AEMbdwNGHfjbh7d/Zaq915VZb1ac98XKRfta2zzh1JKKRXKlLIe2Abd8/K8U4pQKBSSIr643/3uZWOxlM6dSuxz+ebrIIg8tyPD0kl6pq4RDBXZXql/DwgC3RO3ePJ+vadpV98I80/uceHaSw3fiz/UjyWo5A7HRGO7WygOF+H+0YbPsUNZUV8sFjFN81R159TUFA8fPqwkDlajOlnRDj6fj46ODtbX1xkebsEz+BDlsceyt9ZZaozh4eGWg4A0Tat5r52dnUSjUSKRCL29vS3vQxnlejYejxMMBuuWFwqFlsz622mLbdihTXK1gNN+NKlUqiJnnZ6erjNCbKbAEgQRQVSxWvHOEp1YZrYFTY0MlgCiTFE3yefyKIqM0yEjUCyNkx1rbEiOIHqmSU8dU0MUDbR0fSFQhiCqiKoHDB3VP46eKxcUVsVYvUSfWYf/WRi6TiajoeVB0NKYWhY4eo2ETYPN2zdNfOnkZMYyLKNIcPAKe4uNZdLVyMfW8YZHSO82N2uf2HyCu7Of7EFpn73hMSSHl/2VmYbPMfUi/p5xdo+RXKqnE3/3aMmIvgH2lx/gDHSTT9R3Bb3hMUTVZTuW6A0N1ZBc/oFpXvvB/4BoM55pSSoOS6Nw/FRiWTgFHREBQ1QxhVoz93w+jdvbvHKuaAk4q77goqwiyiWlmqmlECWZP/9//Qqf/HvfTnq1PhkxGV1p+rUA0JofZXty57PkMklcntbHSaqTfuyKrGoCqSw5P2tSTlkRsL+/T39//ylr16M89rixscHQ0FDl8epO32loNpL7JOl/d3c3kUiEvb09QqHm1IrVKHcIh4eHefDgAaFQqCXT2kZopy220cZ/O5StIRYXF+ns7OTll1+uS9z6wya5LFNHi81gFvYQ1A48Dv1wX02MXBQtu81eQmBtJ83o6BiTk5MIAmjJJQqxpxj5UpNKUALkk8t12zdyUSTVj1Es1U+mliGx+lmy+8+QnH0Ehm4jqZ7KNeZFYJomm5ubrK2t0dfXx2uvvXZqw+TDUnKV1Vvla02zQVChUIitra3KtWJn5Sm/+Pe/lVw6jvvQV8rh9hEavFDT1DJMCIf66xRClgXJhH2DOLq5iOJwoRXqx9bL9hH+zh6yBYPNR6Ua0+UNNCRtvYEu28cTB1H8HWEKuQyj09dZX3hcIZ8i64vIitrQQkGSFXRdQ9fsfwNut4d0IoZh1BOTxy0wREkhk6lXYZWxMveQ6auv1jzmcHnxdYTQJRf5oj356fR2ASsUCvXfV1GSGb7wCs8fvI2vI0zPwCgAD770W1iWxXf80E833J9GqFbUy7J8Yl3lcDgYHBxkaWmJqanaoK2ytcRJGB0d5d69e4RCoTPVCENDQ9y/f//MxHF1ENDNmzeb+g0Vi8U6RdW5c+e4f/8+HR0dZ/Y2LavTHj9+zM2bN+vOJa0oucpopy22cRztccUW0OiEkMlkePjwIc+fP2d8fJybN2/a3jQ1W2AJkoOKMsYqkTxHcinB5s8CyQuCdPgnUvpoS8vLXYrStkywimi4wMyhigX8HgGXaiBYheoXqoHckvrDwlJPHnG0zCJGPoaW2UaUZLK7s4d/c2T35snuzZPbWyC3t0huf5H8/hJaYg3ViCCa+UOC63QIYouS2pZUdOAK1EvHG8Ky6OgZxddzjsDAJeLbCycSXGXsLr6Pp+fwgipIdJ97FUPLnUhwAZiGhi9c6/ngCvYRHLnOwdY8ew18t2IbzxCk0sV6+CN/ids/+pu2BFcZBhLCYaGmWjoeNJyiiCU6MUSHbdKi4OpoicjVkDEbeJOJihenr4fusVt889/5Sdt10gdb9E+cPC5ajZ3lRwRDfU2tq2sFHn35t5re9nFIklTpQh3HceLrRU1MVVVlY2PjzCM7Y2Nj7OzsVPynWiG44EhRdprc/bgf13GcP3+epaWlMxV75eKpeoSgPbbYRhv//cPuXGNZFnt7e9y9e5e9vT1u3rzJ9PS07fnD6XT+oZFclpGnuHcXs1AiqqxijPN9WQoHD8is/Tb56FsY6SU6pEWuj+bo9usUY09ILv462e0vVQguAIzGTRfJUa920NKrFBOLrHz+R4nM/CLZtc8jxe8RX/59snsLLY0alZVxd+7cIZfLVUKTmlEEv6iSq9pUvlWCC2B76TEkV3jnc7/G7Hv/lX//Y3+BXDpOsHsIh7cDywRv55CtatvlsW98WLr9Z2GZJj2D9vYbxXwGj78DXVCJ7e1UHs+lE/SNHE8lP1rWCGMXbyLJCrMzb9eM+5mGTs9Q4zE/1emqS4EsY2BsmqVn9zH1Ag537Xvv7O5n+VltvSnKKrJysverKJUadL0j53F29HGQSLG6sszi3FNyNv6cnkCIxcNExWy61kNMEAQ8oVGePyip71KxXcTDOvX+Fz/D0pPmlPd2KNdgzRCyfX19ZDIZEonaz0fTtFN/E8eDgFqFKIpMTk4e3dOdAeUgoPV1++/BcdjVYrIsMzExwdzc3AuNLbpcLvr6+mzHFs9CcrXHFts4jjbJ1QKOX1ir02QGBwd5+eWXbWWXZZRHk5p5HUFUDkf6DEAv/Vn6ocn88T8dELHMApiFkpG3VawsVxURUTAnEydFAAAgAElEQVQPt3Xo9yWITbl+VWAVkZpMTgTwuFWa9c+yjHRTyXyllU18/c2PKeVjq8iuxp9J3fr7y6geew8M2/VTzZlgq55OgiO3yKYSpPa32F9tbOxuh0Jql67R63g6eoku3MUoNmdyvbf0AFegB9XbSefYSyT3t4kuvH/ic4rZBOGxa9z8a/+ci9/24yCefOE2EbCSO6h6HkFyoItOrNM+T0GkmGt+3FMQZQr5k5NhBEFg8PJH+Rs/+e9RHPUqoVY7O71D47aPB0L9DJ2/xcil23SPX0N0dfK5X//ZF7rYy7LctDdE2cTUzh/rNIiiyPj4+JlTCo+TVGfxhujo6MDpdLKzs9NwHU3TTvy8VFVleHj4RCPYRqgu2s7qU9EIZWP8+fn5dtJPG218yDheg8ViMd577z22tra4evUqly5dOlEh6nA4PrTAiWpYRh4ju44Wf0Qxdp/i/rtoicdYev01S8/tlWqz6udrSQr776NnN7H0+pt/yywgqvakiyjbEw2iXAomSm3cIbn0u7jS99h78musf/mnefqr38n6m58gu7do+1woEUy7u7vcuXOHWCzGrVu3GoYmNcJZlVx2iYl2vluNYOgav/fL/4Sf/YGP8s6nf4a7v/Jj/NI/+FbAomfiFpvrazy68wUAVLe9Anvt+T0kqX7YJZ1obAHhcNmPgOWzaRRPF7vb9dcZSbH/vq4tPKbDptHWNzzJwqN3yTVQUXm89vVuV+8Qz+5/GUWtf73BiYvsba+Rz6Y5iG4S6Dxq3noDXagOd931LLK1xn50A/mE8CHTshi/8jorS/PsR2onO/K5+vqls2+s4ie2s7HEyMVXKsvGLr/O7kbtqKZlmZiWha5pJA8iDffjNJQV9XByEBAcKZCOe3raeYjaIRAI4PF4zpxSqKoqqqo2TVLZYWxsjEgk0lQQUKOGY1dXF5IkEY22bvpfjYGBAdLpdB1paFnWmcJB2mmLbVSjTXKdAYVCoWGaTDNo5odXo+ZqbqstJScqsoigNL/PALLafJqgIlmo/pHTV6RUIHp6mk9OFMQWGHrLxNd/ofn1AW9otPnNZ3ZQvPbHUXZ6CQ5fxxmaILa7zcbTN9lfe0TXcPOG9QDe8Ci+rgFEWanzyjoNqstPx/AVcukkO3N3sMzTu6qy08vlv/pxul/+BlsVVjWsQhpJSyN7O+GUrt5xmEJr5EihgcS+GorDhzM4xPd9/JP4O2tVdnurj3G2MFIY31km2DPM8PTLjFy6TWj4EpbsY2tzk+cfvM/T+2+x/GyGVHyfzcUnPHrnsy29n2qUvSGqzXRPWvcsJqaWZWFZFqFQ6IVSCoPBIG63m+3t7TOlNQJMTEywvr7eUFVxmpILoKenB03TODhozRvveIdweHiYvb29Mycf2W1fkqR22mIbbXzIKJMcyWSSe/fusbKywoULF7h69WpT4z8f1riiZZlYWgIzu44We0hx7y301BxmIYpVjJXIrQbnZkkwEJ0NFOBmnkZ1nyjb11+NiB/Lsh9PdAb70LMHHMx+jvnP/ABLn/17pLcf1Vx3yuTh9vY2165d4+LFi2caSyrfcDaLs5Jbpmmys/KUtWfvMfPGr/Hz/9vX8nu//I8xDZ2N2ffxd/Xh753kIJZg6elRk6+ow/rcA2Qb4sfQNTwd9Z5DhWwKt8/eKyh6jITxBkOMXn6djY01Ukl7ZVZ0s7H5dlfvUM2/fcEQ2XSSTDJGqNfe10kQ7Y9VoLMbQ9cYPX+t5vGBsQtEN5cp5I8ID68/iCjJBEN9OJwudtZr31f34Djbq3Psba/bKscEUWTswi2QHCzNP7MlOnPZ2uttz9AkS89rvcJmnz5CVp2EByZs0yYPotsIsgNBlHD5Ovknf+vPcBBt0lblGFpRc7lcLnp6eir+WNC8NymUbBvOmlKo6zqBQIBoNNpSWnU1WlGUnVSLTU5Osrq6+kJhHmUbjrm5ucqxf1EVVjttsY0y2iRXi5ifn+fevXsEg0HbNJnT0KwBqCAIh0RXCxDU1tRZzaqnyqtbeYQGXUM7SI7mSTfV3bzBoJaJ4AgONr1+ncHYKZCV1qzqZO9RwSopTgJD13D3TJOMx9l49g57q4+xrKOTdmLrOWKD7l01HL4Q4fGXSO+tElt/zO7CewT67KXtx+EO9hIaf5lCLsXmo9/D4e1s6nnB0av8yZ94G7XrlONrWShaHEn1YKl+kF0IhRYTbpydGIXGkvzjkHx9GA3So2o2GxjF4fTxN37y3zIweUQoWoZGV4OY7zJUp4eBqVv0Tb1CKlvA5Q/z7OG7PL3/Fqvzj8me4EHxX37pn71Q5+h4kXXSKKDP5yMQCJwYkX4c1aqrF00pLBdpmUzmTCTXaXL3ZkiucnG0sLDQ0kjMcZJLFEWmp6d5/vz5hyJxz+fzuFyu9thiG218yCgUCszMzDA3N8e5c+e4ceNGS356qqq+EMllmRpGehHj4C5G8glmbp1G9YVlNL4BlRopFCwDyWmvJBdl+/OhZdi/H7OYKNlXHMPxsi+1+YDF3/k/ePap/5XFN3+R9++8ycrKChcvXmyaPGyEZmvjF1Vu3fv8r/DpT/wg/+qH/gyf+vj3srX4sLLM4QmgmQKW7EQr1Kr4LEAvFvB32ttrhPvsiaTBcftpAk0rMHn9Kxm7cpvwyCUO4gme3H8brVikq8++pirm0gRC9kEsNVYFviD+YIjEoWLJ47f/nqTi+6jO2jp96Nzlipl8ZH0JWSl9l3qHzrEf2aCYr23ILD29x7nLr1LI59iP1NcYwa6j4+X2BumqItxGp2/iCXQz//gecx/cpSPc10ARF0N1uhm9+AqhoWlWlubQj9UjhVyW7sFzFHQD49i1dPTiy8QOdikWCnj8HUQ3Fll4dJd3P/8p2+PSDMokVbOK+lgsVmmOtUJyvUhKYVnlXvbWOmvNWQ4C2tw8uXF+Ui2mKApjY2Nnngwow+1209vby/LycuU1z+r1Be2xxTaO0Ca5WoAgCHR1dfHaa6/R19d3pvSGVjqJgnQ6EVILE8TmEjOAo6TFFiC7mvegEq18JQ3vNOiFfSyh+ZOaOzza9Lr5+DpiCyojLdnYMN92XxwiSuc4nv7LpLN5Np/fIbr0ANNokKSUTRAevWa7DEBS3XRPvoqeT7G79H5NR9g0iieSk97wKI7QJOlYhOjCu5h6Ecs0CISHGj6njNGv/iu8+oO/jug8+TskGjlkI4uhdNTsiym2TnboxXjT6wqiQj59uvpIEGV0VGTFwV/+4X/Kpdc/WlmWT9RLxAPhQYYuvk7X8BWS2Txzj99n4fFdMol99FzzJNzys/d5du/3m17fDmXCqKzoOkmuPTo6WuOPdRp0Xa9sX1VVRkZGWFhYOOVZ9iiTVCsrK00Xdsdxkty9GZILSufToaEhFhcbj90ch53Xg9frpaur60MZWywUCjidzkraYlsy30YbHw4URWF4eJiXXnqppeStMpoZf7EsC8vIYhWiWMU9LC2Jqecw89sY8QdYhQjV3qWC0OD8Z+lki/bLLLNxDSgq9tdfS8/ZXvvNYvIwDfv4a2i4Q/Uj91p213Y7xWKajTf+Obk3fgj3+mfQD17Mb6cZvCi5BRDf3eS3fv5HWX58B1mtrfPCQ1PkLYXtrQ3kE5oxbq/9d6mYtW9qHR+NG5y8xsDUTRLpHMlMlsf33mJl/jFalcJFP0Ht4gvaTwPous7gxCWcbh/ZVJzNqqTC40RWGVsrs/QNHzVDBUGoIfeS8V0mLt4i1DdCKrFPPls/Uuvxd5LY366kF1ZDVlTWF59U/r27s46/o2RlMjp9k+XnD4kf+o4V81k2Fp/itXl/xUKO4elXiB3ss75UHxZUhjPQS3RjpeaxYPcw84/epXA48lit0F95Xp8c2SxaUdSXm2xloqoVkgtKKYWKorQ87ldWz5eDgE4jqU7C+Pg4W1tbJ9aQp9Vi4XD4hSYDyhgcHCSZTJJMJs/kx3Uc7bHFNqBNcrWMUCh0pjnhMlrxhCgnLbYCS2gxUaLF9SWh+ZOFgIkz2NzIooCFr3ey+R2xWpDHWgae3uYUUABaZh93h31nrQzVF8Y7cA25c5Ld9XlS+1tE5t9r2isrsT2H7DzWgRYkwudeQVJUovN3MWzG81LRFbrPvVzzmGVZBPqn6Ri6QjKyTC46VwoYqMLe0n28jdRZgsBX/s1/y/W/+GMop3y+SjEOogPTZnTCUvygNVY62cEUmydyzUISXS+gZaKnjl1KzjAWIIoSX//X/y4f+XPfAUA+EaGjb4KescsMXnwdR8cgW5vrPJ95m7WFD+q6hbsbC4ydv970Pn76X/2DF+oclb0hDMOgWCyeWDSVI9Kb7QYeL8K6u7vPNO5XRldXF6IovpAyopHcvVmSC6C3t5d8Pk8sZp96dRyFQsF22yMjI+zt7Z3J6+z49svG9pZltccW22jjQ4Isy3R2NqdKPgmNztGWnsJKzmCln2Hl17Fyq1jZecg8xcxtHfqfHt9YERrUaS53LWFlmqCZKggyouJDkJwIshdB9iDIHkQlgCVICIofQQmC7MMSXSD7QHIgO3uQHF2Iio+j8t1CcfcBAqLiKS2XS69bV2NQUn7ZkV9iIYrsCmDqeXYefoaHv/BdvPuJb2Tj/mcqPkkfFj4Mcqu8nf/4099P4ZCo8QaP0nYHz7/C8vIi8b0SIZXYjzS8TuaL9sqdndVZ21HG/e1l1MNm4NiV28w+mWHu8T10TUNR7L8LW8vPK0bsx+GyUcudv36bpafvo+tFWyJKOKHZ6XAdbW/q2ut144b7kU16hybIJOuvmbKiEuzqIbKxxNiFm3XLR89fJ1dldr+3vY6kOCoEl50KSrVpnI5ffIlH772Bv7Ox16/bF+Qguo3bV9uMdx0jJZ1uH+H+UQDSiX3mH75NNtV8g7IakiQ17SXn9Xrp6OhgfX29ZZILSimFrY77VVtEjI2NnUpSnYRmgoCaqcVedDIAjrzOZmdnyeVyL0xyQXtssY02ydUyzqLeqkar6T6tq7ksDLOFj9XSQGxBjm5pSC2ouVrx8ZJdPkS5uROblokiOprftupq3osJwN3RX/NvQZTwdE/h7r+KJnexs7bE2uM3iS7eRy9k6BxonkQDKGbjOLtGK/8um8rvLrxLMXOyuml/9QOcgW4sy6Jz5CqB/vPENp6daGZvWSbezn7bZV/7Y58lfPmrkWQVVXQg2KiXRCOPomcw1A7bEYgKGoxONIQrhHEs0dKyLMxCnHx0luLWQ4zdWZxaAr/ThS84iKJKCEYCLbVKITZPdn8JoxCvIb5ExYdhlH6rgiDwFd/w7XzLD/0U/r4pFKebpdlHzM68zUHkdPNOkeYvkGtzD3nvC2eXy0PpRq58cT7N1L2jowOXy3WiiXsZmqbVFGGCIDA1NcXCwsKZY947OzuJx+Nn9mQoy92PG8i3QnKVO6rz8/NNvY9GxWh1vPaLEJXVY6aiKDI3N8fMzOkpqm200cbpeNEazOFwND5fSW4aJUw3MnjHKjb0yxKtbImgEt0gebEsHclKYxbjYOYRBAstG0HLRtGyUYqZLfT0Blg6heQKxdQ6WmabYmqdQnyJYnqN3MEqmegsudgm+eQ++cQ++YNltKxBYvkR8cX3SG0tIzu7QbA/H8oN7AucwdqxvXxsne37/4nf/5ffRnrvbCpXQRAqN9BlcquslDkruVXGe5/7Jebu/V7l3w5PEEEU6Z9+ncczd2rG3w4i6/SN2ycsNxp5NQ2d3qH65qtlWfSPTTM4/TKP771Vs0xrUN8bhk64377pWzaRF0WJvpEpzl1+hdmZUopgI5WZVmxca5WP98TFlyrbqUYw1MvTe19i5Hz9RMHI1DU2l0vKqq3lWcSq4KGu3iES+/UG76n4AcvPP2h4/T1uTt89MMbG0lPgZLJuaOIiqwuPCfYcjUMOTFxibb621l14cg+Xv/SdTicO+Pynfp4HX/7thts9DeX6QNc1LMsis/2E+MKX0PMJ8rGVmnVHR0eJRCLk8/mWSS5FURgdHW0pQKea5Go2rfoknFZDNuO7qqpqy+/DDm63m56eHra3tz8Ukqt8fnnjjTfY22scGNHGH120Sa4/ZLRqfCqI8smkwvH1BQFRbVHGL1dd4AUZRAeILhDdWJIHS3RjCi5MVEwkRMmBiYIlukHygxJEUAIIsg9BclXtr4gYvIrosvc7OA7V28vw1/x9PH2NR/mOYOHtbl75pbfg/QQgYqB6uvAOXEXpmiKR1lifvcfGk7dI7a7UrZ+Pb9BaUADkIs8ITbxEoP88+yszzZvKWybhkat4QqPsLT8kvjnb1NP2lu/jC9WOLd7+7v8Pb99kZdxBEEVcLl8NWaVocRBVjAaFfDV00YNptkaY6NkdzPQ6QmoVObeDx8rhd3oI9YzTOXCRQPc4sloiYkXZiZZLIQgiqsuP0xfCE+hAliwEI4mZj2Bkt0rb5OjCLAgC4xev8Y3f9bc4WH+Mv7PeULYRdlaeMjDWfDDC7/6Hf1nncdEqymquZpILyybupxFN1eOKZTidTgYGBmwjnJuBaZr09va+UHETDocrSV5ltEJywdH7OG1ssVwInuR1Vu7MngW6rtd8ZoIg8IUvfIG33nrrhGe10UYbzeLDILka1WCCIIGjQb1i5qCB8jibs1fnW5ZVSsoWZcziAQLHyHPLRFSPp+FZWGYB2RXCDqq//2g9o4BlFrBMDbUqAMcspokt3MHQTNub32zO3i/MYZMsXYyvEN94wmd/4mt5/vmfRy+2lk4pSRKapmGaZs0Y2FnIrVhknS/86sf55I//VT7xA3+az/zsD9csd3r8OAO9PH1gf75tFDyja43fk9N9VPfIioP+iSuMXLqN7PLbmqHbeViV4Qvaf6YHkU2uvPJREAS2V+dYeHy0XaVBemHyoPGYWyq2S+/wOdYWHtctE0WJg+gmpqGjOlx0dpemFlweH2MXbrL45L3KuoV8htHpGwCMXbxFKr7H7vZq3TYVhwunp7E3nj/Yyej0DaavfwXnr38FulageGh036heVJ0u1g/3f33hCbKiYlkWmlavptSKeRaf3MPh8qBpBXKpA7aWn/L7n/43pOP7DferEURRRMRgf/5NIjO/QWb7IVp6i7XP/yMSS19Cq7LNKJu4JxKJlpOmoaSoN02zaRLmOOnUSqOzEU4KAjrJG7Ya3d3dGIbxwmTS0NAQ2Wz2zI3X4xBFkX/xL/4F8Xjz1iht/NHB2cxU/hjjwyiwWh0PEiQnlt7MCI1Q+hPAEpQayiWRTBLw+yl1Ka1Dn6fD/1tFDJPDZJ8mYVno2caRvRYSav/HMCUvrrFvxExvIYoyoiiUCklBBEEqdXEEERARpVIxGL76HXSMfTV6dofs/jLpyBPMYv37l93NK7mKqR0UTwgt0/gELLuCKP5+dBPiB7vsRncwjnkBNEI+EaFz+BIHa/VFhR2C/dPIqoqeT5Deba5DGug7hycYIrn1nN25LxIcvknahnBrCMvCE+wltbeOLzzK1a/7PnpvfT2ItRdmQVRx6hmKBkiWVvLeahKC4sJKrYPf3rAVwNIL6PF1ZMvEGxjAwoHH1zzp1OjiJwgCUpU5r2VKFFJLyLKzEtM9MDHN3/74L/Ab//pneXLQfFHg8zX+rjk9PrqHJpEVBwe726zMfcBv/9I/5Zv/+o81vf3jyGQyrK6u0tnZeao3lyzLjI+PMzc3x+XL9p1qaKxg6u/vZ2ZmhkQi0bLPjaZp9Pb2sr6+zt7eHqGQfRF/GqamppiZmSEYDKIoSh1Z1AzK7yMejxMM2seoNxPzPTo6yr179wiFQng8LXgccuTHVY2NjQ0+9rGPtbSdNtpo4w8GTqeTfD7f8FwnyAGsgv21QZCcWDZ1kksVsY5dlixLQJDcWMWDUuNPkKhbCZAUT0nZdfxxRxA9V1+vCI0sBWx8vpIr7+LomCC5/oRiNgWCgHfoGm7VIOcN19ysl16zXq2m5xJ0Dkyxs3CPh5/5Cea++O+49Ge+n7HX/kKpZjsBlmVVVMmCINT8tQJD1/jyp3+O3/2lf4wkyyiKg0yidt97Ri8x9+wJei6G0+2zHfFrlKC7s/IcUVYwdTvVtoDT46d37DILzx7x7HFJRTQ6bW9jkDiI0NU7xP5OfaNEbKBa6h2ZpFDIYRr2BI4d9iLruDw+cpn691ks5PEGOm2fe+7KK8w9fAeA+Ud3Gb9wC9XpopjPsfzsft368b0dJi6/zOLj9+qWAQyMTbPy/AHnr38FszP15GL/6Hm21+bwBbts/bIMrUhHuI/Ybq1f6tj0jYoKTdc1RiauIMoKi0/r97Hy3q7extCLzN3/Ij2DY6w8fge3y0U2Heelj/6lmnFWO1iWhWloxNdmiM2/QXLxi6i+LjDSlO6vLJwdw2y9/QkGv+oHkQ6nSQKBAIIgsL+/z+BgK6FYJUxNTfHw4UOCweCp9YmdsmpiYoL79+/T2dl5JgVU2WN1dnaWK1euVH6fzRJcZZw/f75Sx53Vq1UQhIohfn9//wvZA5Wxvb3N0NDpvsRt/NFDm+RqER/GuGKznlyV1xRVLCFPmZQqFIoUCnkcqoKqyofdwWMdQkEG/Ui9FPBIYDQmykQ1iJlv/qZf9XSTK1TN84sKonsQydUDagADEV0Q0S0DBAnJ3Y1T8TR9/ER3D06XD1fXJF2TfwrT1NFzMQrJTTLROXL7Cyiqi6HXv431d365qW26u8dJLB8VjbK7E8XXh25YJPc2yGysAUeEU9fQFaKL79tsyR6NvBiq0TF4EVESSWweGW2GR2+wM29fQChOH6GRS2jZA9K7yxRiR1205NZj/D1jJCPLp++cIBDomUBWVfoufiU3vu57cE+8jtVAJSgiIJsFdKV1c99iIc/xUllP72GmIjhVN76uMaTeo3QiU3VhGLptAo8dDLO59QRRRhBVTLNIJrqFyxdGcfnoCPfxLd/zffzmL4jce+vNpra1Mf+AjlAfsb1tVIebnpFJFIeHRCxKZH2B1GFyURmf/eWf5uU/+U0MTTQmneyQy+VYXFykUCgwMTGB2+3GMIxTL/ShUIhIJMLu7i7hsL3HRSOSqzzu9+TJE27dutVSUVEuuMokVSAQOFPaYrUR/oULFyr71QrKng6PHz/m5s2btiRZM4am1WOLN2/ebGk/8vl8Hcm1vr7OyEhz3oRttNHGyfiDVHIBhyOLpRtau9e2o5hEwcCUvVh6iUSxkBFEGUs7rMEsA1H2YWr1ZJbQ4LrXiMwyignb/dPz+yg2xFUhuUUhuVtRkaWX3iYjygRGX8HEiaEVMfUirkAnNPC6rFYz5eI7vP+rP8LGB59n/CPfwdCVr7Z9jmVZmKaJ1+tld3eXoaGhpj+7WGSd3/z5H2Vn9Tmq000hm2Z/u1TnaAXovfRqDck1dPF1Ht1/B8ssHZNz5yZZm60nQxp5eWrFPD0j54ms1qriJVlFdrhJZHQi79USOLFo44CiznC/LcmVOKhvDJfJoYHxC7bb2ttu0AS1LIYnr9SNI4b7RigW8uysL9IR7ie2e7SfiupkZ22+ZhuZZAxZUTmI1ofQKKoTb6CTbLKxCqZct819cAeXN0AufXTfcf7abeYe3cEyTUSbcASA+H6EYj6HrDjQD31oJVlhZ61WlS1IKpotCXlYw1y/zQd3fpdAZw+iAMuP3iIRWebp27+JZRr4gmGK+TSXXvuzeIPdCKJYup/KpUjuLKC6A6y9++vIikrkg9+iZ/waWEUso4goO5CdnWjpbYrpKPm9BYqpHVyOc5V9UFWVra0twuFwy0RTdYDO+fPnT1zXjuSqTqu+fPnymc6RXV1dRKNRotEoPT09DV/rJKiqyvDwMPPz85U67izQdZ3u7m5WVlYYH6/3D2wFJfWf1tJkQBt/dNAmuVrEH3iBddJrHkZSOxRwVE48jQy4JSyaH6ATrPLaJ891a0ovknaAIAjInTcQHSVzVFOQsTDQLDCQ4NhxMkQHupFFkZtTRoiKDzOXLF1ABQFRUlC93ajebnz9N0reDoU4kqwgKm72Z98gn4ig5206dYKEpLoJDE1hGAb5bJbU7jqZ9RVgpfExaRANbofxmy+z9OB9vKFhW++KjqHLCJgkt+tHCw9WH9A3/Trbz9+pPNY5dAmH20Ni8wmx1Xt1zwEw9SJO2UJx+dByx7p5gkCgZxyHr4t08gArEyWzt0Rmb4mv+t6fwz3+qi3BZVkWaDE0OYCRi0HrfAVyYAgjn0JPRhB1DV+wD1egDyFo7wkmyk609A6Sx159cxyujkHM4l7DSPVqaLqIKoMrEMI0NFLRJdydg/g6e/mW7/0BXv+aV/j8Z36H54+eN9yGIIh0D00T6BlGWQuwvTrH0vOTPZYMQ+cX/vHf5kd+5neRmuhoFYtFlpeXicfjTExM0NXVhSAImKZJsVg8Vc0FJfPPmZkZOjo6bMksTdNwuex9ZcpeCK0WFcViEUVRKqln1SRVq+ju7iYSibC3t3fm7p3L5aKvr4/l5WXOnTtXt7yR6fxx+P1+gsEg6+vrDA83ViUeRz6frytwNzY22iRXG218SPgwarBEorF9gSCIWJLbtimoaTkEy0K02QdBdGCRLpFkeg7LqB1ZFyQVO3tHoYGXjllMlpqVx83uLQPJGcTI15uGOwP9dSQXRg7/4CVSG4+ONmHqpLYec7BZO6bu7ztvS5TZBQ6ld5f4zZ/485x77c/xFd/+j/B09GGZh15bolQZSxwdHeW9996jp6enqZv/bCrOv/mxv8jBzir9Y5dZm62vf8qKJ0lW6B6/yQfv1xI9smI/VprYa2wJ4fHV+pT1jV1iL5bg/rtfRtfqrQASB1EGJy6ysfi0blk6YT+tEd1cRnW6K6N6E5derqif7LyuAJKxXVxeH7l0vWIrvl/bmO4dPkcqvl8xlB+duookK3R2DyAIoGtFFp8cNW4VhwtD15BVBw6Xp5JWCKXxxc6ewYr/1dS128w9rD3OQxOXKyORlmnQPxDWaYMAACAASURBVDzJ4tP3D9d/ndmq9VNx+ymKg2jpM+kfPc/WSqk+Hr9wk/lHd2tXFCVUub4g9fg6cAc6mHv4Fm6vn96+fjbS0YrKMBAaJB5d5WB7idm3PkVi5R75ZLT0GWlZbn3d3+DJb/0kwe4hBLNAsVhkcPolCntPEQQBo5DG1BUEqXTflovOIru7KKYiuEJHNYYgCBVF/ZUrV2zf60no7e0lEokQi8Xo6Gg8PXHcW7WMrq6uSqOzu7t53+RqnDt3jgcPHtDR0YGqqi3bRgD09PQQjUY5ODg4c0iIaZqMjo5y//59UqlUQ8+8ZpDJZHC73S983Wjjf0y0Sa4/ZJQTt5qFZVns7e2xtrbCtQv9LfxQDZC8hzLb5tYXHCGsgn0MrCn5Kaq9aHIA0dGHIsiAiY6JYQmYCIB8IquWtyRk00AQmxtDEtQuLD1ma0wpCAKys3Qh8A+/ii88iCAYpfhv08DUi+iFHFouST4VI3uwTefwNIG+Ue7+6j8je8qoWqB/mP4LU8QjqxTSJ4+XXv3o1xLoG8Qf6mZtbruG5OocvgpmgeR2SbklSiKh3l52t7Zrvgf7S+8xdPlPYBg6udgm2d057F0zapGNbREeusz2wgy+7jHcgTCmXiAVXSKzt0xmr1blNf2x78Yz/hqWWF8smKZBIbGBFCzJeiV3J0Z6Czx9TexJCZZWwCmKeJxBZK99LLYdVG9304mZoqSQTezi6To5ARNAdQZA3z18nowzEKKYjWEU8njDIwxNf4Rv7giSPNjns7/xOyw8K3U5/aF+OrqH0bQiO6uzbCw9YXPlGd7QSNOeY8vP7/OfP/lxvuGv/u8N1zEMg7W1NXZ2dhgdHWVqaqrmNy6KIpIkVVRYJ5E/5S7awsIC09P1HmKnpf8MDg7y4MED0uk0Xm9zo8DV2+zp6SESiZy5uCkryh48eFCnhmoFAwMDPHjwwHb8spVo6rGxscrYotsm/coOx8egyumKzR7PNtpo42R8GGr6aLSxnxFQ8im1IbkUycKSu7DMIqaplYJ7kBAlFRBK5vLFOLbNQtP++maZRQTZY2NJYaJ6+yim6hVBcgOSS7AhAQBUmwaSkU/i7homu39UryS3Z1HdXjRHP4GODrI7TwELI1+v5EnvrTBy+TYLdz7Nwp1PVx7vHL6Md/Aar3/T99PRO4ooinR5BD73yZ9iePw8A+eu0TVwDkMrkNjbIpPYIxAeIBAawNCK/MI//A6ia3MASA3U8ZG1ORweP47AAE9n7tTvmw0hBJBLJ+gbnmRvo161VL6ui5JM//mXaoizvuEptg/3qRoen31jbreB+sqyLCYvv4Kua0Q3l2v8r9KJgxoCrBqd4UE208/qHo+sL+ELhjB0jaFzl1h5/pBC/uh7JMoK6fgeezY+WlAiwcpkUjWJ1T8yRSGfZXPp6DVNU0eSFYwqNVU6XZumbR2meo9fvFUZiSzD0DXc3gDZtD3B7HCVGuA9g+OsHyMORVFiP7KBZVN7hfuHKkTc6LkLrD4t+ZllUzFGL90mm0nRFe5l5c6n0A5W2HpukU/sIDp8hLsCzH/2p1BlAUMv4HD5CXgFKBwRcpZRUnNpgKTIODrGSK49oJiuJ+3KRFM0Gm2ZaCrXP48ePeLWrVsn2jU0qgMnJycrJNVZFPXlIKCyIuwsJFf5fTx8+JCbN2+2PLZY7Zs6PT3Ns2fPuHnz5pkbn602Ktv4o4U2ydUiyn4CZ02yKKOZWedYLMb8/DxOp5MLFy4hyoW67uCJEFUso3k1lyhIGIedQwswlDCG0lUyEy8TTZaBiUnOKiIgIwgnE1vVsASJopHFITbHyguSE7No0Yw1j46KQq70+UgyoiQjO9w4/V34esaq1gzw2l/+u+j5GKahY+h6iRArFtALOYq5DIV0gvD4eSTFyZWPOlm+9wZOjxvF6UZ1ulCcTmTViayqIIjIioKWy+MOhhie0Bgc/iimaWAZBqZhIODBGnkF0zRRXS4kWSEQDpOMRHB1juLwhzCLOfLJCCYW7r5x4pJIer9kYKq4/DjcQRSXF9nhQpKUEllqGlhGEaOYZfzaV7L25G2ye41HFy/8qb/G6J/6HgSbVEqjmEErJisEVxmmKTSVTiECTlFCcnoajnScBKtF035Db45oUlwBMtEVVNeRglB2uJAdLjIH6wiSE9XdQwD4lu/6dtLJFF/4nS8xc/cu+5Harq9lmvi8blqx9/ytX/gnXH7lY4xfvFXzuGmabG1tsb6+Tn9/P6+++mrlIm4aGunIItndOTI7TykkIwTGP0Lw3Ffh9J7sj1Ymmuy6gaeRXOUxvdnZWW7cuNF0UVE+j5WLmw8++ODUIq0RHA4H3d3dp9+EnrI/09PTtuOXxWKxaQKtemzxxo0bTd1cH/fkKndd213ENtr47wMOh+NUywhB9tX5cllQCuMpHmCJbqyq0cOSsOjwNy7KYNZLtiwji+QMY+SPNRLNPKo7RCFZT6qJDRRJomKvyM1n7E22hQbncl94pIbkAihm02iyi7n7X8LfNUDPwCC5/SUkxYGhVU0hWBaqo/4GOL41x9rSPB984Zc5/+rXcbC1SHT1Cao7yLv/qUTMVY+mdfSMsLO5gtPjx+3v5GB7xXZfqxEenmZp7imFffvQnWKusTWHNxiyJbnS8V28wRCiJ1ynDPN3hGxJLq1g/z3StQI9g+NENmqVciNTV8lkEqw8q/emAugI9xJZrw+B8fgbX/cnLt7i2f0368YWuwfGWHk+Qz6Xwe0Lkksnau5bJi69XKOWWnzyHm5fkKGJSyw8uotxzB9sfeEpExdfYu6Dd3C4/XT1DLK1XEtGJRP7DIxNszpnn/Tdfxjgs3BcpUXJYL8j3E82najzUzt35dUKAdc7NE708Liev/468x+UyLSO8ACrz45Iw2QsSjIW5fLLXw1CvOJfGwj10Ts4Si4RQTXiFWIO08DjduD0d6In7UhBAUfHJPHFLwElkriM6uP6IkTTaUr001BOa5ybm+PSpUstPx9KQUDRaJTd3V0MwzjTmJ/D4WBwcLCp8cvjqB6R9Hg8hMNhVldXGRsbO+WZ9lhbW2v7cf0xRpvk+m+Asgy0kaIgmUwyPz+PKIpcvHixogKwTLk1kssySh1J3b6rdRymZWK4JtAEFV10VIgHyzIRrCIGQmkU8fBro6IDrZ0AC6gopt5wPv84BLWzoZqrGoorjJ5aQlZO367T20Uqe4CkOJAUB9B4hDI8No3L50Rq8mIlKSrZ/a2GBIFlWViWQNdYPz3TX0VmdwOzmKOQi6Oah0VzPkFv2Ic8+ifQTJHt2TuY2QiFbITGg64rDE7dYHv5GXqhvsC78LHvZORrvhvRadfRjaFZIHrqU6UUfx+ZrSc4+y7WLQOQEHCIItIxM1mT0vepkdFqPYSWxmtlV3OjjQBawUK1uSdQPSXFjVEQ0HQBRQZfwM83fuvX8ZUffYXf/rXfZnmhljTcWnrC+SuvMPuoPlXpOPpHpgh2hvmPn/gRvvXv/FOGJq9iWRbRaJTl5WVCoRAvvfQSiqJwsPaY9ff/I2IxjpbaIn+wAtaRz1588U02vvwzDNz+bnpvfHMlDfM4TiKamvFX8Hq9dHR0sLGxcWr3y46or045nJqaOuUI2cPn87Gzs3OqbP8klMcvl5eXmZiYqDxeKBRaMtf3+/34/X42NjaaKpSOe3JtbGwwMHC64rCNNtpoDi9KGJfrr0awLIvIXoYu1UKSyq8lARJWMXbCPhwqECQ3ltlgHNLUMC0QFT+mngNBQhQ5VHHV20VYWgMlvqVRai3VerHK5DFcQYxcrfLKaqAiQ7fXi3s7ujnYnCe5v0k6scu5q6/S4Yixt1ZLaphafUVi6kVGL7zG/MzbPH/nNyuPF7NxekYvE1l5XCG4AGKRVSav3Wb+4dvkM7XKIOPYmKDH34Wjc4j775VIkoGhUVuVUj7fmOTSG/g6uXxd7CVyHMw/qVumFe0rr9he45aXv7O7huQ6f+02sx+8g9PVuN70B8O2JNfys/uIklxjTC8IAucuv0Iuk6xRbwGE+kYo5LIVsqh/ZApRkirqqtHpG2ws1r5PQ9cYv3iTx3d/z3bfCrk0sdg+fWMXOYis1RFcUPIpG52+UaP2qsbO6hx9I43rgnD/SJ0CTHG4KmOMAJlUgtHpG2SSsRqj/N6+PtYSR/5jvmCI/oEhHOQ5OEwudwdCFPYWyBQyDJy7Qma3pMYSRImOUDe52AZOz7F7GkHE3T1NevMJieU7uHsukI08Ix/fYOudf03/63+9JglbUZQ6f9FWUFbUJ5NJ/P7aNFDTNE89/1XbPpw1CKhsfREOhxtaXJyGvr4+dnd3W67jjqvth4aGePDgAaFQ6Exji21P1D/eePHYgj+G+IPy5cpkMszMzDA3N8e5c+e4ceNGzZiLIKogtsqqS1gNP2YBXXCSl8Ok1WGS6jBZyY8mOjEtwDKxLB3NsigiHxJcRyhYEvlcC6Rb6U2Qy+8fSoALmHoOS89h6VksPVP609JYWqr0ZxYxDa055VwLJumyo/mTpaw0n7AmqY4TCTxBEEpFrZZES68gO1QK8RUwsjj8YbwDl/GPvoy7ZwpRBNLrjFy+3ZT/VGr7Gd2Dozj9pTFBUVbxhYa59Rd+mPE//T1I3nr5dD62giY5EJ2Nj52vr/5CLQsCHlHCLUnIDaLA8/nWvOesFjh3R3CwQRJSPSTl5FEzZyCE29tFPp2imM9hmQad4TB/5W9+J9/7d7+X8YlaciO2vYjbWx9FrqpOJi7cZPra63R1hdnfmGfxg7dZfvoeH//bf5bP/eon+NIbv8vBwQEjvQFSjz7Fe//2O/n9/+c2z3/5u8jM/TaplTfJ7y8dElwCjkA/vsGrBMdexhMeZXfmP7D6hZ868ffgdDrp7+9nebmWoDtNyVXG6OgokUiEbPbkgdnqwq4a/f39ZDKZE31vTkI5sXFhYeGFYqSHhoaIx+OkUkckfyvjimWMjY2xs7Nz6vGA+mPcDFnYRhttNI8Xrb8aPd+yLHZ3d7l79y4HBzFQ/CAoWKIbU89jVTcLjWzDWqxhA0L2YSGQy+YoptbQc7vo2R2KmShIbkRH/Y2gZRaRXfVBImYxjuSytwNwBupJdaNg3+gsJDYPRy1roVQ1C029yNyDN1G89fuh5ezP8WbBnpxTZPtjrzVQXuVzR9sZPP8y0WSR54+OiA2nx75uScWiDdVriYNalXD30BSu7gkePngXl89+e1urs4g2Nhux3S1Up319UcznCPePcv76bQYnLpb8qSyLfDZNMFRKkx6bvo5YdQ3VtSIuj4+v+7bv44d/+tf5lv/lR4CSMX7f8GRlPUEQmLhcUmLtR7c4f+11oKT4mrr2OgfRjRqT+4XH7xI/9Pw6f+02q7MzFI6NRbo8PhafvM/YhZu27wcg2NGFJEAha//59o9MEd1oPE2QTsYoFkr3DE63t3Rsxi/R1TvE0vMHrM7WK8DGL9wknThSKKbi+4iihKzIFbK0d3CctedH3m2SotAZ9BNdfUohdjTuOzA6hV7IMHjpNpndIzKxb+oV8rF1usZvoR9TQ6r+QRLLdzGKaUwtV/LJA2Kz/5VstKTuO37d7+7uRtM0Dg5OtjqxQ7lROTc3V/G1K6NZI/ipqSmWlpbQ9UaezSejHAQUjUbPbNhefh/z8/Mt1XHHa7TqCYPjx6MZbGxsMDo62vLz2vijgbaS6wz4sEmucqJaJpNhcnLyRD8bUfFjFuwNHOt3VC7FVqthCvkklmngdDgwMSkKLgqiGwMREQsLAQHh8Obawqhk8YgN5TWCIKC4mhv9kQQB8ZA0M2U/lllAkp00w7NaWAjaHqapY1mlMUZRrn9d2dmFnk40dSPvCvSSis4jNrGuMxAmvbfStJrL3TNMPraLWWzcTTzaZwnR4cUspDH1fE16IoCkqoiCxvitj5KOxzEN7UjhY1lYWJXPrJQsZOH2nsMQLhOZu8PEK3+a/le/CdNRWxBblkVm5ylKz/lTVXKmIKICRctCEQRUQURsIgZcdbYYZSyIp+UeVCBKCtls+kQZfxmuziHM3HrDohfAFeylkI4giKX3pBXy6LkMAa+Tb//e7yQX3yO2vU4yniAaOSBHkC9+8U28gW76hifQCzm2l5+xMVc/htDhdzLQ7SH39JMoa04OZIucS8UwTGTRQWD8GoVkBEl1ozi8IIBRSFJI7mDm98jna3/vB8/+C96+K4Quf33D91P2pao27WwmpRFKRcXU1BSzs7Ncv3694efcqOCqTmtslHJ4EorFIh6PB1VVWVpaYnJy8vQn2cDO06FZ4/lqSJLE1NTUqWOLdsq2th9EG218+HhRy4hyqEf5fFhtDXH16lXcbjemWcTKrmMV7AzBLSTFj2Fbix1enwUZQXZjmSZGIY5VPFT9yB4wq8f+TIqpNQS1A61YQFK9SLKrlM5o5pFEJ3qu3iu1YCi2BbzkqFdeGMUUguTEMmrH6yxTx9s9TnK7NnjFKh4jxSyL9Q/eoHPidTB1zEKc9O4ShZS9h2vm0GrhOATDXlEW3633HQNI7G4iiBKhsevcf79+xC2ZsifvLNMi2NVPbLfeaD6+u4UsSlimwfDl2zx8763Kd8nptm9+5rNpekcm2Vmdr1sW6h2qURqV4XC52dteZXdrpW5ZMNTH//x9P8qtr/qfePM/f5LPferfsb22xPLzB5y78gpf9fXfRs/QJP6OEL/28z8BgPew1ukeGMPtDbBwqCbf215lb3uVgbELePzBOiVUGen4Ptdufy1LT+/Z/nbKSY12pvmCIDB59TXmHr7D1NXXbLcPpSCAVHyXoXOXWF+oV8RBieg6f/0222sLdSOWOqXPIJ9NIYoS5668wsrz+ppKlBS8gRC5dIJkbJdgRwep6BG5NjZ1nejiPaav32Z3vnQ8+ieucrDyAFcgTG73aFzVGQiT35sHQcIq1pK27p6LJJaPfe8O62/L1LGMUqP1OMlV7Ut1FusGj8dDKBRibW2thqBpluQqpzU28mdtBt3d3SwsLJDNZs9sIH8WZb9dI9Lr9RIKhc40ttiuwf54o01y/TeA0+kkn89TKBRYWlqqJKqFw+FTiYOSmssJZlWxUiazKr5ZFkcy9hLxoTi85PIaWclDAbnGA8lEQLTAxDrMamyexNMtC4eNB5OIQDZTMrAuLzMFEQQVATAEFYnmWHlBdmNpIpLsOHxHFkZ+D9PQEGQXkhqoHDdLdAPNGZhLihurSbNzWfFgNbldAaskb96wT0WshllM0THxMvtP32iwhoWW3kVL7+IJDhNZfFbqJjWB1//Kj+M99xF0tZbgMg2d7P4Sam/zUmpJEHELILdg/thqz6VVXy53xygYp6uFZNVFajeF03+y0k+UXViHvytFdaCope+baRhIioOuwTG8wTh9A6Uu7Ks3/iLFfJ5EIk00msNLiHw2Q1/IQ1eHB5/HidOpIstHBY5pWhjIODqHcHo7sYwCWnYfp9uBoRtkdh7V75gN1r/4z/ENv4TD32u7vFxkVRM8QhPEZBmBQACv18vW1lbDcbuTCq7qtMbqccFmoGkagUCA7u5uZmZmbA3km0W5WCwXR6ZpnskrLBAI4Pf72dzcZHBw0HYdO5PWjY0Nrl69eqZ9b6ONNuzxoiRXudGoaZqtNQSAKKpYjhCGLckFmAUEJVjjzQWlm18kD3ouCoX665NLLqCZQQT9mJm7WcDIxzDytSSD5AiiFy0ExY2mWwiihUMq4A92kI1uUQfTXkHt7hkns1U/YuawMaXPxzdLCdnHjrFWyLP48MsABHuG6esZIpWHbLyW7Cpk4ng7wqRjtY/nkvYN2lwqhr+rl2RVWmBn7yiyN8z23Htk8vaKlIPIGkqD70JHz4gtyWUaOuHRaUzZxcy7b9Ysy6QbBzV5ffYNNW+gXlE3OH6R+Ud3Gb9wsybNEMDt8fH9//fP0dlTuvH+mm/4Dr76z303qYNt/suv/izzTx/h7yip5nxV2+7uH2Bk8jvx+Dx85hd/tvb1Ji6ys7ZQ8nsN9ZI8iGIdql4kWeHc5VdYm/+AR3e/wOSVV5mdqf0cvIEulg/JpN2tFQbPXWNj4WFpf70BeoYmKuRZfD9qayDfPTjOyuxM5TmSJFd8vbyBTvpHzpNOxtjfWSPUO0RX7yDJg3rvTW+gg/7RKRL7kYaE3X50g4PIOtM3buP1B1mfPVL39Q1PEV16gOrykNx6hsMTRMtn8ATDWLldgv2TFHdn8fVdILX9jNDAFOnNGTrHX0aLL1a2o3hCpDbqlWXV5vfmIWlrp5J3OBwMDAycuVE3PDzM/fv3CYfDeDylaZJmSS4opTVGo9Ez2z4IgoDL5WJzc5Pe3t6WDeTL6O/vZ2Zmhng8TjB4us1IoVCoG9OE2uPRSpDPxsbGmf282vgfH+1xxTPgRZVckiSxtbXFvXv3CAaDvPbaa3R3dze9XUH2lWKqRWdJMi+IgFXy4LIMqukFC9BRyFgO8g4/eZRaMuGwNshjtsJt1cDAAstCFgQUUUQWBAQBPFUE13FolnW8fjoRlnCk3BIAWfWguoIoigP0JFpmBy0bRVKDTUtjXYFerCbXdQbCaGYLChAjiWCjNrODntnA03u6OWMhvkbPxPWGZrRHELj0zf8nHee/BlntgvQ2pl4ib0xDI1coooabM7WUEXAekqdyi997C1q8EWnRsL5JXzeAQv50gtLh67XdX1GScPiCOANdBAYmsCRnKVjANJAVma6uABPnBvnKj1zhox97hakLk3SEwygOJ4WiTipTpGg5UXy9qG4fLpeKlYuQ231G/mAJI5/A1PMobnfTXmOWoZE4NEBthOpuILT6WcD4+Dibm5sNTZpPK7jsxgWbQZksOkm23wqGh4c5ODgglUq90Ll7bGyM7e1tcg1GtI/7cUG7i9hGG/89QhRFHj9+3NAaogLZj6Da+9pYpoEgKKUUa8kLkgcEF2YhjqUlEcTGSmZLqK8lLD2LMzhe97hRiFPULPK7sxixOfT9eUyzatuCiCi7EQ4Tk/XcAYLNCKLTXz9uCPYJhqZewNNR30CRpaPzZzyyxrN7b4FpVvwtq9HVW3/ey6UOUFT7+qWru9Q8CIT66Z54mefzSzx+cJdY1izVeTYwdJ1Al32jp1CwHy/vG70AjiBzj+/XLcumGo+XSQ1u8lOxXcamb1TGDr2BLpIHu1imiXzs2PYMjvD//sb7FYKrtGEH/z97bx4jSZbf931eXHmflVWVdd9VfR9zdc/MrsA9uFyJ0pKiSO2Ky8MypZVMigRhEwRhk6JkgqIpE9ACgm8YsEDDomTBFmgYkEitzGPn2NmZnu6enumu6u7q7rrvK++4nv+IyqzKysiqzOpZ7g5ZX6Aw05mRkS8zIuP94vu+v+9XuCbxjj6+/LP/Nb/0m1/HNnco5bfQdJ3P//BP8k9/99/xM7/8m3z1536ZH/7K3+J3fvf/4fyVawD0jZxjffEptmVSKRUIhaJMXLpBJJ5i6trrROMppm+/QamQw3VsX7P8vuEpzPLBvLaz4SnxRs5dR9X1Ou+rtcVZuvsbz9N46uB3UikVGb34EuC1UIajCWbuvsXS0wdUykUWZu9jNDkPegYn0PQAm6v+asDRCy+yuTKPlLD09DFaMEbv2GWiiQy6ESCoA9Kls38Ks7iHiHQz9tIXmLv/DrGeCyx99CbRnvOszD2ke+o1SsU8ih4mt/qYYOd5gp3nEVoA1IDvgnJVvQVeKy94JFcz64Z8Ps/e3l7DcyfhcPBNtXarBtm0gtO2Cx6G4zgMDAzw+PHjkzc+Zhznzp1reRzlctnXUkJRFM6dO8eDBw9argellOTzeV/S7Ax/MXBGcp0Cp71RchyH2dnZ2gXj5s2b9PT0tL0/RdVRtCh+vV0SkEJHKhEsNU5BxMhhYClafbuW9KguExdzX35rSrftE0LfH7uyr+ZypWyJqPDIt9bfTRjJBhPSKhTVwAglMIIxFOFimxaVwial3RVKuytUCls4VuPELhS11l/fCuItEkPgeWlEuo9XSkkJCB2hhQh19KBHO9EiGbRIB1q4Ay2cRgunUEMp1GASNZjALu+QPfcqwWQvsewU6eEX6Jy4Sdfkq3SNv0Tn6DVe/OnfITHxeYSRRlE1wol+IkYYDXAVtaUWUwUI7rclVqeT09AM7b+mPZVNy7SNcTJ5FIjEMYsn+4hFO3sRqoZqBNACQbRgiHAsRjydIpnpINPXQ9fgIJ0jY2RGr5NKJwgHhSeFl80nebu4Rbz/UqufiO3Zb564zeDgIBsbG+Tz+bYjmFVVZXx8nJmZGV+C7CSSq1rctOulcFgRdVgRdlocLhZPuxoJ9W2Lft+Hn8x+fn7+zA/iDGf4mHHaGqxUKnHv3j12d3dJJpO89NJLx6pEhRAood66x6SUoIaR0kVaW7hmDre8gVvexN03pgdQjvHyVPBXJvnZMADo4XoCrrTxkL25d6nkbbZnP2Tz4XtszX6IVQY10Ekw1Wi0LJ0mnoLSfyyRZCMpVt5dbnismN/GiGaQiTE6xl6u+Yc28z7syDaOLZrqwogk6Zl4gdn5Fe7dPmgRcyVs+CnW9hHzGSfAxnI9SaIZQYYuvsrD6fvIJnPh3uYKetD/uBVyjaq8/rGLVMoFNN0gFIkzdfU1kplu9vaVbYW9A7Wequv8w//h9wmE/W64JThlqKwSChnEIzphvURYL/K3/u7X6O4f9s556RmPd2SS/NJvfZ0bn/1BNpbnqJSLDI97aiFFUdCDIVzHYfr2Gw0eZE+m36d35BxT1z/F1LXXmbz6Kg/v1QfpVFsbnzx4n9x2o/rOtutr8Xi6q06xNv/4Q2yzQjiaIJpIsbZY79NV2Nv2nUPHLr3MB9/6BsX8TsNzVRwm40KRGLMf3WJtbR1TBDn30mcplB0Gzt9g7dmHKIkhNpaeMHfvDSrFnOetJ12e3PkTKvltXClZfXyLWP9lrMIW23P3WJ95m2DmPMWVdZqcTgAAIABJREFUB77v71oHvyN5SMl1nHXDaf2kYrEYiUSChQXvXG5HyQUH/qyzs41hBq1ASklPTw/lcpnt7e2TX9AEh1MjT8LRhOrDiEajdHR01BZuT0KVfDxLt/6LizOS6xRo9wfjui5zc3O8/fbbKIrCiy++iKZpbd901o1B0VCMJCgGEtWLttYSSDWJpYTJSUHecbBk/YVVSs+G3kJSkY0XXSEEJ9FUmhDo++oeS0psKbGRbStF2lFzCUWjXDhZESKEQAumUBQdPRBBD0RQVR3pWFTyWxQ2F8itPiG3Mkt+fQ7pCqxSAddtIdvPziH8Clehougx1GAGLZRFDWVRgl0gJFqkBzXYgdATnhJMCSLRcV1vJdi1yziVPJXdOWL9F5BOAekUkW4R6Za89jlZBip4bZgmVm6OdO8A4Xgca+sh5dUPKa3cw7XzjH3hZwn03kDoB94SUkoc6bWi6if4bwk8cksTSgNB5UjZ9kTttqfNQop2W8la8/3qGGgtTtl2TyZB9GAIR7ZGlhgB6WvY2wxWoUW/PaCwdBez2LwYhHp/rdO06aXTaXRdZ3W1sWWnlYIrEom0VZRU93uYjBoYGGB7e7ttRdhhRKNRYrHYqY1YqzjcxnkUR5VcUko2Nzfp6moMfDjDGc5werRbg1UqFe7fv8/t27fp6upidHS0dW8+NQLBHqQw9v90XHNnP+EQVCOBXyktFKOpmksTFbRwY5qxa+2hBBpJN02U8KtPjNihVjnpkF++z8aDP6K0vYllqhjxwZqNhVXc8J1fnbK/yiQQalS2lXaWyY7Uz6Wm6aDoIRafznDn3bdZ2nEoG71sbu/Rc+4m6b6DVi1F1Ql39BLvHqGgdJBX0uzJGNNPlnnjj/6AkqPj+Fyjd9bmfccDYDRJK8ztbBBNeOqiQDhGMNnLvffeRErpq2aqorPXP4lt8cn9WmtiuquPqauvsvD4Q7bWllAUlcLeNtN33mTh8UFL6OribG1x+Vf/+b8hmmwyF0gL7E2EqK+vhBComgLWjkdGygNDEV0P8J/+/C+gad7+f+5Xf4MrL3+apWczfPjOfyQQChPwIexGpq6zvbZIKb/L9O03mLnzVl1qI8D4pVcayLHDmH/0Yc2/rHf4HAit1h4JXlLj0+k79A5P+aZFgmc8HwzHGBi/zNS115m4crOmGCs1qfX7hs+xMHvw/Yaj3m9lc22Z3c11Hrz/JktzjwEFx6qwsTTL8LnrVAo7hBMZtucO2g87R66yfP+bBMIJ8ov3ALBLu2ihGHapea3hHApVcEyPcGum5AJvoa6rq4tnzxpTQFtBNfimVCq1TXKB58+ay+XaDgKqeox+HIqw6jj29vZOHMdJarWhoSE2NjYoFE72PF5aWqK3t/fE7c7w5xdnJNd3EFJKFhcXeeuttzBNkxs3bjA8PEwoFDo2wrpVCKGi6nFUI4mihVAVDUdAxXVwfNijYqGAK6Asm1MPlnQxi40SXRWPIBF4PlxHyTNbSs/DoQ24gN1Gj2S460pLlIkWiOI4jROOqukEIglCiQyhZCfBWIpQPE0g3o9r5rHLe1jlAuViiUrFxpE6KCFQYwgtidATBGKDqMEuFD0FIoRju9ilXczcMpWdp5S3H1HZfoS5M4uVm0cPJ7EKazjlLVRZwrUKnvmrj6LHKiwR8ElG8oNV3MA1t0if/z46r/xlpn7ov2Tqh34dJX0VoR4Yz0opMV2JuX8+SA7Ud0cREAqGD7l1GH6poMfBpb02OSmPOR+kBNcCOw/mOpSe4JprmLsPsQrL2OVdHKuC67pIKZAotdZcgesdyxMQblaEHkE8O+glkJ70eZwK4a7W44vt0hbB1MDJGwJIl53ZN0/cLB6PEw6HT03wjI+PMzc313DNMk2zpYKrnaKkisM3sadVhB1FR0cHlUqlrXH4odrGebRt8ajMvnren60inuEMHy9a/U1VPbeOWkMEg8GW5zIhBEqgC+mUkXbeS1asg4saaDRmlnYO1fA3MnelAClwMXDRkegoRhqkTcVpTOuTTsW/NpAmQm0k0oSA3ae3WXr3/yW/toER6wPpokQbW/vM3ArhjsY5p9m1PZZqbN80iwdEmW2ZbKzOM/fwA8qmwwcfPUCkxuiaepW8kuDu29/AkbCztcbu1jq53YOFmsN+R3UfU0pSnf5ticfdeCc7e1E1nVhmkOW5g5YrPzP42ueL+6u+pesyduFFBsYvsbW2yPQhv6hy2X9OsSpluvtG+OrP/0NGL77i/4auCfZe00rYi4RyPaLLqZ9zQtEEv/W//O9cufEZUpku/tqXf7T23M7GCoMTB8rw7v5RJq++yuz99ygVcliOfwHT2TvMs4d3WXj8EcNT15qMCnqHp5i4fIOlpw/Y22xc9Bm//AqKqtIzVG843tU/SnZwnLtv/SHlYo5ScY/p22/w8O7buPvHMr+7Td/IeTI99S2vwcgB0dnVN8rs/QPf24mLL1DK75LuzLL8yFOVCUUht+od996R87X2QoBg0Du/s6NXcA6FRAUSfeQ35wk36cSwy3s1qwynksNxnBOTqwcGBtjc3DxV7aGqKhMTE0xPT7dccx1GtX5q1/bhMKFWNZA/rSKs3XEcd30/rMw/aT9zc3MMDLRYT5/hzyXOSK5T4CQDZyklq6urvPXWW+TzeV5++WXGx8drF0FFUZ7LNLXZeIQQBFWNhB5APXyTiEduaOGQL/l1FIFweD9v0SO2VCFw8Aiw415tSfcUaq4Gb9PmEIDqXzQeRSDV6BnQDFrgYJ8CF02xUWUJWdnCLq5hF5aw8vNYuTnc8iaVnSeYuXns0jrSbtICUIUsHgQCnATpEEhkat4aflCNKNHeyySGXyHaM460Noj3jRLITCGjE3Xx5Y6UlF3X80w7BIGo89cyhCAglP2IguMRCLTmM1b3sdrc1stNcLyCztyC8gKUHiPKs4jKHMJaRTh7CFzvu5I20trBLS/hFGax96axdj/C2vkQa+cjzN1HWMUVHEfUebv5IdndDz43DUdhBIMosda8lqzcEorhHzPuh2Da39jcD3vP3mppdS2bzVIul5v6ax0HXdcZGRnh4cP6ZCnbtltSQ/h5SzSD67q+19aj/mKngWmaZLPZlsZxHKpti9PT03X7Oark2tjYIJPJnJFcZzjDnzGq1hDvvPMOwWCwwRqiHZILQNEiqOEm12W3BH6qfOn4twhqMcr5bSq7jxGAlV/EzC9S2npAsSKakix6tNHg3ClvEc02pqdZhTXUoFfXlLcXWXrv3yH0TrTooUWcQ3VJpMNnLnObLMTajYugufV5wrFGBZpZ9BQxC09neP+db7K9sYrtgoN/jbO1vuL7OEA44u+rs7PZXHFkhKJkx67xZOZe/Xh3Nukd8vdBrZT8a7q+kXMszz9i/tG9hud2N5qP+/N//cf5/A//hP+T0gVre78Vsdmc5B0ngUT4LEEmM1l+8dd+DUWa9A7WG2w7rsO5Fz5NLJlhdWG2zsi9uLtBpqd+Aa7ql2ZVythWBb1Jvdc/eoG97XUeHWlzrCIYjrH8dJqdjRViyQNSNDswRn5nk5W5g3TDiI+fW6WUByHo6D74zXVkB5j96IDUiiXrieW9fR+xgcFhXNtTWY5eeIlKbgMjHGN3/uC4JbKj7C5+RDTdR3Hl4PFw1yS7C3cpbi9SLjcJeJIuwaRHnLhWEccyT1QftVMD+SGZTBIKhdjb22ub5ILT2T4cDdKp+ou1qwjzG0eztsWTyMIqYrEYqVSK+Xn/VNYqFhYWGBpqfZH5DH/+cEZyfYyQUrKxscG3vvUtNjY2eOGFF5iammp6I/hxEl2HIYQgphmIfXFVRboNyqtjXw8YqEg84qoVYgw8UqXtVk7ArjEbLUBtLVVDCNmy35Z0ik2LrsZtywQSwy1tC+BaecJdrUf42sU14kPXDx4QKqHOCRLDrxAfuIwRjeCUVzH3nqIGonRd+UHCfa9CZOggYVJKTNel4jYnJRUEOgIqJu0YvjunaEs9dmvpIqSFcAoIaxPFXITKCqLyFGEuIZxthKw0X+Vs5XSTFtLcxrWL7C68RWF70VPpNTk/ArHWSKZYR6syaEm4q3U/t2a+XUI1CKSGCXZfQk1OUiLB3N0/xbbtE1e0XNclnU43EDOtorOzs3Z9q6KdVcVYLEYymax5SzSDZVlNr5eH/cVOA9M0SSQSJJPJE4ujk5BMJgmHw3Vti0c9uc5M589whu8MmtUZR60hbt68ycDAQIM1RCAQaJvwV4I9CL2JgbFTAOFzLZQuQositAhCT+C4gsrOY1ThEUiOW/85VGsVMzePFmpscW/mqaWHGxf+HDNPcuSFusfWP/yPFFaesL3nsrq6xdLcIkaHR/QoSuP3Wdl+5vmWHkFpuzGxUEqXvuFG0shP4QOwt+WfWLmzvsDwOX/1UDXt+CjWlp5iBP0XkVQjyoM7/kSMnyINYGX+McqRzx1PdbK3s8HawiyJTKOibGdzlVCk8Tj8nV/+DT7/13/C40CdokdqHYa17Utc1aHJuV7Ob2KXtnDMHDgFpF0koNl83xc+A0AmO8je1joLjz8kt9Nog7C7ucL22iJ6wFO4a7pB/9iFOpXb3MN7taTHKqauvc7i0wdsLD9j5Hz9OVbF0OQV8nvbrC0+YW/bIyGzA2PsbW80pDIaobDvZ1yc/ajWAhkIRlBVrVa7ZHqG6giv8QsvsLU6T1ffMEsPD7zBnH37h2R2FNs8+P2ks4NkJ14mlek8UA8qWp39Q6VJEqgaiKKEOjDiXv3nmLmWDOFbrYGaYWxsjEKhcOr7xv7+/rZsH46SXIeDgJ6nbfG4QCI/X9NmGB4eZn19/Vh13Jkn6hnOSK5T4miRtb29zbe//W2Wlpa4cuUKFy9ebGqeBwcR1t8paIpCQFOptCFPFRJ0FGwpKUsHcYprqXmCmkvgtT5q+2oiTQhcIZDS8lrRXAtc21PyuI5XFEjXI8E8p3ZkC4SUQKKEGj0v/KAIiCRbZ/s1o7mhrO9YhEcktQrpFElPfY740IuEMz3g7GLuPcUqrAISI9ZNcvQmnZOfQu+8gTi0wuxKScV1vfZRv33vP+5KiYNED7SRGFn9PG1u71Y1YtJFuCbCKaDsE1qatYxqraE6O6iyjCoEpUobbXVNTHP9oGreeWMV18gtvcPO4i0q5XKDuktvMeFQU1uf6LVA60ouc3cBNRAjkBryCK3UFCVSrC6vM3f/Fs/u/AlLD95mb+UJldwGVmHzxKLDsixisRiGYfj6a7WCyclJZmdna22P7fpDDA8P17wlmuE44qy6Gnpaoq5aQI2MjLC6ukqxeIIK8wSMjY3VpU9KKetupufm5s5IrjOc4TuAo/VXM2uIZj45uq5jWZbvc03fUw14JvRqxEu2FhqgeAnXagjFSCL0OEKLgxZHqhEcqWCbOSq5ZcrbD7GL9dfeitlItEmnhJQqip6GQx5arpVDCzcSM9L1ryOP+jsByOIKxe1l7HIR6To8ufUNiAwh7cZxOGaRjr7JhsfN4i5RH7N3w2i8bpcLu4yev97weDG3TXZgzOezuASbEFbNyB4pJV19jcr98SuvszTfPBUuv7vp+3i5mGNg4nLt35pmEEmkawbsmW7/9qeeIyTfr/7z3+XTf+UrCKHsj92tV8fZewh58G+nSasmTXxKdcNAOmVcM4d0vXNZCPjKz/wMP/zVnyS/t83G8jPyu1t09Pp3NjiOTXffKKFojOzAeF2KIniKquyAt0DXMzhB/9gFpm+/USOfysXGBadER7ZO4bUy94jswDh7O5sNBBfs+4ZeedV3fJbpndtDU1dq5vXjl16hUqone4Rr0jc0QSoaqI1taOo6xd1Vxq++jrV+YCKfyaRQ9h4SCoUpbRy038V6L9UFK1QKW3VqRyPew0Ze5fHDWR586z9w//13Wdl2efgnv0s+n2/J87SVGqgZNE0jEAicOoTncP3UStviUZILPCVWNpt9riCg4+wn2iG5WlHHnS00nuGM5DolqkVWLpfjvffe4+nTp5w/f54rV64QDp98Q/udJrkAIqp+otE4eNyRvt+uZh5aafJb3TsJzmFvLtfFLJXQhUDfJ7TUaqvnoV17ImwNag1zLuB4f1WzTWkd/GkxL0FS6MeqhBStdWJB1Vone1xzG8VoPZLWNfcIdzdXcylakEBqlFBmEiPSibTyWHuP0YwAQj0oHPVIhsTwK8SyQ8S6LyA6bqDse4FIKbFdl7Lr+q4L1sgtvGNsI3E5nV9Qq7SpgkSTDrpbRjGXPELLXkd1dlBkuenFR8o2UjdxQW3tODesmEqH0uZ9dhfeIrf5DMfVQOi1KPCT4N10NLaQ+MEuHW8or4XSBFJjqLEhyqZK2Y0wd/99j9C6/xZ7K4+b+pWU1j3p/3GFS1UG3sxfqxUYhsHAwACPHj2q7bMdM/uqt8RxRYlfYXUYrcrU/VAtoJ63daCKw5/Htu0GtcgnfRVRiNb6rMVZP+YZvgsQQpxoDXHca08DNZjFscqY+UXMwipmcR0zv4yZW8AqLlHefUJ57wmVvSeYubmapYEWTPnuL2z4j0NgkV+6i1Use4E2wS4UoxM91IEW6kWP9iJU7zppl7bqbAqqsHI+LXSOSShZr0Ra/OgNtjc2CGavEOi+jNF1iUCiB4BoE7VTostH7ez4zynhJou9fson8FRhfqgSHn4IHmllHL/yGh/e+iZri09RVf9zYWO5+RwSiSaYuvY6gxNXCUTiLD+drj3XrD5YfjZTM6b/6s/9MhOXb/pstf/ZnJKn/jsEs8n9gBCK73xdPf5HUSlX+OKXvp/L1y7UHst0Zok3SaGMJtPEEpk6Q/fDKOR3OHftdZbnHtaZ6gNsrMyj6QekhBEIoQXCOHY9gZzMZJse782VBY9c8/lNFvK7GIEQtmURT3fTP3aRR/feqVOm9Q2Pk0lGMLefsLn40LNZCYRIxMN0dnayObvvzyUEiUQEQxcY0U62F+7VCMRw9xTbc+/Xv7l00cMHv9tcWZDf8kgwxyqh6CEKWyvM/NH/xuBAH4ZhnEgeHfbXOk3toSgKqqqytta8Rfc4RKNR0ul0S/VTs1qsv7+f3d3d5woCqtpPHDXjP+prehKq6rhmn2dhYeGsBvsLjjOS65QoFovcuXOHBw8eMDY2xvXr14nFWvOLgj8bkksIQVJvfsGQ0lNUKQhMn9Y203XbOkGq5vS2dD21lqoSDodb8nqyhda6d5NQwSkjnBy4FlJovoSXwEEY/sVlA9wSaqDFbZEE4+2ZGQrMg1UhoSCCXRAeJJAcRAiw84uYu89wzAMDVyu/hKKqRHuuEx95jUAiSSTVQzh7E5LXa4az7hFz+bqRSu/br5JbR/25TCmx2iQ8HNm8ZVFFYkiLoFsk6BYwZAkVGyFaJxHD4fZ8vxS9tRZW3ErTVVGnvEVu+dvsLHybSn4DqaUxZQTTMXCPId1CHcOtvbVVIJj2thWqjpEYwEiNg9HF3naelZlbLN79Bqsf/TG55Wn0UOvXktyKl5x4XNtileRq5q/VKrLZLJVKpRYn3e7cmkwmiUQiLC83RtHDySQXeKuhp1FiHVaJxeNx4vH4qVsHqkilUoRCIebn5xuUuwsLC5/oVUQppSuE0IQQrwghfkAI8VUhxC8IIf6+EOJHhBAX9rf7zvTdn+EMx2Bzc7Nlawg/CCFO1XajNlOISwdFb2I036Q13rULKD6JzY65g2JEsPKr5OZvszd/i/zi+5R3nrL79FvsPP4W+eWnKHoaLdRBtOeCz77LhDoaFeqBeCPZsb3wgLXFee5/6z/w4J1v8OzpAoFkH6qPGgwg5OOPZZX8UxrL+W3fxyNh/yCYSsm//aiq5PFDlQTq6htl+MIrfHjrDQBcxyY76G8VUCkXSHXWWw509g4zcv46H733xyw9m2bu4R0Ku/ULVIuz933bI0v5PTq6+wmGI3zur/+U/0ClxKoUwdppUMQ7dvM6zO85RfUnVQxd4NoVfuYXfo6+IY+MnL7zJt1HlHNGMMzUtdd4+uB9Uj4tmFVEYkksy/9exSwXGRjzzr3s0ASxVIbNpXpz8nA0weOP3iPd6W/vsLEyh2WWSaS7EEJh8uqrDE1eZWDsIkIIzEqJQCiMbVXYXl+iZ9BL69RVCBkCtbjGxtx9MoMX0fQAsaCgt7uDyvYipZ0VMvttu/F4mEjYq5m1aCd2pYARSWHEeyhszjW2kgLqvqo/2HWO5Ue3654TuncOSKtEKhpEUZSWridVf62VleY+bsdhcnKSp0+ftq1ErWJ4eJi1tbUT66dmtVi1bfF5g4AGBwfZ3Nyss59oR8lVxXHK/PX1dbLZ5uf29zrOarDnR2umRWdogGma9Pf309HRmpLjKE7jCdEuXNdlZXGRnGORzNYXZiqemso64bfh2WU199pSEShC4Oy3vzn7E4UlJEY7LXqAI3Q02eKF28iAuYzAASe/vw8FqYQ8MklaCEAx0jimf5HVsMtoD6VKa9vi7hvKt+B1JqUEoRLtfQG7tI1dXEM6u+DsYh/zcQPpYbRgBKe8huLGSA29DpFRhNFROy7W/t/Ro1g9Zg7yxBRA9zmuj177qYsqbRRpNj/iQgfZGpmmCInr+nv5+u66BaN4DxI91IlVPK64kJS3H7K1vAGFAz8RqUUIxrsIRFKowQiKpqMIgUBBC3WAUBFC3V+NVKq7QiLBld7qtC2p2AHyi4+R7vEkUzvc0e7ifVRVxXVdHMdpUBRBfSxzZ2cnKysrNWP0dlAtcO7evXvyxk0wOjrKrVu36OjoaChoTNM8scg5rMS6fv16y0Tb0XbCkZERbt26RSaTIRQ6OXmzGcbGxvj2t79NKlVPkn+SVxGFEKPATwGvAQNAL6Dv/1WZ4hUhxLeBfwP8Oynl+ndjrGf4iwnLslpWzvuhaj7f7uv9khSrUPQortWocCgVcjSj3/RghorVSOyEUkMUVuuVM65VQI9msPIbIB1yC3cACKZHCCSHqOzUKyOCqR5Km/WP6aa/CiSe7mZj3mvrKu5u8PSxw9j5S95kdKRG8CO/Sjv++91Zm/Pdh1Xxv8ne3fLfT353i+zAGCs+LYhC0ciOXOTZw3tA/fPRRPPj1dkzyPb6EslMD5meQR7de6fmSZUdGK+1KB5GMb/LxJWbPLz7dsNzwXCEn/u1/xZV85/DXGnjVrYRPnOccJvfDyhN5ji/8BdVD1LeW0YLxvj5X/kv+O//6T9jdWmF5bkZekfOE4klEEJh+dkM07e9dObVJgRiOJpg4fGHdGSbL+rOPfqQyzc/z0fv/nGDggtgYPwi07ffpFL2P97haJxYMkN3/yhmpcyD979Zey4WEPTEVTYfvolhO4TinZjFHbq6s1i5VQKawCzn6Zu8wpOZu2S7OgloLtbeCraQ9E68wMrDb5NIJQmHD7oiNp96qi0tlIJAjOK2/2KXoocRqsHCbGO9Fo6lyBW9llfXMQnqOqZp4rqubw12GGNjY7UaqFVivlrT67rO0NAQjx494vx5/wTI49Bq/XTcguNhJdbIyIjvNq2M49y5czx48IAXXngBRVGoVCpt31M3+zxeyro88Vh8r+KsBvt4cEZynRLpdPrUTDp4BdbzpFQcByklKysrPHnyhEwmw9jwMNuujYtEOg6GpmO2yMDbUmIoSp35vCYUBGDvK4P8jOkrroumiKaTs+97oaJitUaNCQ2JjuDgGAhccL1iUSIo2ypmxcaQGqo42btJwcIjKFogrpwKweQ45e2ZxuekRA0kvJZG18Eub+GUNxFKzuu8bCLrB69YC3ZOIISFa+7ilAtEOqYIZ65AaACheOabtpSUXQcHj2j0HB8kSI+IsNswiFcM/Vgi8yhUQEeiuWVPLdfKi5ooqJpuroU9IrEFKEKh1fV4LZg6geTyEM/0sXeI5BJ2gcrWEypb9cWgGkiw8/jW0Zf7ItR1ntzy9MkbAk6pRbIV2Fu6j6IoaJqGZVm+RZZt23VeV5OTk9y5c4dkMtlSms1hBINBstnsqc3bNU1jbGyM6elpLl++XHfemabZkiK2qsRaXFykv//koAC/1MbD7YbXrl07dQuTpmlkMhm2trbqfkdLS0stje17FP8YGALuAP8X8AEwCxSBMDAFvA58AfhHwF8WQnxdSunv8nyGM3zM6O3tfS4D5Kqavl2SSzEa0+Bqz/kYtQOEAgKnCYchm8xeasDf+zPcOcpuvp58KW89ASWGHunFdQo4Za+2VH3M2qVVxIhnMffq50Fp1pNzpdw2jz78gO7xm+wu3q8z5TYLWw37rRR3CUXjlPL1ii7bLJPpHmBjpT4Zt5jbwQ+7m6uEowlf/6ZYKlNHcnX2DhGJpZi5+xaa3qR9r0laInjJjFPXP8XDu2+zs1GvLrZ8/NKqcJucd6lMhss3P+v7nGWWkJV1jIC/1UUz83wARfW/UfezC1C1IBKwyzmSqQ5evnENtChv/Mk7ICUP777dwDnubKyQ7u5na7We7Okfu8DMnbcoPvqQzt7hOlN68FpOs4MTlIt5X4JLD4SYf/QhAOtLT+nqG6kp8tJdfUgp2V5fopjfIxJPU9knPrMDY4jcAoZSrdslQVVQLqyjAJGgQiihohgRUqkk8VSM1GufA7tEfv59jEiAjlQUWXhGX28H6qHvT0oIpfsJRDso5rcxjulXcV1QUxPkn/x/jd/zofPNtSq1VsKqav44ckXTNEZHR5mZmeHSpUtNtzuMw8mDXV1drK6usrm5eSqhRSv100mq+sHBQW7dukVnZyfRaIvdFEcQjUbp6Ohgbm6O4eHhUym5wPs8iUSChYUFBgY8QnZra4t0Ov1JTrc+q8E+BnwyKc7vATzvD+c70a4opWR9fZ23336b7e1tXnzxRSYnJwkYBnFNR9gucr81sR04rosqJdp+u529n9Z4EoVScY83ofd9L7+EomYw/D0GwDOeD2o28QhooU5s28JBw3ID5ErCS2lUjlxMpU0g3roBfXXilFKi6HH0SB8AVU/WAAAgAElEQVRauBdFC+GUt7H2nmHlF5C2N3FL10QLx+vMLGv7CiaI9F4m2JFFWhu45i5CDZIe+QKR3s8gwsMgvCCBPcemsE9wgXc8bOliuy6OdKm4R5sSj4fk5PNZF4KQEMQUQVRVMBQFpVWC6zQ4emyORevns2q0dkOjhVrbzqnsogZaay2s3ni0gvLOYp0f23Eobs1jlXKoqlqTzB+VkR+NZg4EAgwMDPD4cXNj3uOQyWRwXZe9Pf8WlZPQ0dGBrusN3hKttCtWMTIywvLycksmrs0UYtX2ycMpiaeBqqoEAoFaC4KU0neV/ROEfwb8uJTy56WU/6OU8g0p5bKUcnf/v38kpfxN4G8Cv4a30vh/CiF+5Ls66jP8hcF3qwYTio7Q/G/qqgbgDXBNb+HG76nKNorROIdIt0ww3thqIx3/dj4jlmFn9h3yS0/QIv3okX6cSpnY0CsN/k2p7sY26sL6YzoH69sey4UddnYLTD+ap6B0ER98EaFoFDfn0YxG9Wvv0ITv2DJdPQ2PVVP3fLfv8W/zPmxRMzhxhY2VeZ5Oe21k6a4+39eszD9ueq6kOrM4lonrNC6CLj55gNLEz2t1Ydb38S99+Sc8RfcRlAtbaM4OgUAIu+w/Z+rBBHbFPzm42Mz/qMnnUvY92qzyHp/67KfoG+hkZe4Rui75sa/8Vf7ez3654aWdR77z7v5RZg6p1aLJekVcdmiCUCTOk/u32F5fZvTCSw3jyA6dq5GVpUKOYDiKphtksoPYlsn2ujfvDk1dY/HJfRYff4QAokoRQ7Fx9tsQpIRYJERXXKOvM05PQiMVUUnoZdz8CoYmCKkmAdWmIx2huzOBpqlEOgZJj7xItHsSCSQGr6MGYhQ3nrH99BbFjSfY5eZpzbntVWbvfNP3ucPnhmN715HDNdhJyGQyCCFYX29NfHM46KeqqH/8+HEtCKhdjIyMsLS01LR+OilYqF0j+2YYGhpiY2ODQqHQVv13FFVT/2rb4p+D4J+zGuxjwBnJ9V3Cx01yVdMdl5eXuXr1KhcuXKi7qQupGkIRiDakmwKP3LCRSEVgt9Cadxg2EqcNtkXiqbncVqkToTYk4/lB1VSQDtLcRdhbhNQ8dmkVu7yFbRZxXHBFEKlGUQJRlEAaJdCBCHSgBDpQAhnEoX9X/x9FJ5CaQtHCOJVtzNwcVn4e16f1oAqnvEEoM4bQvHEb8X5kfBg9HMApryAd75wIdVwkc+4raMkrSDVKWbrsOjZF16lROlLKWppi0XUoS5eS9L69dsnFoy2LCmAIQUQRxBVBWBEYDcq8dm4yJLKt7VtXfgns/aSrk9Fspb1xu9Z/J6GO1iZSM7fSMnElXYdopnXft73l+wA1Iuvo8feLuM5ms5RKJXZ2/FfUj4Nt26RSKWZmZk5d4IyPj/Ps2bM6U912ipx2TFyPWyE8mpJ4GlQqFYaHh5mfn6dSqVAoFAiHw5/YVUQp5S0p5YLwu2MDhBCqEEKRUq5KKf8PKeWngd8ErrZqlnqGMzwPnve3FQwGT/2bb9ay6Fp53CZzl+pDZFWhBRvVGK61R2bi06SGb9Jz5a/RffELZMZfJxhLowT99mXvv67I7uzb7My+ze6Td3CtMqaIEEgdLOAZhv8Y4+muxrHtb7q28Jg7b/8RBZEm0jlCpr8xHVFpsrymaY3vZ5tlkpkm/mZNlN/FnEeYROIptjeWakl6ALGEv6KlXMyR6WlcvMwOTtSROEdhVcpMXH7F97n87ibjl28wfvkVpq69xtS11/mJn/0l+oaGG7Yt5TYIquYBKXKMbYNj+hMuRpPyppj3V6mJ/TpHOhaqqtLdEeU//6Wf4Gt/5we4eHGQwcFufuWXf4If/MILtarMtR2u3Pw845dvMHXtNS+N+tC8+uSjW3QPjKHpAaauvcbawixba4sAbCw/w3Wsut9kIBRlc/lp3bjmHn7A1LXXsawKe9seuTNx+Qbzjz6gUiowMHqeVFTDtctYUiWY6MIIxZi68jJCmoycu0JELTcQdKWdZeztWYKVBcKhg3le2mVcq0Ig3kNi4BrbT281kIxOE78xEKxv7GHE/BfTlUNhD9V9VBX1cHwQUBUTExM8efKkJaLqKOkUCATo7+9ndtafcD0JrdRPJ11jq0FAz+Nterjd0E9x3ypUVWVycrIWKPTnwBP1rAb7GHD2RZwSopoSeEp8XCTX3t5eLd3xwoULTT0qhBDEVb0l8kPBI7ccJJV9xVbFdVFP8XnL0qmRMc0u+tU8xep/rTqT8qrH0dG/fegnS3UFLnqsmUJLIu0CbmUTp7SKW/Iivq3CAnZhAauwgFWYxz7078P/L2QZ127PANspbxDJXiDafx1FqxDSClSt+bVQJx2Tf4PowOeReoaSK9l1HUr7wQAHxJZD0XUouU6DmXxFum21iYJHSKoSgkJgODYxVSGkeImYTc/zdlR3AKIddVZ7JJ2iN28hqRsCra16Sbf1iGc90tzz48hOiaRbb18LxlqXoe8seC0BiqKg6zqu69b93hzHaSC5qquBDx8+bLvtx7IsQqGQb0JOq/AzwT/aVnkSkskk4XC4qZF9FZVKpSl59ryJR+ClAkUiEcbHx/nDP/xDnj17VpPNfxJRTeuRUjpCiJAQoq53SkrpyP0YtH1jVCGl/J+llL8um8WjneEM30N4nhpMC/ubaAsBRsj/pliozRfkjs6xQmgY4QwIlXjvZVQ9RCieJdo5RmbsNaa+/+8z/rm/R8f4jZoy3NxbRA02tsJJM09ufY6Vp/cJZi+jBeNYe02ul07j91HerW9rXJ1/zL27d9GiPQQiybrn7JK/Wtks+D/eMzDqv33Zf/7dXPVa5Lt6hxv8sjSf1swqkh1HPGk1Hdexka7L5mrzG/TlZ41eTMnOXgbGL6EoCo8+eIfp228SDko+96Uf8cgXpwKuR2RZZpmgZtVIJzglOdvkntVocgdn2/XzWDSRJJ2OgWujayBdm3A0zCs3r/JLv/ijTF26xsN736Kwt8OjD77F9O03WXraaK2Q7uolnsowffvNhpbNp9N3GD3/IgCjF14iEAxRzNXbLqiajnRduvs8H6eJKzd4+MG36OwZ4sL115B2gZAhKBd2CcfTdGb7GBydwjIrxGJJND1ItKMPLRAGIeiZeJHhCzcZHB4mGjxIhFf0IPH+K1jlPLuLH7K7cBenSfupY/k/bnSeZ2/lMSGfoAagTjBgVw4Wto9T1De8h2EwODhYS6w+Dn7Kqp6eHgqFwqmtb6rBOUdN8Nupg04bBHQYsViMRCJxalVaFYlEglgsxhtvvPGJV3Kd1WAfD85Iru8SnncVslAocPv2baanp2vpjif1RYcMg9x6o5FmFSoeqWHvk1tHUXEdxAnXPk+vI1ERqHgUVaV6oVcUXGj4O7pLB4FTWw2VPq84dPPuSnZLGrY0kMeczqpm0Ko6SA+2RpiAt9oaSk+2tK0W7iaYnkANRnFKSzjldfRIP0LouEIn2v9ZUhM/iggPU5Iau/vqLLmfZmgeIrbsEyahQqG5mqwKARhCIayoxBWNkKoSUAShVuXCLaqnalDaIcWcY49n41BaNA53TVxOJtukXUQ1/D1RjkJp4gXih0AbxJXqs/rdDDtzdw5et19kHS4YmnmuhUIhstksT58+bfm94CCpsJqQ08r55ofOzk6klGxsbBw7zuMwOjrKwsLCsTesJ3k9pFIpgsHgiWRZM1SVcul0mn/9r/81v/d7v/eJLrCqaT1CiATwD4AvVp8TQnQJIX5LCPHfCCFekFLaUkp5FmN9hj9LfDctI5RgV1NlssDyVyK5zd9LHiaXhEo4PUw4NYxqRL0FVdXAMsu1m08hFIxIkvTYiwx/+itkpm6AEMT7LxHtGSU5cplwph8UlcrOPIpmIF2HpftvsrG2hgh1EYw3qrbMfGPrVGFznv6JK/XjdV12Ntd4slpE77pYswEo7yz5dgtsrTzxPV5C+i+u5HdWfR8vFXJceOn7ePLg/YbnmqUyArjuwfsEgmHGL75U84baXl+if6wxnRJgb3udzt5hADQ9wOTVV9ndXGX+0b2akfrg6Di/8Ov/FLWq1HKKIC2k6yIqaw0tj6revFYRin9NJZoo0EWTdkrlSP0uFAV5xIJZ0TQkkmgszI/91fMMD3ayPP+obrxd6Qg//LlzJKM6vX29LD97hG01V6IFwlEmr77K7Efv1pRahzF+6WXmZz9kY2We8UuvsLu5Rkd2gO6+YVzHoiebRe4fq1AoRGHlMbmtFbaWZ8EqsDT9DpGOAXomXmbkyl+Cyi723lOEohLvu0ys5zyBRBY9kmFn7g7ufhuhWdgit/LA93fpmI3kjBHvZu7Db3vfUwvq+8qRBNF21Fzd3d11idXN4EdyCSE4d+4cMzMzp/YnHBsbqynQj3uvZjisxHqekL+enh4cx3kusgygr6+PX/zFX2R2dvYTG/wDZzXYx4Uzkus58LznkxCi7VafcrnMvXv3+OCDDxgYGODll18mmUye/ML999tZXkE7NG6JRBMCVQgsXMxjCGCPbjowNJf7NIS6/1c9mRzAwsVCeoQZboPa6CSYQm/pFaqqEoh1sWK7rDkqm26IHRkhL3UqUtTaJQUOeny4tTd3ChixFrcFXGsHNZDyfU6oAQLJMYx4P7gF7OIyVH07pI1T2SHS/2ni5/8TyvHzbLkBtmybomN7pJZjU9ont05KwjwMNRhoSDMCj8gMCoWoohJTNEKKii4UhBBtHiHaiwD0XtDelkrrhsCttiEChJL+3h1HEWhxO3nMzctRqFrrRJ/bxJ/DD9uHSC7wiiwhRK3wOe5a1d/fz87ODrlmvh8+qBZBH0eBMzk5yezsLKZpnuqaqmka4+PjxyqxWkltHBsbO5Es88PBjac39q9//ev823/7b0+dvPu9gENy92vAf8b+yoIQYhj4X4GfB74K/I4QIgNnMdZn+LPFx9GueBqSa2dnh/du3aZk+ZMV0imjNpjTC1ynTKTzYsP2QtEJxbvRQ2lCySFSfdfRgynUQKLmQaXqYfRgsub9I6XEdW00I4JQNOK9U4z8pR9n8JXPMfaZr9L/8l9h6PUvce6vfo3Jv/K3efnHvsalL36ZwRc+RWpgiNLeIkZIJ3vxUwy/9DqXv/gjTH76c6T7Okl1N8578UTjwt/GwgwIhZmP7rJRDpLqv4BjVej0aQ20zTK9Q42LgXub/osKxdwOiY4DH69YMsPk1VcZnLjsa0gPnvdWIOS/MDV7/xbXXv8BuvpGqJSLTN95q/61c4+bzs3Jjm5GL7xILJFm5s5bNRJm/tE9Mtk+fvXr/9P+IqoHIaCU3yW/OYseaDxHFFXHsfxv5BXNf8GsGfmlNElxFD6G9IrRuAiu6jrlvR10w+Cnf+qLfPmLk/zAZ14gGFD50men+OkfvcHkRD9/8/sneOVSNzsby02/p/HLrzD70bt1LaSH0T04zszdt8ltb7C1toiq6awtPqGzZ4hnD95FcS0WZ94FvFbHkAZmOc/exiJDk1dxHa9udswy+ZWPcB2LZGcWXIud+bsUNheQSgCrmKO05ROKIyWBaKPq3jEbVYOFiopdU3j5X2e25+7SMXQVgMqRIIiqol4e08FSRauK+mbEUygUoru7+9SK+moQ0MzMTK2Wadcb67CR/WlhmibpdPq5ybJwOMxv//Zv86d/+qefdDX9WQ32MeAsXfE58HGsJJqmSTB4sq+UaZrMzs6ytbXF2NgYFy9ePNX7S9clpQdYN0vo+6l0xxFbda+VElccTvPz2txaQdl1CLWRtiiBiggQkJW6KcZxXVZMk6VKmQ3TJG/blPeLjphuEFLF/pxk7P95EHgxzCJ4DWU/lzGMTdLdo9NaRpf1hrGaHsAUCrTy3UgbI9pBqbJDVZemR3pQAhGc0jpO+WBVUioaSmISERtBhLuxhcEegFAolMtIxUb6KHgiisbJVv+HsN9mKPFaT3UEqlCO/f5dPF6s9dOq3fNPeib3rW6uGG14yre+iqUHErRya2NE0xSbe+PWYJdbT0KUdus3VeWdBa9NoYVzsLy7QmlnhVDSMyquekO0Iv+uFlnT09Ncv369JT8yy7KIRLwbilgsRjKZrEu2aQdVyf7Dhw/balU8jHQ6zerqKqurq2SzjWbNraT2HCbLjqY+HgfLsuoKwnQ6zY0bN/j93/99fuVXfuWT6stVHfQVYBOouu9+P/AC8JN4F7t/AnwZ+O/2/SHOZPJn+DODEOLUN0TVNNpWkcvlaq3V586dI8gGlU3/ZF1FC9U6/1QjRig5DNJFKBqRzEXK+SWMYBItEKupV6OZcyBAUYO+6hHHruA6LmUzRyAcQwgVs7SLomhI6dTN26pm4FgqwnUQqo4R0jFCUVK9I1SK277XpEAkSjgep2t0EscyyW1ts7G4wMLDh1iFxg4AxywyceFV7r33Bjtb67y/s8mLr7yGlC5ri08wghGS3UMomoEQCnokzdKzmbrjtb22QCQap7CfyKgbQTqy/URiKcKJNEtzUcxijt3NFXI73himrr3m+52XizkmLt/k4QeNPluTV15lb3ujpt46Ctuq0D96kYXZD/e/P53eoUnCsQRGMMQHb3+j4TWu4/AP/qt/TDDU6I+mODliieY2Bq5dQtUbF/EUzf9eQA8lcJ1Kw3mh6kHwqWbMYgk9WD+Ph2MJCps7dcde0XSk41DJ7RKMJ+nt7yHbY3Np7PvqtjMCQYYTYb54o49vvLvE4Pgl5h7dAyDR0U26q49HH3ihbpurC+iBIFbloA1QUTXKxWKNAEt39vDsw7eIBhRSMYP4+YvsrnkkjRGMMDA8ztrsXQCC4Tjbcx8AEIp3IMwdrFKOSDjI3sLB4p5VzuNuLWGEk00N/LVQgkquUWEW7b2EVdhChDJsLDxhZ/GD2nPNSCrXsWrppTtLM9hmqS6MQVVVHMdpKYAmGAzS29vLkydPGB8f993mcM11FAMDA9y6dYtcLtdSOvVRdHR0sLq6yvr6Ol1dXacygB8ZGeG9996jo6ODUKjFzopDqFQqxONxTNNsOTW7GT73uc9RKBT4gz/4A86fP3/q/XyXcVaDfQw4I7m+iwgEApTL5WNJLtu2efr0KaurqwwPDzM1NfVcN026roPjkNIDbDY1XKyHkJ4w35SyRohFFK0tjsMFTOkQQD1x/NUiyAFWLJe1UoH1Spldx6LkNNeE5SyTom2R1A3UqrJHCJD7pvbSRdlvqHSAIhprMsW0mqTL0AgKB1WaqK6F5poYnZ2InQfo1g6qPJ4scK0cwc6LOK6LKzznJ1cNIUM9CC2MFszgGikcxUAREinAlhJTesQP0gGj+Q1+RTpotOYDJ4CAUNEQhJSTv+/DaIuEAhDGsUaqz7d9OyEJjpfI2IKqqhX5OYBqnEw+g2du6hfJ7gcr3wJrtg/XKhPtHCK/5l+YH8X23J0ayQXeTZzjOFQqlQY/rqOIRqM1A9FW2uyOrioODw9z69YtMpnMqQqc7u5ulpaWnuvaNj4+zvvvv086nW4o0FqNpj6JLPNDuVz23Xd3dzf/6l/9K77yla+09gG+NzEGbEgp14UQGvA3gG9KKf/vfWn83wWG97f9RLJ5Z/jk4nlIrlavNcVikUePHlEul5mYmCCV8lTb0jH8SS4lCEJF0WPooQRGuAuQ+x5QEj3SjRaI+6pwHNvcX2USVBfMpJRY5T30YBI1EvFasITttSyGU5iFzf2FkPrvQTOi2JVcwyKJpodw7EYfIi0Qxi7nEEKgGQFS2SypbJa+8TFWHz/i8UMd17aIRoOMjfZy5+4s2AcqGOm6vPv2N5m89mlkbIjZ+SfI5QOyYOTcdSKZYVKpJPMzB+2G2YFRZh/cYfzKqzx9cIelZ54/0dTV11ifb/TE2ttqnkYnlMZjOn7pFaZvv4EeCBEIhmtthkcRiSfI9AzS0dXP3OMPmH/sEV7dA40G+wCjk+cYmbrU8Lh0HaRrHnsxlK5/PamoBtK1G5RbimpQzq8SPGKCrgbCuD5qRE034Ij/qKrp2KaFHqifG11FQ9gWuc0NwrEYVqWMrJgIJI5tY5bL+4umMDqQZrgnxty2ZHFWZfzKDWbv32J382Ahd2ttkf6xiyzsf3+hSIx0IkZpd5nOqEBRQLVWCUZAD0axSztoeoDCzhqTV15ld+lhjeACGJi4xPrDt0ik0gxdeR2sApV4qI7gAoj1XGDzybs4Ef+uCvDOcT/s7hXZWpinUvig4TnzmPTF6pQ3++bv0TVxg+GXf7j+/TQN13VxHAfVR113GH19fbz//vvs7e0Rjzd66x3XQlhdqHzw4AEvvPBCW8FJVUxMTPD++++TSqVORXJVjd+np6e5evVq27VctUbr6+vj1q1bpybLwLtmJhIJ/sW/+Bf80A/90Ce6bZGzGuy5cEZyPQe+k54QjuMwPz9fU0e8+uqrp7pwHYVhGJimSTQaxUGy06y/XkoUBDaub5tc2XUIKEpbLWuWlCjS8/USgKoqyH0CSiJxJWyZJZ4U9lgrl5DIltscdaEQUFQc6bJSKpI2gpTbILTzjkU2FEWIwEH/JUCnJ5kXgIqLJl0MIQkpKopqgOKlQUoEUnhbStdF4IJrI3BxhYKNoCK9tBsdSVRRcZtI0P1gS0lg39PMDyoCQ1FQ9lV21XOzXdJKHiquW4JQ2/OIVwxwWiW5nLbGr+hx3MrJccyiRZJNKK1/sGCytyWSyynvokfSWIWtlvYbTmRaJrl25m7Te+UH6h7TdZ1isXhigQUeUfXee++RyWR8wysO42jBddi8/TQFjhCC3t7emmS/lfEeha7rjI6OMjMzw6VL9TcffumSzTAxMcGtW7dIpVItEWN+CxXz8/P8y3/5L/nSl77EZz7zGbq7m6SIfe+ievJvARkhxAW8YmsE+Ef7z2WALPCHf+ajO8MZPgZUvQv9rg3lcpnZ2Vl2d3cZHx8nk8nUXdeEGkBoUaSdB6EitCh2eRdV2KiKxEgOIYSKlAKhBlDVg/rNReBY5X0lzqHxaAZCjYJ0kVV1uXQwwh2HtglQyW+Ca2OEUwSiGcp7y3Bk7va8lRSOyqFVPUglv4sWrL+2CSEIRDso5zbryrpgNE5Xfx9/7UeyPLg3Q8BQGRrpo6e/h/zmGuHLPTya22Zr1yPO5mZus7KdayAftzdW2NlYZnke+kcmSaYzCFwC4QTZoSmmb79Z//2X/MmF5bmHJDq664iVKvI7m3X/7h+9wNPp2wBYlRITV27y0CdRcWD8Iq7jsrmywMbyXN1zawuzTF55lZm79S2OP/erv4FfiFklv0IgFMGxy6hNlFnHwbHKaIHG1kK/NkBVC2AX7UbfLyOI6zR+f9JtrGm0UARZ3EUTkvz2JpYrwLKxy3kMTcXQVC9iU3rVmItgJCP52z84zrv37hISFY7qIQu7W2SygwQjUQrrT9HLK0hN4EpZM4cHT32lKiqhQIBzV19jd32OUm6LeKqTTDpCULXRyrMMD3V7dWB5m9zqw1rLaBXRnnNsPvFaHc3CNrFMH5W9xvOjWGq834r2XmHm1h+R6p2kUmhMms6vLzStQQ+PY+vZnQaSq9q2WLViOO4erkpU3b9/35eoOsknKxqN0tHRwfz8PENDzYK2mkPXdYaHh5mZmSEWi7VNckF9EFBvr384RzNUKhUSiUStlnzw4AHXrl071X12qVQiEonwO7/zO3zta1/j3//7f/9JVNSf1WAfA848uZ4D3wmSy3VdFhYWePvtt3Ech5s3bzI0NPSxEFxwQHIBxDSDxCEPgP+fvTePkSO/rzw/v7gy8qz7LrJYZLHYzWZTbLa6ly1blseWIO14R2OssR6NZ4y11p6xAM3as17swsB4d2DIsmdhjxfjtYAFDGkxO7AtS/B4NRh5fB+SLLW61Ty6yW6SRdZ9n3ln3L/9IzKrMisjqzKLlNQC6gENNDMjMiKjIiO+8b7v+56UVatvGRrPV2RrHygfiSdlyy6qlDIks6r/SSnxgoCc71EKPMqeixUEFF2Hm9kt/nR9gT9cesh/Xp3jXm6HLbvMrlXB8KNpHQXIaAbdmoEGZB2LDavEtl3BCXy27DKxDv48vpSslgtNiTH73wdwpcBGpYzGbqCQ8wIsz8cPQporCAJs36UYuBSCgAIKeTSKUsGSYt+o1kWwFwQdU+7+oWOtAnFFJamoxBUVrTqOWH9eyg6dtjrWuXacVNuJOkuCaL9ILFltJrNIDzUisv0wArdAuxSblmzuvLVCsq99GbYS0ZluhcO+XBCOOS8uLhKPx4/1hlAUZb8Td5w6Iqrgajfp8Ch0d3fz+PHjE69fexDd2momO9u9Xmuatk+WtQPbthtILiklpVKJs2fP8qu/+qt88pOffCKPie8G6iTvXyRshv174P8FbgF/W33vMpABam33760veYrveXw7fLlc1+XBgwfcvHmT3t5ebty4wcDAQOS2VLMPoXfjWgXs7CxC2mh6qCiWgYeip1A0s4kMUfQkEolnl/BcK3Q3VUyEEofAqqq7PYR0w4ZZHQLfR9VTIFRk4GEXt6ohKSp2MYtd2MXK72AXdpBBgFPKYxV2cOtIo0TPEF41wTDwXCrZLZxiDkXTCZzGJpCiqviBRNM1rrxwmYvPXUJPpEh092CaOtMTffzX33+B918f5/JkL1a5wPnp5xs+Q1cF2e01zEQ4SrU895C7b3ydt954lY21FVbm7jcd26NSD/uHo8fiVxdniKcymIk00+95hZX5+w1G6Vur8/tG8gBDZ84zdv5Zlh7dY+atVzl/+XrTZ0opWZlv3L8f/gc/Guk95rsVtCo5EESo5WqIMuff317bTcDaNpuXb6VYiqq/6pVdhq6TjGl4doV4zGhqNqm6gUQjCCSB73P5XDc/9kPned+lNH1JQc1pQxIwfbYbmZ2lLxYSPDFNENNUJIKx81eYvvoKZ89fYmfuJisPvkEiYdLfnWK4N8b5sQRp3SKmeqjKQRVW3HhI93xBjpwAACAASURBVPgV4j0HNZRqJKlkG+/3Zle0CtuMJ0iNv0CifxKEIDVymZlbXwHADaIba3Y5S2b4YuR7NZ8wgEouWqVfn7Z4HJLJJP39/SwuLja9144Z/MTEBJubmyc2bx8cHCQIAnK53IlILmgvCCgK9Wr77u5ukskkq6urJ9qH5eVlxsfHef/738/ly5f5nd/5nRN9zncTpzXY08GpkusJ8DQKrFqihpSSjY0NZmdn6evr4+WXXz6xP81RqCe5ADK6gZSSou9iywCng5+IIwNiUqnehEL/p0CG5FeUAiv0noCs67KYz7LnWmQ9F7eF4ioQsOk5TBhpsp5DStMxhIIV+OzaFoUjUl58KSl5HqaqH+sbJqXEVBR0ISk4ZWKqga5qSBGm8oTjjbJloqECDJkJ/P3zoU3fsc4MsKj4Hobrk4zH0dscQ/QlqB2cppJ3mS+XasIRxWIgFRzHxfN8DF0j8OMIAUJ6IFv7rRiJASrWTsv3w131MHvPYu0eb+jZyRi8HmsvtRHAO0bxpWgG8b5JAsVka2sP33NRNR3XdZmbm2Nvb4/JyUkymQy+7x9Llnd1de0XF2NjrY33Pc+LVFudP39+X2rejgqqHo7j7HtD5HI5uiLMjtvBxYsXuX37Nt3d3ei6fiJlWH9/P5ubm2xubjI42JxCVg/Lsujv79//d+3YCCH4kR/5kWNTb9/NkFK+I4T4l8B/B3wZ+IyUsvZE8ZPAHPB2ddlTL4hTfEfxtBqNyWQSz/NYWFhgfX2diYkJLl68eOz1Ukudp7zxh+x7cdYlMwdOFmlkEIdShX3PQQgVLdZDgIqCF9oVNFgiSFDiyKBCfSSMBIQWQ1PDMU1FVYklw4aNVymiqAZGotGTR1EN3PIemhnHdx08q0Tgu/iOjWuVMDM9xLvDMTjfsVA0Ayu3t/9QrhoxYukMdj7boHgTQoCeALeMEIKJ0W56MzFiqk88ucvgs90UKx7beYf3Tvfwn17d4MyFy8y89c2G/VtbeIimx/AO2WcUc7v0Do6xu9lsZq21SjSWkotXbjD7zhs8PGQuD5DdXufStfextTrPxKVrrC88bBhffHzvWwxPXGR9oXFMspTfY3RimtWFhwgh+Ec//S8iN+9WdjDM8P5+FFmlqq3vjb7nElX5SyC/vYwRM9HN1L4KUPrNdY5uJrGLfnMqY0RTUjfj2EHQSLypOr7vNt03jViMYtklpikkEjEQCsWKw0h/itH+JIqqULZ9FldzdAcB8fEkqgIzi1mkhL6hUVRVobg5S3Y5JFknn32JTFeGlbt/y9BQLz0j/QjpIMwkmaGL7C3eRjUSmN3jCCEobS+SGpzEdyzi/RPk12ZwD/tsHfqeaiyN0XOOR7e+wsD5F1l/+AZmqof1rdf2azfdMGhVaWotEtfrTesr+dZWFDU1Vzu1yNmzZ7l58yYDAwMNHlztrFsfBPTCCy+c6Po4PT3NN7/5zROnQ9e8Te/fv8/Vq1fb3ofDlhIXLlzY9/hqx7e6HouLi/v7/+lPf5q33367o/XfTTitwZ4MpyTXdxE1T66trS0ePXpEV1cX169f7/gH3QnqSa5a8kdCKNgImjNGmiGlRK16RrgyYDdw0QOFuKLgV5VdrgwwhAqiqvgKfAqOza5dYdu22HWstuhmU1HJaDoF1yGhqGy28FJohaLnYioacMDaqEBcVdCRBEicQFIMAnJeXZfFdRk1NLz9QuTovQ2ANavMSMzE7yDlL++7dKlGdczxAAogPR98n5gRw7EtVE1D1TSSSRO1A/WUX03D/F7z5QpVb+HoZyBVZOAT+A7St/GcMnYliyqtBjIuUHTKO4/qtqOimT1osS5UI4WqhYa+QlFQtPZUX7Gu4bZILqfcvvm8CNo/VlZ2BdWI7xdTih4j0TeJL2Jkd7ZYn7+PP3vQ7dpeuk9FpFlbW9t/UKuluDqOQxAExz641Yiq/v7+lkSVENH+cPVJPVeuXOnovHMch2QyyaVLl7h37x7Xr18/0diiYRicO3eOmZkZLl++3LYf12HUPCq6u7uP7Goe9uRaXV1lZOQgGewDH/hAW9u7c+cOn/jEJygWi5w7d47f/d3f3ffm+LVf+zU++9nPoqoqv/Vbv8WHPxyOpf7Jn/wJP//zP4/v+/zMz/wMv/iLv9jx9zwKVSPTv+Wgc1iPLwBFoHku5BSn+A7gaZBclUqFhYUFlpaWGB8f58aNG21fdzSzFz1zDjc/F9oXCIUg8EEYCDSc0hZGcviAQFDiKJqyf38VMkAqMYRsrG1EtUEjhQGymqgI4edKq7rtbvAPEnHj3cNIBG6lgG9n95P6jEQGzy4hAx9VN1CrBJHvOlSy6w1EiGqYJPqG8VwX0wypFikl5b1dlFgcDhEqyd5+SuuL1ATH6VSc6QvD7Gzn6BppJNt+8HIa23tM9/kEmiqw3ADX9ZAyQI8lKJc83ACKVsBWMay5+kcmIkmuctWo/jBGJy5x//bXSHf3UcxFN7HW5me4ePUGj+6+FjkC2N07RDLdTTmf3a+dNMPAsSpcfu8HeP49l9A1iQxcENr+OeiUtvYJLgChtn68UjQDxypimM0NELuYxcw0jrfvrryDk32MojpYQBBItMR5+s8+S9Bi2sF3XbRY43kcjjU21rNCCGw3wIwd1AXJVIK9nT26Es2/g0w6jlvOoyiCRFwnEdeBJNu7BeKGSjIB/d1xiiUbVfHQdZUfuD4OQkFTFaSUWAO9FOjBrpRJpeJoOFz5/r+PtXGXoJpqKN0ybiVHcuQKewu3qRQPiIpAMbCLWcot1FOlQr72hUmNPM/iO9/CqaqjaiOGVrGxZjPjCVrlSztWKfJ1zw5fT/SOEait1fyKoqCqKp7nHTu2WK+oP0xUtXO9y2QypNPpYxuVrRCLxYjFYqysrJy40XgSb9PDtWm9BUYnZBmEdhE1kiuZTPLSSy8du867sf6C0xrsSXFKcj0BnrTAqhFcAO95z3uO9cF5GjAMg3w+j+/7++NLQgh69RiBB0W/+eFfQOjPJSVWEDSNtLmBj+t7OIFP3nPwpMQUCkhJ2XPZqJRYs0qMxVPkPaclZaRI6InFUBEUXIc9xyLrhDc8Q1HoNeKUWxh2RkFKiRP4dOsxAgFl36Pke5TaCOJbdTzOmhqWaP8hu+j7mEJp+7xQEKgCDKWWVykJqkb0wjjYbqzuvHBkQLzDEcFOSasAgdKRL5fWGcmFhsQLlWzSRwZeWDD6NoFfAa/EflqiYmLlm6XbGjR/qcBFjfXg29XiRfp4lW28SnMylGL0UlzfID16CUEF2cKwXjPb+00qQRnV7MK3ouPN6+EU2r8fCVWj++zzlEolstubrM+/Q/C4dUzza3/1JZ7/4X/Kyy+/3PCgVl9kaZp2ZJFVI6papQweN3p3OKmnXdTMThOJBENDQ8zPz3PhQrTp73EYGBhgY2ODnZ0dFEU5Ecml6zqTk5PMzMzw3HPPtVzu8LhifYHVCX7mZ36G3/iN3+ADH/gAn/vc5/j1X/91PvWpT/H222/z+c9/nnv37rG6usoHP/jB/VHKT37yk/z5n/854+PjvPTSS3z0ox/l8uXLHW+7FaSUgRDiGvA8oRdEHvislLIgpfyzp7ahU5ziOwwpJeVymYWFBc6cOcONGzfa9u2rh9l7BTc/h5YcQ01NoKWnGq6v0tkLfbsAgsa0aCEkUnqt79FVz0spDMDfJ7ggDFuRIoaQdt1rEj2exvcqFLcXiXcPoWoGZmaAwsYsiqaiKCpCVVE0AzPTj+9VmhQ/meEz5NbmMeImQgiSvX14ts3u8mI4RikEqqphJpOoyW5k+cDLSNd1EFCp2OiGhla9D6USBkkZQCK8Vvp+gOvrWBUbsElmwmPv+ZKBdMDyns/iTLMROMDawgyqpuN7B6Rb7+AYub0tHKtM//A1dtaXmtZLdfUxMBqOGZrxJJVSI60xPHGRlbn7FLLbTEy/Bz1m8qhOeebYFj/50x9DBha+7SK0JKoexy5uoOmHfLFUncB3QvIzAk5hJ5LkctyDCjvwHHZmv0EsoaKkUjiVUNmtKAK/PMvWnEMQSMwIfiWIqOVVzSAyjdGH+ta6pmkkU6nIIB9NU9kqefSmDykU5cE5L4QgnTJJA+WKg2kaVCyXoBpiFTd14hQR3d0kBgaw9tYoLX+r4fPMnjNYxR2SA81G8lZ2je6zV8kuvtn0HkDglEkOTrO9uc76zb9ufM9rUaseUbdX8gc1ZLJ/AluamPEEmqaRiPXz6M7Xia8dPRmgquq+Cf1xjcZMJkMmk9lPGZRHWMNE4fz58ydWQUH493Mch729vf2gjU5R8zaNCgI6jCAIIp+benp62NzcZH19vaFxeByWl5e5ceNGR/v7bqy/4LQGe1KcklxPiJOk+xQKBR4+DGXPhmFw9erVb9PeNUJKiaZpVCqV/YtK/YWlX4uhCYWca6MIhUCGqX6Hfblq3l2+DCh7HsVqd09I0IXAdV3yvsumVSZfN1K4UimS0QxSqk7Rd5FSons+Kd1AairbdoWVcvSImRMEOEFoWn9Y+XRo5+gxTDRFIevYbDsW247F2USacsRN/yjsuD5JvX1z/YLnktIMDnNoAogpCroIjeEhNJIPCNMVteoYZ7iwOJKQqvmgdUKwBkiUDmiugA5HFttZUHrgV8DPI1Hwis0FaPTOWKFZfZvqJz3Rd0ByHbk7BcqbjyhvPkKoOl3nXsTs7ke6jesKOiBVE4PQBsnlWzlimQHsfLRJvmam0bvOUiyWWZ65TbfcYeHu37W1DylRaJkk00mRdRRR1Y5svj6pp92xa9d194uhJ43EFkIwPT3NnTt3GBsbOxHJBQdk2dbWFgMDA5HLHO5AnpTkevjwIT/wAz8AwIc+9CE+/OEP86lPfYovfelLfOxjHyMWizE5OcnU1BSvvRbGtU9NTXH+/HkAPvaxj/GlL33pqRVZ1SSffwz8n0C8+t9D4P8RQgwBvwD8gZQyImLuFKf49uMkjUYpJZubmzx+/JhEIsHg4CBTU1Mn3gc9OULq7IfRk2NNqXgAUusK739etJG6IEAKEykrgBYSW7X7tRBIJYEIWiS8qQmkd4g4Q6LF+0j2eQS+h+97KIogM3KB0vYCavVa6NllnMIenmdjdvU2HUtFjWFbFgoS3YyjxWLEe3qRdnmfDHQsC1QVO1CIKQfkjK7rCMDzAizLDacvhcByQmUPhMFDqqpQqTgNI5maKuhJqqHixy1jdY00+Tx6rs3EpWvheL4aNm3y2W1K+ZAEWl98xJkLz+0nJAJMPf8yq3MPePx2SKZMv+cVttcW6Rs+iwx8FFVl/sFthFA5f/lFDDOBDHyuvvIhHNtCBj79A70M7htq+xRzW2iaihmPR56LvltuTXJVPdJ8z2X14WsQOJgxnWI+h1deAQLMhEmsqqZSVA3HsjCqpIVQBIG9jGPpEKGWiTKZ94WGFkFyyYh9NxMmTt6K9AV1/ObXBnsTlCwQ/sFMSGb0OYytBwAYpklqcIr85gJ4ZaSeItEzhrMzS7xnnNLO/MG2u0dxK3m8Sh7pRTcgnWJrKwe/kqVs9pHbaFbhBxHjncCRAxvlvXXiCZPkwCTzMw+xygdKwuGpFxGqhlXMHlmb15vQt6Oon5yc3A8C0jStI1X7k6iganjmmWd48803efHFF0+kqK/3Nj0cBHQY9bXfYVy4cGGfLGu3jltaWuo41frdVn/BaQ32NHBqPP+E6OTiUS6XuXPnDvfv3+fChQsnHsfpFFJKfN/H931SqRTFYhHLspr2XQhBj2YwZMSJKSrlwDsguKREyADX89izK6xZJTbtCgXPQRcC6fvs2WU2ygXu53Z4VMg2EFw15F0bq1gkVrYRMqAoJOuezYZVbjJWP4xdx6Jbb77IqUC/YdJvxBFCYcOusFIpUaojtVYqRRJHyMejUPJdzA7GD1UEju+RECpdqk6XqpNRNUxFRREKPuAicZFIER5vH4nS4Q0o6NBb8KiAgFbobOnGEhspQ3LK3QZrDmHNIewlhLcdei10QBwBoLRPcihae8orGbiYPaGUW/ou2cevsv7Gf2ZvfoYgSCG0cOwgcPOIFkXqYSR72lctJXoaZeRGZhhz6HnK6hAP3nnEW9/4C+be+jquVT7k1XI0theiO98QFlm1h5PjTOghJKrm5+dx3cai0HGcY4mrWlLPzExzBHwr1H+uEIJnnnmGBw8etLWvUYjFYpw5c4a1tbUTm6hC6FExNzfXdBygmeCCsIt4ksjq5557ji996UsAfPGLX2RpKSSCV1ZWOHPmwGh5fHyclZWVlq8/KcSBS/b7gX9NmNyTJoyqNgjl8THgBeC/ObTOKU7xHUOnD287Ozt885vfZHt7m+vXrzM1NYXndXg/ioCRnogkuCC87qL3IiPKbc+X2K4IG0UihsBFSAshK+F/QflIb0khbVCb74+qFg+3rWoNJEVy6OBhU4uZJPpHyAyfwylXKO9tUtpepbi9RnF7jURvD6qioekGdiFPfmsDw4zj1xUHhmmS6uqma3gM2xeULYdy2cI0w+utpiqYMR3T1InHDSRQLjsUyy5+9bre6mFfUQRxQ9AtNznTo6Cp4WHSVRgaGmR36T7l7AZLM3eYu3+rQbmV290kt7tB39AZLl37Prr7R3j01muUizmEonDx6g22VhfY3VxBSh9V01l8dJfzz76Ioghm336D+ze/yoPbX+edN76K77rsbq7wwb//oYYQAWnvEk8kWp6HlWKrAThwXYe9tVl2F75J32CavuE+kj0Z+kaHMeMayXSiIZETwCo3Ej5CEfjOFrOv/xdKe42KdddvvkcbZjxyX6TWXFcrQlC2o38bRsxgbavE5m6ZnVyFbMFGS/QzeOE68Z4zZMau0Hvuvdi7s/vrqPhUNh/Qf/46yeFnkG6R4upb2KVdfLdC39T76Dr7Aumx53GtEm5VHVjYeIARbx6bK++tghJdh8QHprCK0Q1H321liN666pUyoHviRWbevttAcAGsP3qDRKafwHf5y8/+r6w+fL3l59QU9fWTNK1QT1S1U3MdRi0demOjs0m2GlFnmiajo6PMzs4ev1IL1IKANjdb+5VBdEJ1DTWPr3YCkWpYXl7uOGHy3VJ/wWkN9jRxquR6QrRTZFmWxePHjykUCvtR1PXrt8PqnwS1C4Lv+wfeAprG9PQ0Dx8+5Nq1a5HrxVWNuKrRqxlsuRaW77NkFRsM4jUEUgbs2BUUBCXPYa0SPbcupSSj6viWzbZjkxOQ0WMYJ8iBWCjnOZNIU/E9uowYXiDZdiqsHuPX5UuJW3cc2sWOXSFtxIgpKoaiogqBKg50UQEy/OwgwEdSDnwyQuDW3TCP217Zsoh3ICnudGRR0vnIojwUR970vqymH+JD4IFvQ5XEOg5CeqDEIqXwUfDQ2r5QdfLME+sew9prvCk5+U227v4pAOnxqyQHz2L2TFDZPp6sMWLtEym6kcDsm8RXEmwuz7Fz+1bLZe0jOpaHsbVwjyDwUVqQs7UCq2YqfNR1p56oqu9OHdV1q0f9yGBf3/Fplof3J5lM0tfXx+Li4olII4Dh4WHm5uYawjY6hWEYTExMNB0HaPbjgrCL+JGPfCTysz74wQ+yvr7e9PqnP/1pPve5z/FzP/dzfOpTn+KjH/3oExFzT4jar+iDwDrwL6uS+YvAqpTSF0JsAzuEEdb165ziFN9RtKOmz2azzMzMYBgGzz///L6hs+d5HaeAdYKa72kQBAg1jfBz+95aIFFVF02VIF2k0CNvueFYohbeN6Mgmu+OQkR7MgRegUAKFNG4odTgeewIW4B4dy+VvS2MRBIjkQynAQwTSQCBv1/b6IZBvLsPWfWmLBZKSCSHdemGrhL4AQrg2B5eQMubtqYIXD+s19KmIK4LKq7E9kCWt4mrgLNLd0JQtquvV9c9c/45dDMOQrCzsYRdKdI7NI4ARiam8T2PwbHJcHRRQqmQZeLiVYLAbxphTGS6sSsltlYXGB4b3X+9XMhipkK/M61VmIzbui4tFbIk04JYV+Osoabr7K7t0TvYrBy27WbCM5ZK45UW2Hr412yo3QgBnlPCKuyRTfUy/szBpIiZTFG2mu0bYvEEeM0+Z5btk4o3kitBIHEti9Fz0wR154xX3MQrbpIeu0Zu6TZW0HgOxnsnUPUYTm6Fwnp9grKktPGQ9Nh72JtvFqRI36Pr7FW2Hr126I0ALT2Ml29M4BOqxurc26hadE3tOdH28uX8Nqn+sxS3m38HsVQv2ztZfDd63VgyQ3F3lbf+8j/Qf/ZZRqdbe0B1oqjv6elhfX2dzc3NEwWRTU1NtT0yWEN9iuPY2Bi3bt16oiCg6enpY1X9x/mmduLxJaUkl8vR29vb9N73SP0FpzXYU8MpyfVthOM4zM3NsbOzw/nz57l8+XIT4VGLsI7HozssJ0VNvVUjdepHE3t6elheXj5yBAdAVxRGq1HEI7E49wp7WL5H3rXJei4KsGOVybVIOTSFQlrR2CwWWQ6qEubq18+7NmeMGI702yKdDKHQpRuoCEqOjScEKy1ItVbYqY4tZlvM5AsgpenEhIJVKpNMp6j4HklFxRMCRwbVKupoUy/X9zry9nAVgdkB+fadGlncp0lkHZklHVy7gK4GDbWpRGuL4KpBMTIEVvS43mGkksl2pgDD/fDaPyf0ZGujUIDC8psUlt9ESZxh+/FjYpl+Yqke9HgKzYijajpCVRDSRwYOgVdBMVJ46JjJboRqEKAQ+BLXc3FtC7tSwirl0dw1Fu59va39LG4vIRR13zD1KHh2mezaY3rHplsuo2la25L5gYEB1tfXG4iqdqKsoXFksKur68jfRKuH1ImJicikoXYhhGhIizypcnZwcJDNzc0mwu6wHxeEJFerLuJf/MVfHLmdP/uz0GLh4cOHfPnLXwbCQrPWVYSwS1kzlG31+lPCWWCZsGsIcBGYr/6/AM5RTfU5xSm+WziK5CoUCszMzCCl5NKlS/tGwjXUfAqfNmrklqy7VwstE2YL+4Xoe+VRwVhCP0LR27yekB5G5hxOfr7hdUWAkRrFKx1SHPglhJ5Guo0Ej6Jq2LZLonrtFkKQ6ukl8H12l+eRvoeZTKFqGnosRqkQeh4lUwkcx8OuVBrqlFhMD8cXq58VTi4Kspas/vtA2a6pULIlqgKqItBUQUoB2w0aiDFNEaRNkBWJVFQMM8nG8kNcx8VMJKmUw5qgRl5VSgUMM4FtFRuUVoqqceHyi2R6Bhg6c2H/3rjw8E0Wt9e59vKLxKrXet9z0XUFRVHxndYkl99iWGZ19m26emK0aiTmiha9EcLwIOLzVE2jVHZIJWMoMgcSVA20dILK1iyPX91k9MoN4qkUqqqSL1hk0o33rO6eNPZWM8k1evEaztb9htdEYoT+1A5mIk0pLxpGTQGKK7eJdY1iZVeI902gx3uQvk1p/Z39ZXrGn2Nv+V7DeoXVt8gMXyS/3txQ9FoE+2T6x9g9RHKlR6+w9sZfo6gauqo0JV+7lejQgtzaDCOX3hdJcmk957Dd1vVXLXAg8D2ya0crnzodW5yamuL1119vu1HYsN8djAzWUPNGhQNF/b1793jxxRdPJMZo1SytRzvhQO16fNXIw6jno++x+gtOa7AnxinJ9YSI+iHVR1GfO3eO6enploRELWHxaZFc9Z3D2v5FbfvixYvcvn2bvr6+ti5ccVXjvd2NhNiWVebvtlcbSC4F6NViWK7Laql4ZOTDUinPVFcPW4dm7qWUpDSdlKojkeQdhz3bImcfdFFGE6mOiR6AHdtCUxQMVSGtGejV724HPiXPwwp8LHwwdeyqrDnrOiR1o+1tZT2HflVtX1akKijicKlwNAIkageklS8lWhuLCySCACXwwM+CtJu2YkRdNTrsIShqLKI0b4E2FV8ASA/N7MOzjjYBDfehvRu2Zhi4lTxuJU/xGOV3vqJitaG8iqXbJ+MC3yXRM0JpZ7mt5Tdn7xxJctWKLNd1jy2yhBBcunSpgahql+SC8Po2Pj7O7Ows09Ot96lmiB+1r7VI7OvXr5/IW8LzPMbGxpidneXixYsdrw+tCbsomf329nbbiUL12NzcZHBwkCAI+JVf+RU+8YlPAPDRj36Un/iJn+AXfuEXWF1dZWZmhpdffhkpJTMzM8zNzTE2NsbnP/95fu/3fu9E3+8Qaj/NR8A/BC4BNwlNT/+k+t55YICDAusEutxTnOLbg3K5zKNHj7Asi4sXL7Y0T37S8KDDqJFatRpMUZTG66uWBj/6IVvgV/23otQ/SpPJfP2agYhXm1EBCLU67uhEqrZUTY80DIilR7B2m8frEn1juMVd6n3VFVXFDQRdmQy+H+C7Lrqh0z16hp3VZSzbxpMKti8w69bTNZWAZp8UIQSe5+O4YfCNAFQhQUosNyQxAykxdYGiwGG7qdDfVsVxfdxKAQEYmiCdzmBVyg0kaLmYo1zMcena96MoAs9zsSslNpYeM1M1mR8YO8f8gzu49oG/1H/7T/4pEBIZrhUSZdXD3xKxRHMjbXdzDTPmEovF2d7aYWComc1KZqIJDSMRraZxvOZqStVUfMVAD0psv/MNRPd5Bicv0zV0EVlu9ETVdY0gNYBbbGw8mvEEB9W9wOy7yO6jsDlXXL1LrGsYNyJIR1VVMmPPUVy9h02zL1Zla4b08DMU1usINBkQS0X/TkvbcyhGgsBp/G349qFUUkVlbSE0BQ98j8TABKWdRkLXtYrEuwapRCQzlvPNryV6x7l/++vEk11oeixy3FGrs1Gxy61HVGuoH1us/bsVdF1nYGCAvb32E7zr0d/fz/r6+rGihhrqSS6gIQio5j/VKQYHB9nY2GB7e7thkqkG27aPbWLWCLtaIFIrrK+vf6/XX3Bagz01nM5wPiHqi6QgCJifn+fVV19F0zReeeUVxsbGjiykYrHYU5HL15Rbnuftm8q3YrMhVJANDw+zsNB8A2oXA2aCHx2f4kdGv6SNYgAAIABJREFUJ+nTTXpVg3LF5nFuj5Vysa1f3KPcHv1ajC7NIO0GdKOiSdiulJkv5lgo5tmLkBevlov0RfhztUJc1Rg2EyQ1jR7dIABynrNvTF/w3JZeV3nPwYgw32wFX0pEh5eb4/zIDsM9qusbgdBMvnkbIrSBR5MWepBDD/JoQREFC0QH2i/pRXqOtEJHzxbSRejtq3i0eHsdLxn5MBGxnN/ecgCZwTPHLwTYhW3MZPupNYkWRW8UVt45XiGmqiqKorSlYqgnqqB9JVcNIyMjlMtlstlsy2UOF1b1SKfTdHd3s7zcHsl3GL7vMz4+TrFYJJdrUxIYgVgsxtmzZ3n06NH+a4fHFWvqjZN0PH//93+f6elpnnnmGUZHR/n4xz8OhF4RP/7jP87ly5f5yEc+wmc+8xlUVUXTNH77t3+bD3/4wzz77LP8+I//+JEpkO1CHlwofpdQCv+vhBADwAhwWwihAv8GWARer65zMuO0U5ziCVFf49i2zdtvv82dO3cYGRnhpZdeOjYd7GmouWrkVv0ouKqqzfWXUEFp7RsppIek+ToopA1CQwq9aiUAUmhIDIRfBKESuHkCZ4/A3saybEoVD7RmUkR6ZZRY8zGR9g5asjnBrJjsY/PMB5peN+LJ6vdUULUDhayvmMRNg8H+NGNj/RQcwW7B2U8NVLSI63zVfkERoAkZElwc1AlChGoux5MEMtpUfeLCs02vWZUik5euMXHpGqp2cM+afs8rzL7zLRyrwsybr7I48xZ2neXFxtIs3X1DpLr7mLryMjd+8IcZOTMRJnaXtg8ILkDRYi3NzM1EgvzeQcPN93x01SFWtTYYGI9u/IxORDdjRiejr++xVPNoFkCqt+o76pYJtu6y/toXsLbXUCLqKSMVQT7kl4n1XSDWdxHXkfsE1/466eGmuk+LdwOSytYMRzGARqLZS076rZ+F4j3jTa+VdharQQ0h0mPPU9g+ILViie7Iz+oaik5tzq83q7DyroEMAsqFPXpGo/8u9UmWTqVIOb+DbMNzC9i/XhyFeDXYYHu7edS0HRzlK3oYUbXY+Pg4e3t7FArHE3hH7cPs7GzktbYdJReEhJ2qqkd6fJ00+OfdUn/BaQ32NCGO8TI4ZQaPgZQSy7JYXV1lYWGB4eFhJiYm2h5XW15exvf9jk3y6rffjnIrCkEQ8Nprr3Ht2rUTxczW41Ful38/c7dtU/SkqpPRdRzPZ9sqM5xOs9cq2rcFFCEYTWYoBc0XTQXoraYsFj2nQW0WU1T6YnG8Dk7vfsPE0Np/sM9oOnG9/ZluFUFCiSiKj0BK0Tpa3hACTQgU6SOki8A5lsQSXvukgJQ+wm/vJihRcEvtG2L6Usc9PGLRAkLvprR5p61lswtzLaXw+5+n6Gw8foiUbRQjI1dZePOrbW07MXiRzbnoCOzDGLx4g/m32kxY7B3l4799+9hzIwgCrHKBpb/4NxjpAQae/wfE+6I7dVJKbt++zfnz59ne3qa7u7sj+XylUuHu3bstwzb29vbY3t5uqbTyfZ+bN29y5cqVjlSvUkpef/11Xn755WP3od3Pe+uttxgfH6e3t5d33nmHsbGx/TGonZ0dfuqnfoq/+Zu/OdHnfxcRebIIIX4C+D+AMcJ6oPbEVgL+iZSyvZnbU5wUpzXYMfB9n0qlwtzcHNvb20xOTjI8PNz2vfH27dtMTU2RSqVOtP16awhoowaTHjhbiDpDeQlULB9d18PaMbCrxE/VTVN6IP19lZcUGkiv4UfroyPtxodgqaYJnCLSL0L1/vWVoskPdOnYuccNy+7JGD1GCjv7cP+1gojzlnmJnJLiB1f+qHF7vo+1u4U41NFzXQ9re7XB8L5ctsnnCuzlLRIxla3dUoPCy/Ml2UKl6bj5gSRnHYq2EWB0jWJmBthaeoDvVNAMk/GLLxBocYq5HL7vYZXzbK3MIaVk8soN5h68yZkLz+FYZRYf3a1+luDi1RssztzFKhfoGz5DT/8Iqqbj2hbxZJrtlfv88r/7dxhmAiu3jpFsJk581yWWir4fbmxkGRk/Q4CGnZsnqBsJVWIDSDdC+a3G8SLqI2H0Utm937y8yFBYuR2x9RSljUPLJ8YQTp6eqWeRXng+KcYAhfVVFFVBES6elUVLjJBfnUGNZSivRQfaKEYCs3sEazskh4yuUQh8nKq6KzV2jeLGDIHTnA6aGJxmd+lu49dQDRzXx49obHedfYGd+TuoRhzNSKKnB3Bsl0Ax2Jy9jQwCHLuErCOcRi+9zPZsdPCcGkvhWs31qhEzCbzwt5kZvcTdOwd+qReefx/rD76x/++esWm0WBrHsfGdCp5r4XsudrnAx/71f2TsUmtvLgh/Q67r7jcdW2Fubo5YLMby8jLXr1/vyA6lhvX1dbLZLM8888yRyy0uLmIYRpMaqlgs7ivqT+oh3Wofbt68ydWrV9v6Xq7rcuvWLa5duxbZGP3CF77A2toav/RLv3Siffwu4rQG+zbgdFzxCeF5Hq+++ip9fX289NJLHZvVxWIxdnfbN5au4UnIrRoUReHixYs8fPiQq1evHr9CC+zu7rL3+DHfZ6b5mp2PrMo1Ieg1TIQMfbxWyznqJ+ndrM9oVxf5VvG+EQikxPFcZPVrd+kxkpqGHfjs2BYbdrQCxw58DEXBa8PjqIZtx+JsC5l/FPKeS1zVoM2bgV/1zOrkiSbK2PUwNMJjrwqJKgNE0ImPWYc3MtH+uS8IsHwdU23v761EpP+0xBFpVIcR7ztL4RiSSwYuqcFzFDYeH7kcgNLBXzBqlKEVfKf9v1txd5Xd5Qf0nWldzNiFTbKzX8XOLoOsUNm4xdzC39J35UcZvPaPEWojoVsbW7x37x6pVKpjE9R4PM7w8DDz8/NcuNDcRT1KyQUHSUP379/n2rVr7fvXed7+vh63D+2gfnzz+vXrTZ5ci4uLJ25YvJsghIgB56SUvyeE+Dvgx4BnCC8KbwN/JqW8e9RnnOIU3wlks1nu3LnDxMQEN27c6PgBrOaL2inJdeIaTGigZcDdCVVZSgykRyIe7rfED1Pj/GLT3V0KA6TTYEIfOmYqVEoVDD2Bile9B0qEdHCtXXKOy7ecLh5WYMuVfK0IMAWAoUguqCW6NcmwL+jC5IE+zqrWS1xRsBUTRdG5efGn6SotYHkefeUlhouP6Z58L7n5xjQ5Xdewk0NQOVBbJBIxdnMVDDNBPK5yduACW3Nv7SuyNFUQKAbqoXu3qgiGz1xgY+lAPSsl9A+Ncf/Oa8TiKXrPPEd2fY5Hb36D0fNXmLvfTMj4TuiH+ehuo3m5qhkEvscz117h4VuvsbO+1JDSmEyn+NXP/F9IGWAX99Dj0ffsSn63Jck1OHoWu7SDV4lq6rUQX7RSNAXRxueH79c1RNVNge9gbz5GqCq9U1cobW2xe/8PSI5e3Td+Dz1AwwZcZvw90fsCBE4Z1Uijp4eIpYcort1t8JYrrtwmNXaN/FJEsI7QSIy/FwIPN7eEW9pB+g7xvimKa+80LqrqWLbP+nqzgmfwwnuxitE1nFB0FM0giGigp/vPsLvcbGek6XEcz0XRdHZzjc8Qbl2zXCgqtu2yNv8GAL2jF8jXqcg2Zt8k3TeKa5fpHZ2KvDbURhY9zzuy/nFdl56eHs6cOcPjx4+5dOlSy2VbYWhoiI2NDXZ3dyNN2WtwHCfyWphKpejr6zvSc/Sk+9DKriIKuq4zOTnJzMxMpHJqaWmJqampE+3fuwmnNdjTwSnJ9YTQdZ2XXnrpRMkXcFBgtYunQW7Vo6+vj+Xl5WMvfFHI5XI8fvwYXdd57rnnSCQSTOZ2+Pzjd7B9j96YiSlUiq7DWqnATqn1g3rF99guFkknE9jHkE+6EKS1GKaqoghBr26wF7jsuRZ7bfIbS+UCw2ayIQXxONi+35ECJJfP09UdLZeOgtehz5YnJUbd4oLQLF4TAk2EIvLw1Kh+RyGO88w/hKBKo7V5jDo8DxPJXgKrPTWX6GDHpVdCaIn9LuVRiGUGaEd7lugdbovk8ivtE9aifVcyittLxy9Uh7mv/g57mThjN34aPTNE7vFXyS++TmV3Dt/KtiQCd+7+EeX1e5z90P+OajSONNS8GVZXV0+Udjg+Ps7NmzcpFAqk042jCseRXADd3d0kk0nW1tYYHR09ctkaDsvgx8fHuXXrFvl8vsmEul3UxjcfP37cFO29tLTUECv9vQYhhKhK5S8AvymE+B+llDPAb1bfj0kZGgMJIZRTifwpvtvIZDLcuHHjxOrMTi0jnkoNpiSQqgWB3WRCL6p5yFJNIn2r8d6nxAGzwderSJwdpYuz6TIKTjg+JjSkVyRw8nzN7uO/bBRD8qwKTZMoItx3OxC8HaTAhbSvoSXft79cDkIeJvCRqk8hHl7bttOTLPVdp9veZTi9jlZovD/1jk2y96iRkBgY7KW0u4XZPYSR6sWUF6h4GluLodLo2Wuv8PDWV5oOVVfvYAPJBZDdCG027EqRzfkDA/PV2bsMjE6wtdpow7E69w7nn32BhZm38D0PRVGYvnqDhYd3mL33OvFUhnRXP+VC40j9//LL/4rAdyntZUl19yFaEKiKGT0Sa7sCP/cQ0aI9GngliPSWDcJwHqfRv016ZRQ9Q+A2vt5qv0TE9IEuPGygtPaQ0tqBaq/e76o+5MY9pgkYSDDSQxRXo1XpXvlQTSQUzKErrD74BunRy6zNvAFCIT04gecLtLJFcuQKpfV7IGVI1CVGWbr7VXQzhWs1qsI8p3Wdl9uYJ9k7RmFzruk9tcWkhaLHUL0YMj3B6oNGdVwxd6CUHJ5+mbm3DlRdhd0wtS+e7qVS2KVSyvL6H/1buocneeP/e5uR6Zd5/kMfb9qepmn7aYuqquK7FqreOFlTs4jo6upiY2ODbDZLdwfPFnDQoHvzzTd58cUXW14vj6rFJiYmeOONN+jv7z9xEFBtH2qKtJN4K9eSu6N8xpaWlvihH/qhjvft3YLTGuzp4tST6yngSaJG2y2wOvXc6gTT09PMzMzsF23HoVQqcefOnX0j5+eff55EIvQouNTVx88+c41xM8l8do/7e9ssF/NteU7lHJvA8RASNCnpM0xGzSTjZoqRWJIeLYYmBSXHZb1cZL6QYzaf5Z297WYn0mPgS4na4bHbsMt0VEbHzWOjzetRCby2lpdSQiCRSDQgLiClQFqBpAoxRaKKVpxTh2Ss0sEYq/Q7UqJFenO0gl8JE6bahB7hLRK9D21+ntneDd3KraG26RXnlNo3ErVLWXrH2utOjY70oeTvUt68y9yf/2/c/+I/Z+Xrn6G48hp+ZetYpVtl+yFLf/mrBF7zdWl8fBzbtnGczkaL4SCp58GDB03XmnZILoDz58+zvLzc9kPpYZKrVmQ9fPiw7etdFEZGRqhUKvh+Yzrsk3Q53yWofZlngWtAFsJiCqCuuBKnxdUp3g3QNO3EBBd8l2owIUDvAyX6mich9BrSkkg1ia92sakMsUWCPWniogGCitqLpSQoEyOLjo8KQuF+Pk9Janxm2eePN5r9Ucu2jR/x861EGJjXsGdXGoZny1qK1eRZFi7/I+xUY6qYU1glOdZoDh1TA+Jdw1jZFfLLd/AqO+jeNueevYZmmOzMvs749PWm7Tr5dQbGGsfoS9lNBsbORe7n4NAoutF4D/Zcm5WZ21x87r2ku/sYn3yGh3e+jl1N6K4U89iVIopycB799z/7kwwND2JoIAOv4b3DSCRjHJ42CkQMe+8hgZML1XpR8C0UPdpMXjWilYWxdESDp8UpKNTmAse385EruIfJqCqs/DqtylKz/zz5pdvszd8kMdisLtLi3ShGkvTEDczh54kNPoeMD++P/NmFKhEqAwobc1S2Zymsvs326hKu2kN69AqBOcT645Bs6ho817x/hdaNxeLuCka82fsLQLQo/oxEF15inPkHzeOfe+sL6GaSZPcQy4fed60SY8/c2PftUhV456//A045z/qjN5Behb/4rZ8it/6IwLN58Ce/yZt/8D8z+zf/N3szf8v6rT8it3iLR1/+5abt1kiuWv0yMzPTlpfXYZimuR/C0wpH1WK1IKAHDx509Gxz1D64rnui8ctWPmPLy8snasK+i3Bagz1FnCq5ngKOirA+DrWUs1aIjKJ+yolA8XicgYGBYx/QLMvi8ePHlMtlpqamWhq6DidSfOLKe/m7tSX+4+w7uIceJqWUmKpGUtMxVQ0VEY4e+j65UoVu06QgfMpe8xx/FNwgIPB8pNJZR2CpXGA8mcbu4GHXDfwji516WIFPaMHZHmqpQwoCVQgUEf6/Qvj/gvBvL6Tc79zFODBpbQuKDkH743woBgSV45cj7EBLJQVBe3830cFYIYSG8l55va1lXb89/j5wsggtjvSO/o6tOrFNkAFdQ+fYXX5w7KLFnSWEoiEjPOWikO4dYnflUcv3EwmT0bOjDI+PkEin938LKlBxdaRrobXxMGhkhkFVWH/9s4z8Vz+LqDvfFUXBMAwePXp0orTDZDJJX18fi4uLDYVIuySXpmlMTU3tJ+wct33btps+N5lMMjAwwMLCApOTkx3tfw1CCC5cuMDNmzf3O7DAvmfG9yrqiqa7wF8DLwNfrr1eLbRE9f8DedIb3ylO8ZTwpPWQaZpHBlI8bfV8A7QepG+BtKGm4JKysUElDCyp4QsDT+ps+S5ryhgC0Kq/PglskCKvdJMUklv5LL+/tEfWih5vC6QkCGRTm9sLAhTXIYhQuUjC8cHDDcsiBtsv/QuMwiq9K6+jZYaxjCSqXYSVxtHBrpEL2KW9gwaK9HGyi8TMOIqqokbUGrmNOYYvvcLWSuODef/QKI5VIbfTqAafv/cq01deYeHRfYr5AwIknsqQSCYRApZnm8fU8rubTF5+L4/vfYtnnpvm5ff/QHhMrCLp3gGcSg4z3SqdTiKVGKI6ThgQo7x5D0VUzxnVRLZMiY4+l6RvocZHkIELMkBKF+kWoptUMrqGqD4XH/5gjMwgTr7xuHnl3cjRPuk5xIemsHYa1VBaog8rt7mv+ipuLxFGFUkSQ89Syu2Q3V6E7XWMVB/F3WbVvlPYRmgx5KGGmhJLsLdyn721xm3Gks2EYDm7Tv+5q2zPRyvJFD26UatU79nd48+xtpXDiMVJplIU0Vl6FO2rKqWkZ/QigVTY2Wr+Pktvv0rX4FnOXH6F+1/5IgCl7QVUr8DNP/w0ALq/h19YRBGgKirF1bvVZMkuxLUfwy8sY+0tUtl6RM90qEiqJ4Ke1HZhdHT0SDX7ccFCmUyGTCbDysoK4+PNYQDt7sPt27fJZrOoqnoiT2jDMJiYmGBmZobLly/vv76xsdG20v/diNMa7OnilOR6CniSgqfVuvVR1DVy66Rmf+1gYmKC119/neHh4aaUC8dxmJ+fZ29vj/Pnz9Pf33/sd1aE4P2jZ5nq6uE/zT5gp1LB9jxKrkPWtsj70cUXwGqpwLO9/RSVoO1ju1IqMN3Tx67fvsJEQoOaQxUCQygYioquKKgIlGqR4HoulmPj+AHDscS+Csx1HCqWha7rGLFY+JlS4iPxZYDn+xiajiqqxBUhWVXzZA1LAlFlwiQxVSXRwl9hH3XHxEegdqKfiip6jkSHyyux9kkufNBS0CaZqWqJtj3RFBG0OeAYkBqZprB0tFF9J2OIya4+dtsJAgx8uocn2VudaetzfauAEDR0VRNJk7GzYwyMDJLq6mr5e4knk6jxc1Q2m7dlZEbQkn1I38ctbeBVdvEqu1jbD1F0k+H3/g8Ny2uatp92eJLRvImJCW7evMnAwMC+5L1dkgugt7eXjY0NNjY2jo2KbhVNfebMGW7dusXAwMCJDachJMxqilYISa7vZSVXnfx9gFAu/3NCiDlgTkpZOe0cnuLdiCdpNLZScn1bya0aqmotPL9KdNFEcCFUrGrTRkWgIvCRSKCe7lCEQAVKUjBfsZGKQkzTsFskR7a6s+/YLn2aQRDxVaMU+TbheJubGWMzM4YufVTVQBUKXef/Hix8E+/NL2DtzuNmZ+k79x62HtV7Y0mGzj/P4t2vYe0tMnb2HCI1SnZjgcBzyQycQdM0BieeZXdtHs8JibC1mZsMX3ihgeQaPDtNItXN8oOb6GaG3sFRegZGsa0ya/P3eef1v+LC1ffx9s3oEBfHqtA/dp6f/Z8+iaIoBJ6LFgunFBQ9hgz8hqbP4SMqhYZrlXDyjU0ux/VaaugDr9gwsij0DFKCb+0gNAW3vIZQY0ivjBofCgMEFA3qmmPSi7YCaXW66qn+JpILIJYZprK72PS679dcY6u/BS2G0Ey8/MFIqmfl6b1wg3J+h83ZRg8up7iDbmZwrcYxS4BM/xly643NO7/F2RmlLgf2/0aRqB1XoZA6+yIgEPYuCJX02Ze4+frXmlbp6spgl5v3FaDsKpFm9kJROHPpvVTym6zd/wbDk1cwE0nyG7O4lTzpoSl0Zx1ZXNyv/42uETzXRo9n0FSVvXf+GDe/Su7xV8gvvLZPcgENz39HWT8ch5qi/t69e7z44otNz5XtjA9OTk7yxhtv0NfX11EQ0OF9uHv3LhMTE20lK0ZhcHCQzc1NdnZ26Ovr279mP4my97uN0xrs6eKU5HoXQFGUfTVArVCrpfV8u8mtGlRV5cKFC8zMzHDlyhUgNANcXFxkc3OTiYkJLl682HGRN5JM88+uvMhfLc7x+QdvYbcpsX1nd5tnunooG+1vbzGfoy+VxD7iGhBXVFKaQax6EXSDgD7DpCx9fBm6VlQCn0qUL5iu4fgutl9nkmjomEZYvhysERabKiou0H1EaqI8WAUQ2EFAvANFmhsE6EqndliifsvHIGgobo5dWsqORjpVPY3fJsklRPvX9pjmUURH4Xi1WKxrgMIxlle+UyQ1cI7i1vyxn6dp7R+BdM9Q2yRXUi/y9z78fgqFIvlskf7hQZKZdNvnil/ZIn3mRezsMnpqAKTEKa7jVXbwKjuR62Qf/imxzBg90x8G2L8+nTt3jps3b9Lf399xkaMoCtPT0zx48IAXXngBIcSx3cPDmJqa4tatW/T29h5JjjmOE+k1WC+7f+GFF050jbUsi56eHvL5PLlcjq6uLlZWVk4UX/0uQu1k+meEJqcpwo7iPSHEOrANLANl4HNSyoXITznFKb6DeJok13dCPd8ENQ2BBtVgGF9qOI5HPK5gSfZzkIUQGIpCJfARhP6kqlAIpITqfu6Ui1SqxJZ+BMl1FDRFwYmooyzfw4wYgSt4Hl2qhgQcoULgAz6mqqGd/z7UwUuYj7+Cff+PMXqGmtaXlU0yAxPktxbwSltQ2cUQBnv5Hcq5kEjpGppEG5vEsiqkugfD76soDIxfJNMzwPbaApuLBx5TPb0mFdtj7u1vNWxr9u6r9AyMsLe11vC6EAJNN/jnH/+HmImw8eFaRcxMPwCKouKUc8RS0d61vmdjZ+eQfrMazaqU0FvdJgMHofcghIrvFZDlA+JIugX05CgIFbcwj1/ZAARm+ize/8/emwdJmt/lnZ/fe+Z9VNZ9dN1dfU33dM9Mz4yQhAVCYmFXHEuYCVa2sNgQNlix4QjHEovliF0LGxNhge2FwMQuwbkgjhWIw6AQsjmEGHX3dPfMdE/fZx1dd1Xe+Z6//SMrqyoz36zOrKnRjLz1RGR015vvnZnv+32f3/N9nsoqKCE8awPp2+jRbpxifbqmH7AvAHqoRStkLBNIcpVXHxDtmcDaeIRqxlDDGUqr9co6RQtR2FiisBSsNjcT3YEkVyjeRbZBoB+NxgiqCsvZZuN52Fto4DkWmhnBj43y5sUdhdboyfdx783gYLp0/wSL94PSKmFpeRnTjOA2BFsNTz/L4p0qeTt89ByFJzfwXRurlKXryCm8SpZ4vP5LUMkvE+0axowmqazcItx/EkWPkJu9iGJEWPjaL9F//ocDj3dmZmbfaYeRSITe3t4mNXu7/li1IKBbt25x5syZfV0fa4q0hYWFJl+tdiGE4OjRo7z++uskk0ny+TypVOqdv16/sziswQ4Qh55cB4C3+4OqFVm14sp13W9ccbUL3d3dOI7D+vo6jx8/5uLFi+i6zvnz5xkYGNj3vihC8OHRCX7qW76dqVSaqKZjqiraUwrTW9kNjEr7LW0Vz0XbYppiqk6fGWE4HGM4HKPHjBBWNWwpWXcsnlRKPKmUWLUrZB27Lc+wGhbyWWSbLY4+VZqoXUjA7YCoF4rSgX15baEOR02UPUbJGlctPXzZ/vckyDOiJbxydeR7e2EdoUURegqhp0FLghrDk9UWYJcElbKK0DKoof7qy8wg1PpCo83uU+J97bW2eaVgwigIvtta0bgbPf29DE9OoMXS9E/OMHH6NIZptv2b1KO9RPtOgl9CiyYpr96mvHYHz3q67f7y5d+gslktfF3XRVXVuiJnPw+XuyXvUC2uOinUagk7t2/f3nO+Rk+u3YjFYnR1dTE725mp/+51h8Nhjh07xhtvvEGhUMC27X1J798rkFLWuPr/G/gXwP8O/F/AQyACPAN8L/AZoBeq3hDf6P08xCEOCpqmbQ8qep6H53nb6q2D8j19KoSomsoLHZQwqqoRDlevIxVZbQKLCsioCv2KQlRR0YWCEFv3/6199JA8zG8yHEvwTHcfLw8dQWtxXS3adusHgBb1TdGx66XEW8i7duB9oLKVlu3HuhGnv4/Y9/0HxPl/hDDrSRYrt0T30C4FrO8RNhR6R4+TGToKVNsWQ6qLhs3awj0ev/Uqj659je6eHmbvXCG7K9kOYGNplkx3b9M+Sd9nYLj+Xh6KxHnmuRf5Bz/83zEwPEC5mMcp7xBc22jlraWYlDeCCS6AkNa6ShNaFOnbeF4F6TRSOxLpZBvCfySetYoQIISHEuoHFMJdzQpir7JZXzNtbzN4YEjsYX2hhVNokQzo0SaCCyDce5TC0h0Sw88ELA2WF/w7UgP8mESL81jaeIIZbyY9xyILAAAgAElEQVQZpee09Hi1K2XypLl/o15dVim2blFupQzrnzrHxtJjwul6D7r+ydMs3auquxRVw8kv4W21fZayK5jhKAPDg3XXkljPJLFYDFlapLJyCzN9BGllkV4Z37UpzF1i5fXfY/WtPw28Bu1OO9wPRkZGWFtbo1DY+c51MtiYTqcJh8M8efLk6TO3wPDwMMVicV/+YjWYpsmRI0d47bXXePz48b5bKN8rOKzBDhaHSq4DwEGQXKVSafvi8o0mt3YjlUpx5coVxsbGeOGFF/ZlCNgK/dEYP3n+g/zm9av88b0dObcqBJqioNXaBBUFTSioimC1VGJARDFiUSQSKbecK2TVeL32f6h6zy8W8xxJpsj5HmW7vQvnklViIpYMVm8FwFOVDjIQoex5xLT2H+Ad6aN3oIcqWw6xUAeG8kJtX8gFW9L4NmdVBK4XQaF1kmbdrjT5SwgQ2tar2txZHVH3qt4PWgqnuIBn5+rk+kEImxFyKwsUC829g0I10CIZtFAcKRXC3TNU1u9X/S9aQDPa+y1UNucJxbqoFJ7e4phfuo+i6vhe83ZjySTDE2Ok+/uJxOu9EzRdJ9rVTTFfxFCDPxw1lCKUHMSz8jjFJSrrm1vT0yhaqG2CTfoOT772fzL60X+N4+xEXadSKcLhMIuLiwwMtGf0vxvj4+NcvnyZTCY4ev1p2Cthp4YgT67dqLVO7ictqFKp0NXVRTgc5rXXXuN3fud3CIVC3+yjiDVck1LWGZMIIXSgi2ph1U91dJFDT4hDvNt4O7+52tfX3VI8faPU800QAkdGKBc2ScTD+L6kUKygR6IkNB0hNIQQ6KqBYhUDaxBXSpKRCNFd6tp0OMJKMVgtbagaFa/5Pur6EtTmLUhAkx6uaL4XBqlACp5LRPpV2wchcI0ouhlj5n/5z8jVB9z74r/GL2+SHDlFceF63bKuVSTaNc7szYsMH3sBp1xkfe4Gqm4SMtP0nHiBUiHP4t3LjB87y62rf0cjFh/eINN/BDMUwQhH0HUDoShIKZk48RyVcgldU3nx/DTPf8t5zFAUzYiih4I/fz2SBhotMRQqxTV8ewPVSIDfbJmhyDKeH0NV6i+VQk9VFVlbnlpqqBffWtt6LwEIkG7Vt61xnWY3dmEBofoILYFQm/dZ+g7R3imKSw0eoTK41rXzrdOuXauE70vs3ELTe7GBE6w/am7h241Esou1teaE6qC2yeLaY4RQCOrMygzNsHCz/rNevvca6ZGTrD261jR/SYZZCrCjWH9yn6GpM8zfbX6vVUfY2nq1pvPVnYEsRVVxSzupnKPHz7N67+s7f8+cIZ5KUH6ysx1FM9E1H7tc/VxDXaO4xdXt+lPRdsjU4uJb6H3B3lu1tMOenp7t8K92sVvNXvNX7cQ2Atj2Jc1kMvtqORRCEI/HefLkCcPDw/u+7vb19fFP/sk/4cUXX/ymtotowGENdgA4VHIdAPZbYNVGDuPxOEtLSweamNjpfiwvL3PhwgUcx2F4eBjDMA6U4KpBV1X+0enn+MfPnt+e5kmJ5Xnbfl1r5RJLpQILhTzzxQK319ZYLxVZLpdYqZRYrZRYs8qsWxU2rAqbtsWmbZFzLPKOzaPsZmcpiMCmbXWkSNmotI4tbkShzdTEGqytVom25++0HaHj75fsiBMrVVrvj0QghYEUIXxMPB88ItiVMpXCOpXcApXsYyqb96ls3KWycQdr8y529gFO/jHCr+BV1p9KcEE1bjvcdyL4Pc/GyT+hvHKb4sKb6NE4diGHkRjFTI0GniO32LoAbEQo3R7po+vw/Ld+gK7eHhCQ7k5z9PQpXv7Oj/DiRz/C0PTRJoKrBqEoJDK9aJGdkU1VjxLpPU64axy8EpX1uzgN++1VNogNHG/7WLRwF4oeZe2tP2oa6ZucnGR2drbttMPdUFV120R+v8VNq4SdGp6mENvdOtlpnVCpVLYLu09/+tNcvXr1m9rwtAYhRAj4QyHEtBBC25Xq40gpl6SUbwKvSynbS6Q4xCHeYbzdGiwajbK2tvauDTB6nsfDhw+5dOk1iiUHHx2hxfGkzr2b91AUvW6/4i18Ox3fx224jvXv5dnT4ppX3iMMyW1BkLgBAzUAXoMqzJESR9UwBo9z8h//BjM//J/o+cCPcPTTf8T7/9c/Ita90+6t4jAweYalO5dYn7tRXZ9j0T8wwNyti6zP3wTforB8l76+bqZOv0wolgIg1TPI+PGzmIpNubDG3O3LPLj+Kvff/BoPrv0duaX7bC4+ZHSki5f+3vswQlEUzcC1W9d2vtOcTOh6sppaDCjaHgMlDUnVwujCLS/WmcZL6YIaRQoTK3sfK3sPK/cIz3NQI0MooR4UI4NUIlQ27+C7RXy3QGnpJl55tWnfAPR48yCS7wQfo1NYwYg3q9/CmXHyK4/AiDd5kumRNPnlnY4pp9xCIdXCHL+yuUA4Wb9Nzy6T2kopbESgmT6gm83nvmvyJW6//ipqgMrLsco45WAle5CyaGD6eZbnqkb4j26/TnSrhjpy/Dy5lcdb+xAhv7RjPzH2zPupZJ/gbDxAM+MYsaoyMDN+dptQVDSjGtrUMMAa6jmKYsawck9aWmC8nfoFIB6Pk06nt9VgnZJcmqYxOTn5ttIWPc+jp6eHhw8f7mt5qF7/f+7nfo7f+q3feqpP6zcDDmuwg8MhyfUuoDGKur+/n3w+T7HYnvrlILG+vs6lS5dYXV3lzJkzHD16lKmpKebm5rDt9k3cO8VHxqf4romjbc2b81w21zfbvogWHBvN7eyCu25XiHbQOrfpOogOLupehy2LnbRPhiIR3E7lvp22LIr2W7BCIRPHE0hhbhFZGq7rYVcKOMVlnMI8TmEWtziHX15EVRV8exNapg/tQDo59Ia48r2gay6yVYvBLrjFecKZI+RnL5N7dBnP9jDTkxiJnW35TonEQHvf2XhAak3TPOkUx8+/QCgW5vgLL/Dyd36Usx/6dkaOHSeSCI4Vb4TvljGT/YQyRwl3T4PwsDbuYefn91zO2nxQnb8F9NgAoe5j6LF+3PI65ZUbrF37fymv3asjuTRNY2Ji4qltg62QTqfRNK0uAKITGIbBkSNHuHu32QfE99sLrqi1Ts7NtZMWsINKpbLdmqiqKj/6oz/Kq6++ui/C7z2GfuADQElK6e42OhVVfDvwu+/a3h3iEG8Tu2swKSWTk5Pcu3dv3w9q+4Xv+8zNzXHhQtXL5/z58wwMDqGoVUVoOp1GSsnaWn0LfKxFreIEKFAG48ntoJxGVFqQWSXXaXo4iKgaPaEIcSNMfyhKrIE4KLcwBS8HKMUs38f3Jb4QaF3DmP3TKF4FPdmNLyHWM073xPPkFu9gBvB5Kw+ucuzMizv7m13FKW2yMnsLz8qR7j9CaXOJh299nfz6Ev2Do/QMT2GGIjuEgVfh2efP893f92EURd8hQqTED9jn6nseqFuKGaHi+RpWdlfrXkBr4Dbs9W0rB2F04ZaaFVHSrSA9H6dYf/92S4t41iaVjdtUsnfr3pdeBaHoOMUsoWRXU7iQZzUryu3cQmB7I0A0U9/uFek9SmFtFtfKU1i6TSg9ghbpIjZ0Bj11hFJ+A6eyQ2yVNxYIItsq67Mt7SkiqWZiotXgXmP6Yw2iYTAr0TfJm5dfBWBo5vnAZVq1LLpO/TaEorC8XO8H9mT+IdGuQVZ2pTqOzJzF2lLwR7sGqKw/QDdjmF0ThLpG0RPDxPqmqKzuEGGxgVPY+XpTMq+Sxatk0aO9uKUNxMJf4pQ2Avc1mUwSi8VYWGj+PrWDsbExlpaWKJVKHZNcAJlMBk3Tms5Pu3Ach9HRUTY2NupaJzvF2NgYJ0+e5Mtf/vK+1/EewmENdkA4JLkOAO2O/DWSWzXlVo2Nv3379jesyMrlcly+fJm5uTlOnDjBiRMn6h7YJiYmAh8cDxL/6JlzfGB4jBPdvUyk0nSFWhtYL9oW0Q6Iq7vZdWJ7FRxB2ygXO2rj22jRAhCEgteZmiuoWG0FIQSFcnutZzsLdajSU0x8KShXXEoVB89XkGhIlK2WUQ/pVZBuAUPkqBQ2cApzuMU5vPITfGu96qkVBK+Aaqbb3hXNaL+tTHplEkPH8MXTb9yRngG0SHUU2C1tkHt4gfzsG4BBKDONHu0h1tNemqBwmg1WzXCIvuFBJk8e4/j555g48yxGPE0k00e8t59k/xCuHext0gjFiBHqmsJMjeKVVzAiMezsI+joe+Mi1Np5EZjpUSK9x9HCXTiFJ1RWb+IUdhVf0id37TfRGtoiuru7URSFlZWVtre9GwMDA1QqlX2T6n19fdtegrvhOE7bBdv4+DiLi4uUy+0PjDWm+Hiex/nz5/mpn/qpttfxXoEQIiyEOC+EOAN8J1Vz07AQIiKEMGqeD1uy+BngxNZy37wxRof4bwZvtwYLh8P09fXx+HGz6fY7ASklS0tLXLhwgUqlwvPPP8/Y2FhgKtjRo0e5e/du3UCAJhQiAWaSbsBggaoo9Mfi6IpKRNdJmCF6Y3GOpNL0xOJMpjNMZ7qZznQz2dVNT7hK4iS3IuBGQibHk2lG4im6QhEMPURC0+iPxJiMpRgKxxBA0ZeBXl4lz20ijSRQaVCEST2MEsvw7f/iT3nhh34KLRQjnBpAN6NEkz0oiko4kSE9NM3g0fPoqsqpFz/CyNFzqJqO9D1GJo/RNzRJfuUx/pb9RLJnGM+1CcsiwwO9jAz08fyzpzk+OcJL5yfRQ3FUfVf7mabjea3vwRIFqUQpZZ9Q2awPjfHdvRT+PoqRIF9RAwmu2rp9P7iW8+1NUIIVfFokjWdX8FwXPRxDDSW2yS7fLqAYza1sRgCxBOAUqub1ZmqQ2PCzZBdv4zk7+6SEM5Ry66w9uERx5T6yUVXvO0R6JprXW94k0R88SGiEmms6uxhs99BKaVfaeIJmRtDMCEYkyfJm9XwArC48CFzGLgfX8K69Q9iakTj90y+y+qTe39suF1ESg1hamlA0RSiSZHP2Gnq4+n3qGztJpHsSK7fE2uO3cFyPcm6DSKqfUGYKgOjASYqLzS2W1uYsTn4JRQ/hlTfwFv6OSkCrZw0TExPMz8/va4BttxrsafYOrTA9Pc2jR4/2XcOpqrptpL/fAU+oerVms1n+6q/+at/reLdwWIO9Mzj05Dog7JXu004UdTKZxDRNVlZW6O1tlgsfFIrFIvfu3cN1Xaampki0UJz09vYyNze3nRr2TkBVFE739vGfrl7cnhbWdbrDEaK6gaYoOJ5HzrJYLRe5tbbK1ED/numJu7FSKBCOhNpuzyt6LqFCASMenDzTiA3PIeVLtvOA90DF90i2aDFoNX9oj1TGRhjhzvrxW58TwXb6ovS2/CBsPNdBJUtY21rOL+7p0xWKdSMr7RtS6pFuPCt4pKoR0t7ETIxj5YILlzooOoqqERs5R3lzhXC4quoS1Do1ar/Z6r99Zz6CU6lQWLxDZfUhvlPGyj7BylaPJdQ9TXTgGYRSNf21rQpWpYSuShTfwinniKaSdB0Zxa4UCEUMookURiiM0BQU3UCPJFBbmHtGurpxPQNZaTav18IZtEgXvlPELS5hZ3eO387Pokd7cYrtj6a55Q3iI8/hFDewsrNYG08PafEL88jVSzA6Vjd9enqaq1evkkqlOkpJhCo5lMlkuH379nayayeoJQ29/vrrnDt3brvNei/T+UbUjPRv3rzJs88++9TfXZBKbHZ2lo9//OP8x//4H7ly5Qpnz57t+FjeRYwCPwN8K7AOGMAvArPAArAghFgAUsAPAzWviP8mDMgO8c2Np/1e26nBjhw5wsWLFxkYGNh3pP3TIKVkfX2de/fukUgkOHv27FO3FQ6H6enpYXZ2ts5vJqSolBp8RFuF1sz09hEtBrdmqeqORYYCdMVi9IUNdAEDZgRP0Ztu9YpfwlfiCEUhrBhMqQlWKkU0fNyAsfOgeBzbdQgH1DglVadn6mV6ps5TXHvM61/41/SPn2TpwTVKuVWs/Bo1/U2idxxNjxCLhOgafhaEJJ3pIrsWI9k3hlfOUlqfI5etKp/i6V6SiShrs9c5+vz7GTl+Gs1oVqkb0R6ks9k0HcCx8jgtag/PzqHu0REgfUFEC+7YUIw0du5RlZwSSsCAlUQzu3DLzbYJWjiNU1hGulVlnqrpeCKGEe/CKSxgxHqprD+s316LelQxE5jd0+Sf3ICVHdJXSknyyHOs3av6YYWS/dj54HrDCKcIoqL0ADILmlVYAJVc8KBZcW2uzsdU0QxSR84wO7fAxqqDa9cGqnZqqMLGEooQTe25rmOhKOo2IVrD4qObTJ5+P67rcO/mVRavNKcwxlI9zN++gu85nDr3ErF4ko37XyfSM0Wqu5/c8l26+kZBetilLCv3LtM/8xLFtQW0cAxFDyOf4osqFA18B7+01KT22g1VVbfbBp955pmOW66TySTRaJSNjY19pUPrus7Y2Bh37tzh5MmTbS/nujtJ9buN9PfrqzU/P8+v//qv80M/9EN89atf7din7F3GYQ32DuCQ5DogBJFcnUZRT01NceXKFTKZTOCI3ttBpVLh/v37FItFJicn6eoKjkGuofbgeOPGDZ5//vl3zKfi/UOj/Mb116upPYDteSwUgouxlBnCyxcZHxio3rC2IJF4vsSVPq7vY/suluuxbpU5Ho2SxyekqJhqNZFIV9RqMg0CX0osx6Zs20hVpWQIMqqOolSjuVUhUBBb8+/cI30kFcsin8vSlU7jtKG+KfsekTZbIqspixK93fOuqLi+2zJJKRgaSKfqlSBdkHbg1VIIUHQFSRe47RFRuupS9A10pc2RHa+AGurBq7SnBhKyghru2fbCQKioZhJFi1Y9I6SH75aQbhG8Ajog4zG84nJrRdkWFCA5OEh67DQoGkIouFYJOzuHZ2UJJbqwSvnqb0JEEcSqv3+q/k/RTC/S9xh/9hxWMY9q6OiROEqbv2lNtdH7TlBauoEeH0A1oniVDbzKBrYdXHgjPfRooi2SSzWrKUlWdp7y8nWkVPHt9lul7dm/Qp77/jpvjN1tg8ePt+/3BVUfiFQqxcbGxp4m8nvBNE2Ghoa4f/8+R49WR4s7IbmgaqQfjUZ58uTJU721gtY9NzfHK6+8wi/90i/xyU9+kr/5m7/pmPB7F/EA+AmgG/h3wDDV+uD9VE1Oo7BtdXgN+IWt/+9/2PUQh3iH0Q65VUNNwX7nzp19ke1PQzab5e7du5imyalTpzp6ABsdHeXixYv09/dvX3fMAG+iICUXQFIPMUeLJF1J02OSoxo4QFxRCTJC8BsWkIpGJhwnikdFSkoNCcuO76Kr9QoRX1GRno3QGq/RAse3MbQQ0cw43/LJf8/ijb/kzS//P9y/8l/r5swtPyCS6sOMJCltLFLaXMF1LbqiCaJRk/W1VcZOvIAQPkNJj4ePloiZLs/+99/L9IsfQNWD7w++E2zsjxqnsnodPdpfVVY1QroILVmtOxqgmN3Y2dvo8VGk01BHCQ2ntLq1Dh/FSFXV743r0IK7HZSG+4zvOoRSg1Q217ELBcz0GLpj4+R3K8jqa9ZwzzTFQp7Vh1eJ9h3FiGawi1WiSAslCXeNsPno0s4uh7ugBclll1qE7rQaoA6Y7jkVjEgSu1TfUuh7Dl3T7yef28SqVHiy8Jg7r/4tAF29R8iuNKcN+r5PItNLYaN5f41IjEqhfhvpvlHm5xfYWGytnuo/Ms3Da1Xy697tG8R1h/6xYyw8uI4sLuFYRUK76l8jmqa8cgvPLqOaUVK9R6pBBXtBSnxUjNQYTqm5O2A3MpnMdhDPfoQSExMTfO1rzWReu+jt7WVpaYnV1VW6u7ufvgDNddTbCQKSUlKpVJiZmeFTn/oUn/nMZ/jZn/3ZjtbxLuOwBnsHcEhyHRB2F041UqtGcLWb1mOaJgMDAzx69IiJiWa5737gOA4PHz5kfX2d8fFxjh8/3jZhFYvFSCQSLCwsMDTUvg9SJzA1jQ8dGedP7t166rw1g/lwsUCpjd+1EILZfI7eRBxb+tjuHstoCiDBl7i+j0Tw1GuHqmArkh4JpqqhCQWFKgFm+5KK79atIe8Fj1y2gu176B2QVrKFIWf1TVktJKQD1OTlAuHtfeOshxJUD7dEKNaDV9rbG2o39FAMz1rbo+VOINQIQgshFB1FqHhGHN/J4zsF8Er4Xut2AUOpYEe6UfFxdnlWCS2MHulG0aMoqoqULtIpIn2LWpKSGgYzPIwnZnCz9wltKSCllNXdVTQQKopmoOoaQpEYcYj0QHZ5sW2CCwR6bABVN4gOnKS8fIM9DqkOTnGRcM8xyis3m96TQsVMjOC7FtbmI+zCTrEX6hqjUG6PvATwSssU5l8jPvxC3fS+vj6WlpZYX19/Kom+G7Ztk0gk3pYaDGBwcJCrV6+yublJKpXal/R+cnKS1157ja6uru327SDs9uOqoTYCmclk+Ff/6l91FMf9bkNKaQEXYNv0VJNS/u7W3ybVwqtWaC0Dd7eWOyywDvGuo0Ze1QYaOyG3dqOnp4e5ubnta8hBoFAocO/ePXzf5+jRo8T3MoJvgZpSYzcBZwa0K1q7FCkKEFF1NEXB2EqsDlJ6ub6HrgQ/Cqi+3WSYDmBJaLyyCaFgI0gaIVJ4+J6FL7dCdHwP084hkAih4KgmrmIg/QrQTDS5voVBqKrwURJkJl/k28ae4czcW6zcv0almGVt7gYPrn6NaLoPZfMJlgOG6jB2ZJDurhj90xPo33EO1QwhPRekz5mPDhFKZFA1A6uYo5W43nfyqEa0PoVQi1Naeat6rC3aBgGEGm4iuRSzGztfVUo7+UdosWFwa7WXglCjyPIOMaSoocDqs9H0fWd/mwk3r7JGaekGZnIIPBtF0whljiF9F9/J4W5tL9Q1huMrrDx8c3tZ17HRwgmswirJI89SWLpLdu6NuvX7e/jAljeC6z6/hW+bbBFaEE3315NcQsVPTLG8luXB9a83zR+KJsi2GCeNJDKBJFcs2V1HcnUNTrK4tEoht0E6naRSDLKeiLFwb+d89AyOUpi/xtztq0ydfpnso9cYO/1BCnNXt+fJjMyQ3/o73jNOefUOiqphJAdRjRjllaqvqR7tRY/1UKmUyW2soYW6KC/eoJQaxrNLqAGtpzVMT09z5coV0ul0x7WHpmmEQiEeP35MOp3el7BhZmZmu4ZrJ7iskeTa3Tp59uzZjvahVu8JIfjUpz7FH//xH3e8/+8mDmuwdwaHnlwHiBq55XnedjpHp2mJIyMjLC8vU6l06LHUAM/zePDgAZcuXSIajXL+/Hl6e3s7vnBNTk7y+PHjlullB4GPjE11NP/Gegs1SwCKrkOow6/5bCnf0qi1CULwZHMDH7ClT0X62FKCgJCqkdB00ppBWjOIq3o17Q2Bwk5jYCtY0sfvyNxewfP8aoCB74Fng1eovvwiyDI7BBeARHZgQC/wQGu/8FeFjSc6GI3xK5iJSYSRQgn1okYGUSMDqOFeFCO55R/lIN08vr2OtFZQjQi+k6ddMzVd9cBIEB95idjgOULpIxiRGIIK0lnDqyzjW+tbBFc9pPTxJYT6zhLqPoqZHsWId2EmUpixGGY0jG6qKIqs+1yTvf1EeoNTHqsQ6PFBQulJtHAC31rFKcziO5sIrX3DfwDf3qgb7dXC3ZipCZAK5dXbWJvNLYmVjQdokfZG3mpYv/EnzUexpf68e/duYDpRK9TMTg3DYHR0dN9egEIIjh07xp07d/A8r2MlF9QnPu7ljWZZVh3JJaUkl8ttk3sf+chHWio1fu/3fo+TJ0+iKAqXLl2qe++nf/qnmZqaYmZmhi996Uvb0//8z/+cmZkZpqam+Lf/9t9uT3/w4AEvvvgiU1NT/OAP/uCBhIVIKb8gpfxdIYQihOgCTCnlvJTyipTyq1LK24eF1SHea6iRXEGeW+3WPbVr2EH4o5bLZa5fv87NmzcZHR3l7Nmz+yK4aujp6cFxHDY2qgMSqhB1vlyu71PxXFQhGI3EeSaRYSgSZzQco9eI0BXQlgdVmwbR4lBloI4LylINPD8uAsUrV20C9BiakUA3k+ihFGHdJBLtIRzJENdNTL/csr3Sbbif69E+hB6hb/wZTn/n/8z5//Gf89F//DN86j/8EX/vB/4nzr3vPB/88Mt818c/wUvf80Oc+d4fY/Tl72fo2Q/Te/Q83VPn6J5+nmjXwLbBvBlNINTWhIHQElWTeS0OWoLy2h1qdYbco3Lz7HpSZDfBVYNv55GA7YfxfR+n0KA+avV9bXG+pGehx3qbpoUzo1jZOYpP3sTOzuMUF/F9yM3dxbV9woPPsbH4gOz8W3XLWusPKa0+IN43g2eXcSvNKkC/tFIlAgP3x0aPNPus7vb2qjtWNURi+DTxgWNEMqPbRE4oltw1m4IdG+f2tddoZUNkRlsro4xQvQ1Jz+gJekdPEO8aoHf0BEMzLzB8/GXmF5Yo5Kq/sUQmOCV7ZPr0tp9XsquP0lJ1kF4oKk5uAUXVcTZ3PtNYzyj5+SoppugmslJdf3H5HlKq1AQ64Z6jWPkV8nNXcFZvIgoP8fLz6PEerNwS9//ssy2PD+rbBvcDIQSGYezbRN4wDEZGRtqu4YJqtEQiQTKZ7DgIaH5+nuHhamiCoih8z/d8T+B87/X6Cw5rsIPEIcl1QKjF2LtbRof7jaJWFIXp6el9p5X5vs/s7CwXLlxAVVVefPFFBgcH991uqGkao6Oj3LvXWrb7djEQi3M8036L0pxVJrSHMWgjHueyHTUt+0gct0W6TgAquopjBV/cXCmxpI8lfVwkZeljCIGpqIS2XmFFJSQUQkIh3PAS0ickRN0r3OJlAioW0ZDcIrTauOCqrc3+g6EgO7hshKKpFinlylYBmQQ9VS0kFQMhC2hGFOls4lur+NYavr2J9MoEEVnS3sRI7qF6VHT0yCBGYgIjNoxhxghpLthrCC+PGcH8YagAACAASURBVB9sSiPagUAJZXC1XlytGz2cIhICTWYxwskqudbmfcYrL2KmxuqOX48NVQkoLYxfqRJb0t1ppZRumXD3ZFvrr8F3ioS6pzBSE6jhHqzcAqWVm/jOHu2I0iOU2rs9b3tWKdGi/Xi2RXHpRtP7oVCIwcFBHjxowy9tC7sTfXp7ewNN5NtFOBymv7+fhw8fYtv2vrx1urq6ME2TpaVm75MaGpVcvu+3/TB96tQpvvCFL/DBD36wbvpbb73F5z//ea5fv86f//mf82M/9mPbAyY//uM/zp/92Z/x1ltv8du//du89Vb1oeQnfuIn+Gf/7J9x9+5d0uk0v/zLv9zx8e5GzdxUCPE/AD8P/CbwBSHE81vTZ4QQ+39SP8Qh3iHUyO39kFu7EY1GSaVS+04rs22bW7du8cYbb9Db28tzzz13YKqwmZkZ7ty5s00wJVUdDUFYqDieS1jReCnVx7Foin4zggBiqsFQKELfHsE+oRY2CrYfXGd5CDSCayTZwjjddXYkyULRieixLW14Mxy/2f7DlwJFj+DZeZzSMlKE0IwYPdMvceK7/imT3/bDDJ/9CN2TzxFO9W37PCmqEUyuAIrR+lLmWgVKqzcprVyntHIN6e+qp/a47/tODqFW7w1BBBew1aaZRrGX8O0ANX0LZZNvZ1uGBmmRZvW0kehrWD6PQgU9HEXRTKRToGfqRaLRCOnx8yjbg2oCPTNNYfl2y8RIp7geaDC/s+3mljnP2iHLQukR1O5TPFrxuPS1v+bC3/41F199lcuXX+f6nQXmczo520TvO03syPOIzHEe3qoSRY4VLG9fe9K67lB2KYsGZl7mxlvXeOuta1y58Nc4SpQ3L3+dNy79LeVdyq1QpPn7oWo6K7M7nSfDoxP4W15og5PPYIajjJ16H1ZhxxMsluza/s5kjpzBKdZaUz2Ki7ewS1mMzAyFhWtIr3mAVTNjFBfeYPPuX+G1MN6voaenB8/zmhJZ24GU8m2byPf392NZ1jYZvxcqlUpgjTY2NtZxENDjx4/b8hN7L9dfcFiDHTQOSa4Dwuc+9zl+//d/f5vcejseVplMBt/3O3rQk1Ly5MkTLly4gG3bvPDCCxw5cqStNsmnYWBggEKhQD7fwtPhAPDyYHupdTUUcu0nGxZdB6XUWerIfLmA1gE1tpgPjiJuhIcM9O/a/s40vCxZHTNUhNh+iVYvRdmJt24TUvqUrfYJQ4EPWnutaFVfdx/MfgoVdYvMSoBiAj54JXCz4GyCm4etIlJV1e0isS04OczUdDV5qIHU0vUIeAWktYp0cjQSZb61hpkY3SrkBGqoGyMxhpE4ghpKoGIT1sqENauqZNu1XDg1Tieej4oiMJPjVcVWKIZvreAW51BFa0LVLS6gx/pavl+D0CIYiVG0yADW5kOc4jJ2rv1WUWvzYTWRqdW+G3GM1AS2Z1BeuUvxyZusXA1OMB4aGiKXy5HLtdcKa9v2trReCLGdJtaJGmw3hoeH2dzcpFgs7ttAempqisePH7cs9BqLsydPntDX9/TPCeD48ePMzMw0Tf/iF7/IK6+8gmmajI+PMzU1xYULF7hw4QJTU1NMTExgGAavvPIKX/ziF5FS8l/+y3/hB37gBwD4xCc+wR/+4R/u42h3IKWUQoh/CPw21QjrJPBt7PQU/VPgh4XYqzf6EIf4xsJ1XT7+8Y8zOzu7b3JrNyYmJjpWsLuuy71797h8+TKJRILz58/T09NzoH6mkUiErq6ubZVDSCiYQsGWPlFV533pPmLaTptSRFExtmrAtN76nmq02MdyQCthDaJFkqDXwhes8S4nFBVNT6E6ufq2QEBqoSb/STWcwa7kUY0YeqQb3y3gqQnschFVj6DqSWQLBxbNDH4mlAEpyADCSGPnH7SsQ/ynGIajRQMJLolADffhOSU8u3U97VlrKAGJ09K3MKLB6iJFa25PUwPa9e38E/RomuLCG1RWblF4fAGBj/AstFCsqqgaPImzVlUDFVcetBwIDFJ4AUT6n2FhMcemm0TvOUW47zjh7ilUM0G47yQ5+rj49Ytc/dpfkFtfxi7niSQydevIb6yQza5z/cqrXH71r7n5xk5A1dKjG9uKvN0oZldbB4BtEbb9My/x5uW/rXuvlZ2E4/kcOX6+btrosXMUs1UCqat3iMW7VTVQPKKT1IpETIXc7jbFsTMUFm8Q6Rqhe+IFik/epBFlB8rFHKJVGIBmbqdYltebPcd2o1ZD1QLG2kXNWucg1GA1Mv5pNVwrtb2qqhw9epSbN2+2raht17D+vVx/wWENdtA4PEkHhM9+9rP83M/9XLVV7ABw9OhR7ty589Q4VSklKysrXLhwgVwux7lz55icnGyrH7pd1C6aT2vheTs4PzDc0fwLdgXRQdTsqmM1JavsBUnVq6Jd2IZGqc3PPuc5bZ9HCVQ6itRV6cRqTwiBEU612exXg4dUYkgljFQiSCW69YoglRBSGEjUrXZMF0P3CZn6FpmVg4BWwDpIByPau4fCqrbzKoqRRosMoOkRIl0zmNGBPUmtJig6ihYi0n0CI9qLgoW015D2RmAi1G749gahdGMktkAxUmjRAfT4KHr8CHqkH1WPg19GC4VwCnNIr912ZIkWjhNEpqlmEiMxhhrqwS1nKa/dwco+QnoWZqozDz3pu4Qz4w2HomKmJ9Cig1jZJYoLb6DYO6Nz+dkLlNfuN61rd8tPO3HQUsq6oI1QKLRtIr8f1NoWC4XCvgM8NE1jYmKCW7eCvQIb2xXn5ub2lUq0G/Pz84yM7JD9w8PDzM/Pt5y+trZW531Rm/52IITIAP8H8FtSymeklN+y9dbyVlG1CnzfoVT+EO8laJrGZz/7WT7zmc8cSI1SU7C3cw3yPI9Hjx5x8eJFDMPg/PnzDAwMvGNhPePj48zNzWHbNoqiEN8iN4ZD0abgmW7d3Ca5BsKtrQOcFtfpkuciWpxPu4XayKIFYaDFkA3bEYoCvkfcd1EK81WbBemjOllct4Rb3EmUE0IFdaflTI/04tl55Nb2FFXDjPXheQK7tFn3PdCMMJYTsF/SRTHq08OFGsbKPayus4UxuGett65PhILvS5xSfcuXUCMINYxTmAPpVkN2RGvfJKXFgGUrIoSA1lK3uICZbCbFglRWmmFgF1bJzb1BfuHartVWSAwGJ+ZZhdWmaeH+Z7j26pdRNYPF+2/x5t9+iSt/8xdc+dpfceXvvsq116/y6PbrTcsFtQY6leCa2nMsYukApZjrEI4HD8LaVpnBkx/g2uVmc3XPCR7Mun/9NfLZnQHsVM8Q6wvVa4IZjtLX14+u+EQMlUwqjGGYeFqceN80hqGS6J9CdbOkB6ehvEzpyRtNKkAZH8Vfv42zepdwd7N1ixHvRwoVIzmEHu+jvPn0Nj7TNBkeHu6ohtrtIdrb24vv+6yuNn++7aDdGq6xjtqNZDJJLBZrW1H7dmuw90L9BYc12EHjkOQ6IPT09PDJT36Sz33ucweyvkgkQiaT2fNHs7GxwWuvvcby8jKnT59mZmamY6PldpFIJIhGo3u28LwdZMIRjqYzT59xC47vY1jtk1AFzyXewly1FeZKhS2qpj2sWuW2CmwPWfXtahMV32u/cBd03IKoKIBaP9IpAYlaJaxEaOtlItEQCFDC4FcQfgnhF7deJYRfQUi7TvUkkIRjXXTE1XlFzPjITiGpGKihDFpkED06iBbuRtNDKNjg5qpx324eVdNRQ0/xl1IM1PAAFklUzawub6+DFsX12zfrFKqJoiiEM6fQY8NooQyKalQN8CtreKUneKVFvF0eX761QThzrIMTAV5llXDvsWqrYLgHPTGG0JPYhRXKa7ex8/M0knl29hF6hz5bdm6hqoSLD2KmxvFsh8L8G1VD1Bb301Zqrmg0Snd3N48fPw58/2kYHBykUCiQzbankAzavqZpzM7uPeq5F7q7u1FVNdCfYneLJTRL5T/84Q9z6tSpptcXv/jFfe/POwmx80Q+Q3Xk8Ke3pn8AKAIbW0XVGtC39d5h/XCI9wy+67u+i0qlwle/+tUDWd/AwAC5XI5CIVg17vs+8/PzXLhwAd/3OX/+PCMjIweint8LtRTImu9NWFEZMSOBITW7ibZuM8xQC6Ir2+IhH8BoYXaeFxHwm4muihJGBiidpGpU2+0a4KkhVD1GMjlOXLokhUIi1EMoMoiqaXilue2WPiWUqWt7NKK9KHoYZ1cbnBFOoRpRXKuIVVjDKqzh2hZaKIllu6BEEFpyS1Uero59anGEngQUPM/ePq6WBvPSRzWb61WhRZBSx9q4iWvnQaneIxQjhe9V6o9f+qhm522sMuCcV6cHf4ahdPMAsm9tNrUhVlbvoUeDa3CnRVqiW87VnSOz7xmuv/rl6h8tSudoKtiaJBSJNU3LrcyiGy3qWSX4WSeeDl5/sZClUGqhzC637gxZfHwbVTOIpXoRvoNVLnDi3Lcwffw0q7NvEY9o9HRVW4MjqQHKaw/xFRNNN0h394C1gR5NYyT6mzxW1dgAolgjcWTduRSqgZ6aIL/8iOzjy6CFyS7PsnDx8+QXn66yGhgYoFgstl1DNdY0R48e5f79+x2pwXajnRqucZuNmJiYYH5+vi2P6tnZWcbGxoBvvvoLDmuwdwqH6YoHiB//8R/n5Zdf5hOf+MT2j+3tYHx8nAsXLtDX11d3Icjn89y9exdFUTh27BixWPPN4Z3A1NQUly5doru7+0CVYjW8NDjC7Y32+8jznovRlPHTGouFPJFI+KkjrCFFJaJpGIpKSFGIbI2U1miEmvWolDUyaCtwQFHJb2ZJpp+ujMp5Nt3CbGu016eq5gq3q0oRKtXso3ZbLQQIHalEqm0D0qfquiFBBt/gBB5S7wGnPYNKgYMS7gdr8ekzKyZCiyAUnXDXTDVa2yuBXwG/sve5lS6KoiLNNL61ozoSiokayoDYalHwckQa7q0qFmo0jWfnApVWihZHGHGEUJBeBekWkW4eqLaLek577bxeZQkjPoKdb4OAUfTqfqOghjJY7bYgSh8j2b8TS/60zWhhjPgAqtlD7vGF9rYBZB98FSv3BDPRPAp75MgRLl++TE9PT8s4aNd1Ax8Ka2qw69ev89xzz3X84Oh5HqZpsrq6uuf2n4ZWaUU1aX8Nc3NzTE7u+Kf9xV/8RcfbGhoaqiPl5ubmtlNtg6ZnMhk2NzdxXRdN0+rm3wcE1ctZP7AB1H4d48A6kBdVt99RILdrmUMc4j0BIQSf+9zn+PjHP85XvvKVt12j1BTst2/frkv6qqnn79+/TyaT4fnnn/+Gp6j29vYyPz9PNpslmUw+fYEt9IWizJeb1TFFz6HfMKgEqNdbDfT5QkFHBlYZlYpDONas0HA9u1nnpYVx7Ty6mUBr8MhyPUko1IXveziFx6jRITzXY3dnnh7pxil6WMUVVC2MZsaQRQ3NUGAXQaIKFymtqoKqcb9cq+qFJLSGmqd1teFW6r1eFbMLu7C4XTtIz0KJDiGlh1teChwo2svAvhVp1UoFLlu0j6pmM0nkljeI9E5tG6YD+E6JcDhDkH2nlX2CmRzAyj5pes+IZbByixi9p7jx9S/vrM8N3v9wgM8VEFgLW6U8g5PP8ejma83ricYJok9aKbkwU3gtPOY2V+aJxtP40qNcqG9h9X2fzMAE849u0Z/UOTYYRq5exfE1BrojKNIlEa8+V6ilB3SnIjhema6hacpLb9VWgqKHUeNR3I2qv7FiRBF4dYmTNe84oepo8WEKT7Z8TyV4VgGQ5OeusnDlD5j+6D/fsy7qtIZqTKM2TZORkRHu3bsX2N73NLSz/cY6qhGqqjI9Pc2tW7c4ffr0nvPOzc1ttyt+E9ZfcFiDvSM4ZAEPELqu82/+zb/hJ3/yJw9EMq+qKuPj49um76VSiTfffJPbt28zMTHBmTNnvmEEF1SPb2RkpCNT6U7wUoe+XLP5HKEO1EHrVoWEoqMLhZRu0heKMByOMRKJMxSO0RuKkNANVFXFkpK85zJfKZL3HLKeQ27rlfcc8p5LwXcp+i4l36MsfWwBJU0gfYkuFMJCJaKohIWK3nAtqhFX7aLsex0lLbby5qquQd31qk31quSSdBB4bV05hXSRavsFtqF5WDSMXKoRhNFVTVMM9aCaCVRNQ8HeVogZkXRgjHlLSA9V01HMLrTIIFp0EEXTkG4W6WTZs4XRr6CFuhCKWV0+OoQWGdxqaXCR9ga+tdYQES6r29Pb94IUChCgLPSlBD2NFh1GMbrwrDJ29jF29gFGvP1wBgA7N4vawoukBj02gJEcwy5skp+9glNYpLP7pmT1WrAPgaIozMzM7NnmvNdIXiQSoa+vj4cPH3awPzvrDYVCzMzMdOTr0IiaP8XuIJBaUbMb7fpB7IWPfexjfP7zn8eyLB48eMCdO3c4f/48L7zwAnfu3OHBgwfYts3nP/95PvaxjyGE4EMf+hC///u/D8Cv/dqvtUwUagO1E/QYqAA/svX3BLCwFW89ApwBLjYvfohDvPuYmZnhW7/1W/nVX/3VA1lfMpnENE1WVqrEyPr6OpcuXWJtbY1nn32W6enpbzjBBfUEXCfXtv5Qa89Op4V/jrdH277TYpDRaUEmuKFgpZDX4p5syy0Dd0XFjHQjyysEdeno0T6M+BiqWbVMCKXHUMIN7WzSQ40EB6xotf1qGNTby1bAd3IIvVp/K2YXVm62aX63vIH0nJZKaN/OtSS6/BaDZtIt7aiwFB1FT6IYaYQWR4v0IRoVTi0GKs1Es9LbL6+hmcEDQuFksOekFoqjd5/k5oV6YqGSayYTATQ9mHx2W5jJa3qr+qBFsmPA76F7eJobb762nZrYiEopT7pvkMHx43XTu/pHmD7zPjZX5jkzFmV6KELIUNA0lZDuowkf01CJJ5NkuuJoKqQGJkhHFeyNHT+2wuINKmsPYKvV1EgMoppx7Fz9oG9p9SGKFkKPj+wQXFtwS1WPOs/zyc9dacuztJMaKqgW6+/vp1wus7nZfqJ9u9v3PK+twct0Oo1pmiwuth4gl1KysbFBd3dn3Qu78S7XX3BYg70jOFRyHTC+4zu+g1/8xV/kL//yL/nQhz70ttfX19fH7Owsr7/+OpZlMTk5SSbTflvfQWNoaIiLFy9SLBb3rY5ohZ5IlG8fm2St0pioIasKHFmvppJAStdxpI+mqehGVRnlI7cVVkiJX5tfSlzpo2sqDhLHe7oM15MSRYLf5nO/owhy+RzJZKoag73rfisQ6EKgiapblS19TKmgCVFX4gWVexIoeh7xNkenJVC2wHfLuK5HIhFHEdXWwSDvhip8pBJB+Hunt9RBaEgRQsgWo4tQ9Z0QOgiVSAw2NiSpmIaQNlW6z9rbp8uvYESro6StU40UhBFHUUPV0R7fRtN9HMfaIrbagGKgmimEUFGNGHZ+Ht9rM93FtzFivVQ2CuxJom1BukXCXdOUV28gtCiqmUL6Ln5xGa+0hBfwEbjlZYQWCmwFCd6IR7hrnEKD0alQdMzUEZzCBqWl+hRXp7iCjA4jiu23+W3c/jJ95z6OFmom1OLxOIlEoi7euW57jrOnXH14eJgrV65QKBQ6IvRrhqbxeJxUKsXc3Fydr0In6O3tZXl5mdXVVbq7u5uSFaEzkusP/uAP+PSnP83Kygrf/d3fzbPPPsuXvvQlTp48yd//+3+fEydOoGkav/ALv7DtKfbzP//zfPSjH8XzPD75yU9y8mTVI+VnfuZneOWVV/jMZz7D2bNn+ZEf+ZG9Nt0ScuvJQEp5SQjxJ8CPCiFWgL8HLAkhjgL/HkgBv15bbF8bO8Qh3kH8y3/5L3n/+9/P93//99PV1V5Iyl6Ynp7m4sWLzM7Oous6J06cOPDaZz+IxWIkk0kWFhbaVhAMhWOoQuAFEAEF1yEcQNjlHJuQpgf+2Cu+SyigpU+a+nbi7G44tSKuoZ7y9RbESiQKu2oLPZRAlXE830EVHnWXIDWC8PPgl0AYqHoSv1yvNNfNMFbALb2lwbydrRJKMrhmUtQIUjGwcnOB8whNxy0vIRS9GtrTuH4njx4dwqsEKOJ9B2F2g+8jfYms2VZID89VKK8+wM7W+xUpoW5KS7cw4r2Eu4bRY13V/VcN8OqVVcVScJ0XSQ+RW2xIdxcqtjSJDJzZPuNVVY3EweDWhT9tWk9hbQ7djDQlIdqFYKKpnAvu4hBKcPHttVCKWeVmcrAiq57di3P3MVUNP6D2X7h/ncnT70NRFMaOP49tFVm4fx0K85wejaPvqr3NcJSuI6dQFIiQxa/skEBeaQ2vkquq8GP9eKI6cK4LF0VTCA2eorB4q07Btb2sVSA68BKb9/+u+T27iKIZ+K5NaeU+vufgKcpTfUfbraGCarGaGuvatWucO3duXx6nIyMjXL58mXw+Tzy+UyN2kn49NTXF5cuXt5OvG1Hzfm2HNHsv1l9wWIO9UxBPGQU6PIH7wL179/jBH/xBvvKVr7ytUT7HcXj06BFLS0sIIXjppZfecb+HdrC5ucn9+/frJPwHha/MPeAP7t9se/6QUMikk9U0wjYxmUhT6sBUXhcKPeFI2z8GA0FvONbWZxVRVBIBqTiNqB1dRFFaemQ0wnNdFIqIjn7GAtwSokU8eCOqJJYBXnHrM1DYpiClBy1UYcWSRUjtLKRBKhGcrRY/oUURWgRF0UA64FUIvFwJFc+XeJUWBZQawZEmipAYakPjhRZvr6Vw9/r0FJWNhgJRCaFoJkIxEIpGtT0UpO/hlLNYmw/bXr8WHaa0eO3pM25BNeJUstVWCS2SQTVTlJbv4Nutz72RHKG0fLvl+0Hofe4f0Hf2lcD3PM/jtdde4/Tp003k0MrKCoVCgfHx8cBlAQqFArdu3eLs2bNtX/+WlpYol8uMjY3heR6XL1/m1KlThMOd+dXVYNs2V69e5dy5c2xubpLNZuvaEz/4wQ9y8eLFd6SN+xuE7Z+pEKIX+N+Af0hV7hmhOiB2BfjnUsr/+q7s4f8/cViD7QO/8iu/woULF962R2qxWOTevXsUCgXS6TTHjx9/+kLfQLiuy8WLFztqmfzPTx7woBicKtgdCuMGPBMMhaKUAgaYBJDUg20XEqhoZvMARlLVmuYXUpJUjcD16LIcrHVy1rcU67UhTwVh77TTSQSubeN7FfBtpFtCKDpWYblJ3ST0BHYh2NBbDQ/gFoNtArTYEezcw0C1lBYZxC1VSSgtOohfCbYO0CL9+FZ9faIYaUrr83iWHVgfaNEByivNvkxmepLswwC7AS2BEUmjhqNY5SIShVAkTmX5Hla2/thCfadYvX9p++9o/3HWFuepuILCUrOReM/UeeZv/G3TdIDE4HFWH9bXK0YkyUaAMkgoKpbrbacg1jA0c55715qPKd13hNXFR03TI/E0lfzG9nepd/QEr7+5M9A3MjrB6sJON0ooGqd3eBrdMDGjCVyrjO/YlFfvM33i/2PvTWPkyO5rz9+NNffaN9bK2tgL2WSzF+0eW4KMMWA/yDAw0GgMyXg2bHkZW8/jRZItaCxBsgRbGm96GssWDI3x/OFJlixphCfbD89jubV0N8kmu5ts7ixWFVl7Ve6Zsd07HyJrycrIYhabVLONOkCg2ZERNyKzMiP+ce75n3MctXoWsUlgCkHf+OM46zcw4ynaOjrriEahWyR6JlGaQamQRzolxA4C00h04Jc30BOdeBGm/bGOYdYXrpPu6MMrNdasIt6Fkj5ucY2nfvWbmKkedF2/Y11UKBS4fPkyJ0+ebPrMdvXqVTo7OyMnBubm5nAch8nJRmP8VlAsFrl48SInT57cOtdsNsvy8jLT07vDm6KxtrbG7du3OXr0aMN7WFhY4Fd/9Vf553/+5yZ7vy5wUIPdB7z2jMm/Q0xMTPDjP/7j/PVf//Vd7R8EATMzM5w6dYp4PM6b3vQmOjs775vp+37R3t5eJ+G/l3iiZ2BfzVJVJWnX92e234qCq257JTH2cVYuilKpuZHlTpRlgN9KAh3bSYuttifohoHQ99vOqsBINjxZKUAJA6XZYaqiHkdpMdBMEAqMFKAQqopQzpb5fLNPLZmwCfQWTVe1GMJsRzfi2O1TGHYbugaaLIdpjUGFps+CKkAXCl9spyQJMxMmMsZ70HVBzHAbCS4Av4CVbiWtRSCMNJrdhdBNrLYJdLsDYSQAATI0nA2qK/jlBfzybfzSbYLKEmaE+mkvSGe9wTB2LwRemWT/YxiJfiorMxTnz+5JcAG4uTliHa2pnjQ7Rbz3ETYu/VNkUQb1vgq7v7t3Mh6FULHQ0dHB/PydU4U2sTOa+m7iqHfDsixGRka4evVqg5JLKYWU8q6THB8kCCGEUmpZKfWfgLcAvwL8MvBW4CcOiqsDvB7w3ve+lxdffJHz58/f1f7VapULFy5w4cIFhoeHeeMb30gul6NSaVHZ+0OCYRiMjY1tWVq0gu4Ij6ZN2E0m0NwmNYoinATcOp8dD59N29IirsFKiKZ1mWyaQKghglLNrzNUdSktvZWiLVBhEqH0UH4RYaRQMsDONKrelFdoel+VbrTySI914WRvEFl7aHaYwLh1gOZ1m/R3fKc0m+xGmeyNH+Dm5tDMaIVZs/XNWhzNVDfZm2dYu/hvFG+eoXTzFGuv/Aury4tUSWP3HiXecwTNjKPpOrqVIHXoMUgNc/P8sxTX5pGVaJKuml9pOskcSzbaWbjlHFasUbmnZEAyooVytxJsE8Vs9PNHpZRj7NjbGJg6yeBDb2StUP+bTXf2o9cmllPtPaQyXcxeOsO1l77P5dP/HyJwcNev0WlX8VYvodXeW7ytl4GJ41SWL2Il22hra6sjuBL9D6OZFuWVS1SWzmObENulnPLL4XfJTjcmQwL4GKGfaiq67c7UNVI94YRgeekihflzLbUt7lS0N8NuT66dGBoaIp/Pk89Hk+N3QiqVoqurqy6IqFqttqzkAujq6kLX9cjnIt11HQAAIABJREFUzrm5uVedbv2g4KAGu7c4ILnuEz784Q/zpS99aV8RrFJK5ufnee655xBC8PTTTzM4OIimaUxMTDAzM3PXSRf3GpOTk1y7dq2lC+x+0G7HmGjbX4tB2dmj1S0C86ViXWHWClaq5a3iqRVUdLElob0TitJv+eE7gH0lMyJ0EK3fSEJIMNpDMkvbSWaJ8DX82sxlUL+PnkLtEYm9G4YuwNhZBImw5cDsQLO60KwONDOFrmuhR5eqoFHFSPSE6rEWoBQ4vsCItWGmD2PEOsM2Bz9PZE/gbvh5zNQOwkdPoFmdaLEeNLsTYYTFmvILSGcNWV1BE4rA2agZwe79twqqK5jJ/pbeC4D0y5Ex03UQOlZmBDM5hF8u4xVXImd994KZ3NvbwMocIt7zELJaorzwEm7uNre/+/mm22/6Kuwm6lshuQDGxsZYWlqi3KTFYjd2S+Hb2tpIJpMtx1FHoa+vD9d1yWazdSRXNpulra3tnqtaXwuoHRcipdRFpdTfKaW+qJT6nlKqtZSJAxzgNYau63zmM5/hgx/8YMv3YQivG5cvX+bcuXP09PTw5JNP0tHRgaZpTE1NceXK/q6jPwz09/dTLBYpFFoLPumytq9dcd1gKJ6iP5agw7SxdhE9Auix47SZNkPxFN1WDKtWOwnAEhoxTafbtOmz43RZMXqtGLamR7ZEArhNLAe8Zm1pGNF3UT25dR5CuYigDEILJ+JqfqO6YWLEw3uZ8vMIM4Hyq4gGz1KFlWpsp4fQl2vTe2vr0LFuqtmbSK+IbnfV+WBpZhuG3V7nz+WXFzHi0Z5W0iuGBtxGisr6Alp5Wz3erFWvmTrfKy5HTlBVGyxAQrT1T1DeWGDh0g+4dfk0qytrlEtVVlbWmXnpGVZvbpPE0q1gZRprlcLyDXrGjkeO38xeItUR/VnE0x2N516IJhk9p4IV3/67ZPrG6Zl8AwWZZq3kc+b085x67rvMz1yt2+/Si8/SNzxNe88hDNNidWEGAMuO8ehjT5KyJHaQp2d4GqewSmboGG3DJ+nsG0G3kiT7pmjvn0ToFnZHSKykhk5QWbmC9LY/Zy83h97EGkQzGmvy5MBR8rfDLpbdKYybcIsrW88hcz/4W5bOfhWgpWvc2NgYi4uLTYn6vWqxzbbFy5cv7+t6uhOjo6Osrq5SKoWTrDsnIlvF1NQUMzMzuG59q+rs7Oyr9kR9UHBQg91bHJBc9wmpVIrf+Z3f4WMf+9gdt1VKsbi4yHPPPUe1WuXJJ59kdHS0ThlgmiZDQ0P3zfR9v7Btm8HBwbsyhb4TnuxpTGrbCzP5LPY+SCuJwtynoKMig30do6pk08KiYVspI2c39zqXfZnQazH2/qmLmmeWXVusmo9DAthNZu2FGtHV5FihGk1HCRulJai6Jq6MI+yBGqGVCBVaOAhVCRVhET4WQjkYiQ7Qdt8gBRgpNLsLaXTikES30yRTSRIxgWEE6HZjEdX8YzFqCjIbMzUSfiZBGemuI6srSGe9ZkBf/7dQfhErM9byYez0/swyhYgoMoSOlRnGTA7il8sUb71EafE80q/iZGcxU9Ezh83gbIQGqDuhEMS7p7HbR3E25igvnkfJbdI9d+MZctefaTrm5OQks7OzdQVKqySXpmlMT0/vaWJfd/4RBdR+4qijsFnora+v17UGzc3N3bXf14MGIURKCPErQog/FEL8n7V//wchxBuEEEeEEPu7OB/gAK8R3vCGNzA4OMg3v/nNO27r+z7Xr1/nzJkzpNNpnn76aXp6euqI666uLpRSrK+v7zHSDx+b16VWr41dVhxb05lKtzOYzBCzLFJ2jK5Ekt54gvFUBxOpdsZT7YynOkhbMaQm0IVOwrTpi6cYSWQYSmToT6RJmRaGriNEmDKsaRrtpkVC10lrBnrFISEhrRnYQsOTkqA2WSvY7tFx91A7qcZMxjAJcfcqVC0h0Qsn56SLpm0fRXl5hJlEj7U1HEu5G1uTVruhW5naIRPo8T6q2ZktDy43fxPN7kBPHEKP9yK9XJim2PAemqvNNbuH/O1X8Er1KhWveBvNagwLcLIzmMnoIBo7gogy/fWaVcKuQ+9WZynJxtyLTSds2nujiUDTjg402EwL3I1Yk4RFO97YeVDYWGxK9mU6emnvHSEzdJyLl65y7tT3yWc3cJ3m9/ggCNBjSQLfJ7sStmoenn6MIw89SiKZoC0VI905QGEhTJ68deH7GLqkXNhg8fIPsJOdrF57Fk/pbCzOYvYdp7TwElETmr4TTTyLXc8SQrfIr26rnKI8w7bOvxqqqfLzL+FX8ui6ju/7dySf9lLUQ3gN3KvlOZlM0t3dXafG2g9213B3Q3KZpsnhw4cbJht2Jiu+3nFQg91bHJBc9xHvec97uHLlCufOnYt8XSnF6uoqzz//PNlslpMnTzI5Odn0QjM0NMTGxkbLaob7jaGhIVZXV+/5+Zzo6d+SCLcCBcT3+VW+XSki9tm6tO46+2p3KgrV8qxHLvBaJq4UsFZsrR0SCBVYtbZFpRRSaTUia5PQMtlOr911Dnpm92h7wAjHMntRRhdKb6stGZSWAi2B0GyECEPJE3GLuG2gaXpN0bU/JYyR7EfE+tDjfejxbvRYGsMQaMLFMjySMY2dVgUC0DUPPRmdsAQ6JdcAswvNakfTtNCE38/XTG5b/45pQtUiFO+MoLqKkWg9TMKvrGBlBkFoNWJrCL9SoXjrZUqLF5ARxvR2W7P3HA3pV0n2hrHRmpkg0fcoZqyN8tIrVNcafTk2Mfs/Ps3qy1+P/J0YhtFQoLRKckGoxkqlUi2psaIKKMMwmJyc3Hci2U7Yto1pmnXnMDs7++9CKi+E6AL+b+DPgXcTpvt8Gvga8AzwIvD12ravf9naAf5dQwjBpz/9aT71qU81VS9IKZmdnd3y03v66acZGBho+pA/PT3NlStX7lrNcL+QTqdJpVJ7JpBtImOYPNTeidK0hltuKfBDv0ghQAjUjtf12r9F7bXNz6gsg4Y7oxACJQS20OhIp0lYFramkdYNOnWTjGHRoZt0GBbtuklSC2sCvwkRpKJaCUUTPZOwEEhEUAuBETpGrHuLFFNeDk2PY7cfxg1MPBVDs7tRwsCI1boIhIZmtaPHetHj/eHkHAZuYYHq+uU6k3mhWSFpEVQJKs2FFtKtJzyEHkezewgCg8r6HMprrKNV4GImo1VPUaoqANdvtF6QvkNq4EjDeq+4QmZgatcxPfqnnowcW9ebKMua/F68anSdatrRSiXDaiQ9lAzIdEY/17f1T3Dx2iwXX36hbv3GSvPfgabpFPM5uocmyXT1090/DJVVTMumvHiR2fM/oGdgCFXz7T00/RS5+ZcorMyQ6T1MYT48Vv72RYh3E/gOsc7xyGMF1QLCaKxvnNxtMmNvJHHoOMnBE9hdkzg70ijzS1dJDjy6/RkoRaJ3mnjvNMKwaBs+QfvYE6xf/z6lxQvc+sGXWuqqaW9vJx6PR14nlFJ3VKOPjIzUqbH2i0wmQyaTYX5+HsdxGjxaW0FPT8/Ws/Mm7kW69YOAgxrs3uN165L7eoCmaXz2s5/lN3/zN/nWt75VZw6YzWa5du0atm1z7NixlgyRhRBbTPzjjz9+P0+9JWxK+C9fvsyJEyfuyZhKKfIrq/Sjc7tF83OApWIRKxFtgBqFSuAzpJsUZevHcGVAv55A13Q2J5YEYodb4Oab2KaKpOuBVCRTSTbn8qQCqWTdu/OVwpGSeIu+PkY8hislVu07FfXMXr9KAy2FkM4OArHFB309A0GtQBMGIZml1doXm41Vex+Bi5B3viEK5YfEWFBAqGoYqy0s0Gqqsq0yWoLyN+dl0U0bhIlyV1o22NdxIHmIoLSIMNMIzQzbL4My6TggI2bflIedHsLJt6akVEEFOzOKk2ttezsziF+O9rTaCc1M15IYNcrLM7j51kzoyxuze6ZERUGhiPc8TGX1CqXbL7a2T+Cxcva/4mRvkhl7M6lDjyN2+Lz09PSwtLS0lVS4H5ILQjXW6dOn6erq2rNA8n0/0iNr09twaWmJ/v7W20R3QtM0qtUq2Wx2y+fi9Vxg7UhBexr4SeA3gb8n/FEngQ6gDzgMbD5BbbLiBzjAA4uBgQHe85738Gd/9mf87u/+7tZ6pRQLCwvcvHmTvr4+nnrqqZZCI+LxON3d3czPzz9wxPbExASnTp2ip6dnz/eiaRpDsTSXSo3m31UZkDLMSEuEZpWVAhypMCPUNj4SYxcFpmkaUmwTI0IIbKFjCQ2pFNpWOvb2eBItSssFehsEu9OTxVaAo0CCLGNYFuh9SN9FutlaPSMxdIUKivjlQo3Y6gAtjl9dhx0qHKHHCKqNdYER70VoCr8cGt4LM4Xyookd6eXD9GY9iVfewFnbbqOzMs3vH7oZ/WzQrN6RTVIHrWS0B6odsT4oR6sVSyvR9YwMoo9ZzUfbtehNvN+aTW5nOnrJrdZPbvWNP8ali5eQQSPhvL58i0xbG+ViY7L25LE3cvncd1mYFaQynWhugYJhMGgbLKzfpuvQBGs3zgBgxtP45W1CKJFKUd5U6QmNuKXh525jZJop5RXxzgm8ShavEI5jJrvwRYz8zEv4lejP2a8WqBazW8dJDTxCbr5eLNE2GoZNLD73JaprNzh3+m+xkh0c+V/+M5W1G7SNvyXyeWhiYqIhqbAVggvC3+6mavRug8cOHz7MmTNnUErtq/bbienpac6ePUtbWxumaR7UYAdoigMl133GE088wdTUFH//938PwOnTpzl16hQzMzMcOXJk34lfHR0dGIaxL6+v+4nOzk50XX/V56OUYmVlheeee45ischbD0/deacdWK2Wyej7S7LMu80lzabQ6LZiDMaSDMSStJs2CEHedykFHsXApxj4FAKP/Obi15bAo1Bbsvgoy6AiAypSUpUSdwfBpQFmbcbTUUF4saut1wBNhclDnuNSLpbwXQ+UQigoBwGeVAQqdMravajdizBa9rICUGgoYYW+XGZfSEJpNmj6DqPRRvWX2twXE2VkkFY/Um9H6enIpewaeCoRtkdafSh7GIz2Wuy2Qig/9NxQbvjvHccK/Tg8qn4KpaJuuJtkWTIk64w2MDLouoGR6EfISsv+XEKW9tXuuJ/7v3Q3EBEBCpqRwNU6QnWcsnCytykvXaC61noCKYAmt5VZe0EphdU2jNU2QvH2iwgUKiLqOgpmspv0yEnAJ3/jO8z/y6e4+rVfIXfjmTrl1PT0NNevX9+S2O/HsF3X9TuqsTbXNyvAotomW8VmMXLkyBGuXLlCEAT/bmYRgSngHPA5pdS8UuqWUuqyUupZpdQ3lFJ/CnweQKkmZisHOMADhg984AN84xvfYH5+Hikl//qv/7pVZzzxxBOMj4/vKxV1bGyMW7du3dX1437CNE1GRka4fr250nYTnWbzNqFK4Eeq3P09fvKukpH7FF0HGaEw8ZRq8OwSQqBrGgYuJi4WDmZt0fF3+DvtuK5rjX5dggDMRtJBN2zwc2iajmZl0IwEiUwPVtskrugEoSOra+h2BnbZJKigipHowEj0AQIzPYyVGUEFBeQOUksAerzx2JrVjm734FUdirfP4mTrUwGlX0ZE+DQBBEH0/TdoQqaZIiJEB1BNxtH1xu9+eX0W3Wp8LnHLOWLpRtV54DTxearkseJRrYlNCLombXp2vL4dMtM9yCuXr7G6NBd2AkSg+9BYw7qR6eNcPhcmQSqpmJx6GM8p0zc0xsLFcH37jnTBwakTuMWQiOqZeJLyynbAQ8fISZzsLdziGsXl6+FkaQ2amSDRfwxld7M+9yLKasfV2/CSo+SyG2RvvYJbWsPYI3iotHoDM9VNrHu8geDqGn8ad+UipqEROCWkVwkTG0ur5OfOsPjclygvX9xSo+2EYRhMTExw+fJ2grbneS2ns6bT6S011t1gs22yUqm0nJa9GzuDgAAWFxcZHGwMlHgd4qAGu8c4ILl+CPjkJz/Jn/zJn/AzP/MzfOADHyCdTnPixAlSqf0m34WYmpri6tWrD4xk/tWez8bGBqdPn2ZlZYXjx48zPT3Nid79pSwCEDGjsxeWqmViQkdD0GnaHIolORRL0mnF0DWNYuCz5jlseA7V2s0i67nY+7gw+0rhRdxoNiEJCz5HhQRYLvDxah5dvlL4KAJAt0xiyQSaaSCBAEWAohy0bloPIDWTQO3lmWWitBhKS4AerxnO11RbmglaCoW1Y3sj9NgScZSWrLUlpkJTWL1mWK9ZYKRReqpW2tTTcfG4jWGIHesIvb30OycPBoHCl4J4IomwB2okVhsY6ZA00wyEUIiacb2QZYSsIJSLpmv7SioEMGMtJkICyi9hplq88UqPeOckQrcxU4OYySEgjpNbQhRm8bLX6pReSvokevdHBO8u3OvOFYHdOYGR6KW8fJnycuhH4RYWmpqgbsJM95EZOQm4VFfrWzn88hq3v/cXrLzwNxRmv4tf2cCyLIaHh/eVCLYTnZ2dmKbJ8nJ0a0gQBHs+tG76Ouws8lrFpqF9PB7HMAz+8A//8HU/i7iDDDxLOEsY3atCvSnqAQ7weoBt23zsYx/jt37rt3jrW9/KF7/4RR577DGmp6fvSkmg6zrj4+NbD1gPEg4dOkQul6N4BzuDtGE2TVIsBT5mRI1T9D30PX7+WkR7vmYYiCb1UtDkGW3nETa14hoylKsHxVCFFRRB1khGLRYmPwtjy7tLKB+1a0JPKAfN7gJkOKmlQkLF1H0s3UczUxiJHgwrQbxtmHjHEYwdhJX0ihh2hljHGEIopNeYNCe9Yt3kijCSoKVxNm5SXb+K1iQJ3C+vYKWj/a780lKk9YFfXgsnLnevr2xgxBvrFCd3K3IiDT+CoFKKVGe0xUHUerfSPHUv1dGomPadaHW/W6n/3trJNvqmnmat4NB++EkGpp4gkeki6xiUCnlkEDA4/lDkWFYsuZWiCNDZN8T60rapf0//ELcvnyKV6UCvrqLpJpMn3sb6zVC1PnrsbWRnQ0VXovswzg6bBjPRsVUjQdgOumkJYbcP4ymTlavPUl4L/as03aCaXaCy+Ar+jhZOw96rxhWYbUMUFy/VrW0feZzSrbOomnquvHodzUphJcK/eWn5Im5+HmfjJjf+20cinw+6urrQNG0rqXCvZMUoHD58mIWFhbtOm81kMmiaxsLCwl3tD2EQ0A9+8AO++c1v3tFP7EHHQQ12/3DQrnifsbS0xCc+8Qk2NjZ4/PHH+fKXv3zX7PUmYrEYfX19zM7OMjY2dm9O9FWeT39/Pzdv3uTw4cMt71coFLh69SqapvHwww+TTG6bfmYsm/FMB9fy0ckqUZjN5+jMpIkS9GgI0qZFXDewdR2t5ueQ1HWEEpRlQHkPMqruvD0XSzdalupmA48uB8wWTBYdJfGUbEg5agYfRVUGxDS9demwHqNaLWGbhB5amsEW391kDKXCFgJfKQJ0IIYpNHSh3fG4m/siDJTehlAOeh3hoqhWHZSSxHe2oAkThQmqwk4+3nNdpAywTIPtSUgFBCgtgfBbUxUKfPR4H0F5H4l7QQnd7qyPB4+AUgqEiW6lCaz2GlGosTULrRQgUUqiZIBSPkgHt7CO20Ti33CMVtIhd8DJhQb0XnGbHBK6TaxjlGr2FqWFxtZHv5oj2X+U4q1GX0GrbYBY2wCVtatUVqMJo2T/wwhNUl44RXnhFAC63UZy8Am8chdSRhvW3gmTk5O88MILdHR0NBRnrRiabrZNrqys0NMTbeAbhZ1jP/bYY/zGb/wG6XT6dU9y1WKrvyOE+Drwv4rQGXce2FBKtRbbdoADPIB44YUX+PznP8/58+f5yEc+wnve855XPWZvby/z8/Pk83kymf34Vt5fCCG2zJ1PnjzZ9N4shGA0nuJyqbGdC6iZMNQ/S0kg67ukDSuyTnADH7/qYOg6ur29jaskdkQ946vN6bJ6SGGhqwiVnG7WEzLKhdqDvghCkkVBODmmJGjxrde3hjB0ZJAEfwfJogJimQEKhQIxN6w3hdAQZhxK89ht4/jVDQy7jaAakgKamUZPDuDmZxpOM3DDc9GsdrzSep0Xl1eYQ493EkS0qTUjAwUBwu5EVXfXBQorPYCXn2tYnxp4lMrGLZAegVfBr2RRgUt68BHys2frti4vXyLTP0V+sd7MO5bqIOrbYUWYxjvF5nV6LCIxsZJbidgS1ucv0Xv4OMpMsLKa5cKVV5A3vg+ERNG20mu7hkmk2iJGgosvfI+po09y45VTTDz6NPPXz+NUtv/u/X29xPRuKC2g6zrD49OsXXsOITRGj715m+Dqewijeht2WJukusco3Kq3cNDsFPG+o6zNbBNQ24jmJaLUcpvIDB2nWswT7zpMZS1sE7XTPbgb9S2jQTUPKiDRM0l6+ARubg5QrLz8/+KsXaWycplEhIp/amqKs2fP0t7evm/biJ0m9sePH99326LneaTTaebn5+nq6tq3AT2E17C3v/3tvOtd79rTR/H1gIMa7P7hQMl1n5DL5fjIRz7CT/zET/CWt7yFixcvcu7cOebmdt+Q7g6jo6MsLi7iOK21Et1vjIyMsLS01FJyWaVS4eWXX+by5cuMj49z/PjxOoJrE8e7ow03myFQkv5YnOFEmrFkhm6lMRRP0RtPkrQslCYoq4AN32XNc1j3HOYqRcx9pCZCTc21z30KKthKFLoTNjyXyj4S4BwlcSJmRZVSNaP5sDXAVxJPSlwpCawYq0UPHyNUM9XMZHfuK5XClZKK9CnVFkcF+Ch8oKIkZenjBj6BlFvH8KXEkwGuDHBkgFsj7jwl8VF4wqrz2wBBLBYjHk9sk0GiprLSbRAxhPK2FssUxGyD6JpQorTohKQoCKpg3lkxthO63QZ6HGFmwlRIqxPN6kSYbQg9GRKHKpwtlpVFUBK3MIubn8HN3wiXwgxuYRavOI9fXiCorOBXlkh0T7Z8Hn5ljVhn66QygN02UHsPGWJd0wS+R+HWObxSc2KttPgyqUOP7RhjkPTI4yivGJJbEd89M91PZvQk0l0jqG4XwMKIEesexVk/R5f7DMJZbMkwtWH8Jik70Ho09fT0NDdu3MDzots7olCtVre8wHRd5y/+4i+4fPnyvlouH0QopZQQYhR4G/C/A/8CfAX4ihDivwghPiOE+KQQYn8X5QMc4DXClStXePe7382HPvQhPv7xj/OP//iPfOELX8Bv8T68F3aSSQ/axHpbWxvxeLyp0nUT3VZzha4jmyvEjaiHyapDJZBI20K3bYTQsIWGJTScJqmJfq0+2Q25l7umFkUKaFvbC0AEBZDVmrorgdJqiwjtDzaTEqGmRNdshHIbPB7DlORxdCN8fZPgApBeAelFP3cqv4IeH8DNLzSYzYPCjEVbHsgm7YQArjJre0OgJRB2B5qVxk51InQbPdaOmerD7pwEPUX2+ncJXIfc7MsUF65RzWVBJNCMGMnB45jJsCVP6BaJvodJdA6RHnqczPBJ0oceBSEwI0zgw/feOLkWeNUtJdFuaHpjoVbOLhJP1W+f7h5GdD/C7VzA9555hisXX65rdU23RYfz+Hu0DVcrFSaPvYFr55/bIri6+wY58eSb6e1I0ZZO0tE/Sv/IFL5Tomv0OCNH34TreQgzSefhpzAqt7YILsNO0TF8vIHgApDKoFLIRRBc0KyzrFmLqmbGyC1eZ332Jfxg+/eWbOtFujs+f6HRNnIcXQdn/QqVlUsEbhmEQNc1dNPixrd+l4Xv/2XDMXa2/O2X5ILQOqeZif2dsGk6PzEx8aquoaOjo/z0T/80hcLrnwM6qMHuDw6UXPcJZ86cYXh4mGeffXZLRvmJT3yC3/u93+Nv//ZvXzXrrGkaExMTXLlyhaNHj96LU37V57NpQv/YY49FbuO6Ljdu3CCXyzE+Pk5XV9een8Px7j6+er3Re8jSNDpjCdKWhanrBEBV+pQCH08qSpu+fDELt3ZzanYcxc4SqXUsVsv0xBIELe7rKknFc0lo2h2VfELTKCOINTGDVGqTIhIIoVBKUA18fCmxhIYSkTmJDYin0ziosL2rlpYUKrVCwqrZ/uHxBQqFq6CCJC4Uxj4Uip6wMVW1pZZUKWwCr4hl3nlrAWFBK0stjS0Aw0rjNylYIfQXE3o8JOakB14WTTMIGmZWo2HGO3FLranF9Nj+ZrRUE1PayG0VyMDD7pqivHgBJ98Yc94MpaXzpEffgJIOztpVqqvRCgDNiJEaPIZXnMMr1Hs22J3jaHqAXwpju5EuQ/oZZi+1c/iR/6nlc9nEbhP7TbRKcu0s8h5++OGWjlmtVuvGnp6e3mpb/IM/+IN9v4cHDJ8jND79HGFtMAT0E/pExAmNT//za3Z2BzjAPvBP//RP/OIv/iJvf/vbt9a97W1v40tf+hI///M//6rHT6fTpNNpFhcXGRh4sFLdJycnOX36NN3d3U0J+JRukjZMCrU0PgHENB1XSfK+R0I30GlUagvAkwFJzcBzHAIUWsxm03BAFxpSSTYf9S2hbRnB74avFGZUjYOBiAoe0kyQu1ukZC0cZ7tlThCALIfkwo62QqUnwzGMNpR0wS9udfFbmqAQWNimQGg6Qo+DUtS7cGhoVgZhJEAF6Foc6ea23p2SLsp3kNJHaGYk4dHMNkC6zVtMdcOilDcwvQ1QWTapn+raHHq8h9JSY+usuZN0UgHV7C2quUVKVZ/ArZDqHqWUW6Jw/QWE0Cm54JbDzyrTM4rWpLYort6MXN/WN8bKjXqVmJ3sIFcVzJcSJFNpEsk0be3txG0Tw45TKmRBaPhK5/TzzxL4M4wfe1Pk+PF0htx6Y82SW1/GiiVwq/Xk29jDT7A0d5Vk+mF6h8bJry1w9MTTlDcWsWSRYk7S0dnJ2rVTbCjFyNEfwS0sUiwJROCQSKUozr9A7+TTeOV1dDNGUMlSuB0d9lNYX8BuQvSpIHoiLXDLJHsmKK3UWzcmhbK9AAAgAElEQVSk+h9h+cqzAGzcukhHTx/pvknKt3eZz489QWXp/Nb/J3qmUIGLNMztv5+SZK/+D/qe/o9ou3yL+/r6WFpaIpvN0tHRut/sJqJM7FvBZh3V1dXF0tISy8vL9PXdHXfz5JNP8pWvfIV/+7d/421ve9tdjfEA4aAGu8cQd2BQH6wpqtc5lFK8613v4pd+6Zf40R/90Xsy3tmzZzl8+DDt7a17Bd1PnDt3juHhYTp3mDf6vs/NmzdZWVlhdHSU/v7+lkm+z798CicIsIywwa2qAoq+t+f+I+m2llsPISxPBuKpSDXUXmgzTFKGhaHpoWUVYivvQlFTUBGqqLyaiirmS7pSGXRd3yr6fM9DBgG2ZWEYxtZ6HUFS1wkzglRIUtCcfNrcJ6GF7Zi7P6PN37raSnlUlCoVNMNAM3QMoYVkF/Wk4E5SK1A0JfaSteO2CoMAQ7WmogkCiSFbb11VgPD3bimsG99XyFqrghIGaOGsrlstYAivsTvDSOFk72zwuwk/ALfYmlGn7+m42da2VWgEjkdQjSadFBp2ZgiEhpOdJ3DyxHse2orBbgVW5hB2Wy/VtavEOsdx8otIp5EUTA+dQPr5hmJd6DaJ/iPb5FbDOQpiIz9J5/DTLZ/TJlzX5ezZs5w8eXLLh2tmZoZEIkFvb7PEox3HVoqXXnqJoaGhumtWM1y6dIm+vr6t6+21a9f46Ec/ytraGp/73Oc4fvz4vt/DgwAhhAWUgF9QSv0/Ea93A4PAiweeED9UHHzW9xC5XI63vvWtfPvb376rh7rd8DyPU6dOtZzO+MPE3NwcjuMwOdlcHbzhVrlQzGJpGqXAx6/9tHURzpR1WzHUdlmDqqnCXRmQMKzICTujFqSzu/5oNww0BN6Oy4eBIBYx8SdQGFEtiwB+gcafhYbwG9OJlbBQTmNrnMQmqDQqUBQ6lXIJQ+xQ0msmCBuvvIgK6hX2mpki8Fz8iAkvIz6As9FYIwg9hl9t5mGlIb2dJJ6gItsQhev4Zh9aabZhD7t9hPz8S43rO0bJzZxtXN/7CCvXTjWeb/s4qzfrx+kYPU5pdRa3tF176VacRN+j+G4ZpCSQiorjkWrvxqvkyd26QOBVSfce5tZygY21JZzAwHPrlWpjR9/E+TPfaziPiWNv4sILjesnjz7N1Zefa1gPMH38TVw+F7Y1jh05ju95zF+/EJ6vrjN+5DGyty7Se2iEmKjglPMcnnqE1drn0DtxAi83j27Y9I5MUdxBZGm6Qcfo45g6det3InXoKKs3zpA59DDV1UZ1uZXqprixHKl6bx89SX5uux4zk52U8zkCb/u71jb4CMlMG+7yNqFlpfvQhIusBQMZ8U40Q0e6oWLN7pzAWb+GZiWRbonDP/lHxLrGG4iuarXK888/z8MPP1w3WdgqVldXWVhY4NixYy3vMzc3h2EYDAwM4HkeL7zwAidOnLgrj8QvfOELlMtlvva1r/HMM8+QSNydBcZrjYMa7P7goF3xhwghBJ/5zGf4yEc+sq8Wmb3Gm56e3jNp7IeN6elprly5gpQSKSWzs7M8//zzWJbF008/ve/e6eO9A+TwWfEd1nyHUuDfcX89MmWvORQQyGhJfd12SpHSDTpMi5RusO46FH2Pda/Kuuew6lVZdausetXQsN53yfsepcDHlRIFVAyN28Ucvtxs+1NgGmgxG08TVGreYGUZUJA+y56DIwM8KQnuQHBB2LJZlD5l6eMEPm4QUJUB5cCnKH0K0qcoA0oyoKIkWsyGGoHoKrl1rpufhy8Vbs0Y362Rds3QrC2hGXz0lp/gdF1DidZVS6E+r/nlLWzlrKVHChthJlBaEqk0lF9Guesodx1TiyC4APwimt06sWzGon0jomCnW/eIEkji3eP1KzULu32slv4UUF6+SHnpAoETFtbOxkwtQWpvWJl+0sOPo/wC1VrceXX9OppuYLcPb20X6xwjM/IYfvl2A8Flt48S6zrUlOAK34OicPt5KmuvtPq2t88xwsR+P9J7IQRHjhzh6tWrLbUxbcrsNzE7O8vo6Ch/+Zd/yfvf//57cl1/jdADzAGvAAghbCGEWfOFQCm1qpQ6d1BcHeD1jLa2Nj7wgQ/wyU9+8p6MZ5omw8PD3Lhx484b/5AxNDTE+vo65XJz78YOK0basMj53hbBBRDU7vVVGeDJgGIQKuXLMgjv80JgCi3yDuurcL9g10RjOZAgBIYQ6Cq8OwtNIAQYYeMges3IQO3ZshjVZimj7/fKhQhzdg0nsvVRECB3r5dezYuzkRSVXhGjSfthU3/ToIqZbDRjBzBSQxipQUS8H1d04ARxRCEkymJm9CSsk51Dj6gvnI2btI8/hdDq37/wo03fYxHeViqQFEoeqd6wxmgffoyyq1MqZLnwwg+4cO45Lr30PLOXz3Lhuf/OlWszzK35WP3HuXx9nvWVBZSU9A6ON4ztN0k3rxSjJ+z2wvVXzvDIUz9GV98QM5fObRFcACMTj5CdP8+RYycxvSwDQ2N0JnVWr50i3T1E78Rx3NUr9Iw+SiZl1RFZQtPpGD1ObvYMlfwadqbx7yY0g+J6qNSv5qO9xtziKumuQ9iZRrWSV87W/b/dPlxHcAGYsRTLV09hd4wQ75kGIN7Rv0VwAcQ6BrcILgCvcBvNThPrGAOgePsFbj/z5w3Hj8VixGKxO7Y3N0N3dzeapu1r/51qe9M0GRsbi7SeaAVzc3OcPHmSX/iFX+CjH/3oXY3xgOCgBrsPOCC5fsiYnJzkne98J1/84hfvyXjJZJL29nZu3Wr+EPnDRDwep7u7m/Pnz/Pss8/i+z5PPfUUw8PDd2W4f6yjZ98pi3OlXLRvxB5YcsqkjV3pHLtILakUq26VhWqZVbeKpyRLThkTsb+EQ0PnViFH4N05glwCG4GHqyRKSoTaThyCUI3lK4UjQ4KqIAPygc+677Hsu6z6Lq4M8JSsKxpVrcjUAd9xqZTLyJrirCh9Cptk1z6EBL5SyH0KD3yxj/Y8I9H66EqC3rGd/CjiKBEL0yOVFr6uHAhKEBQRQR6hGyBb90IzY9EeEZGn4+WwktHpSbsRVJcii9bm26+F/hzt45ipQQKnRGnxAuXli7tmhUNIv0qiZ6LpeGaql/TISVRQorp2hd2z5oGTxyutEB84RmbsKQRlvGL9rLjQTBKHHgOKSLd58hJGErtthISeo3DjG3il/aft9Pf3U61W2dgIZ5tbbVfchG3bDA4Ocv36nZV5u1OINpMVH330UX7qp36K73znO/s+/wcBSqnbwEeA9wkhNKWUo5TyNmOqhRC6EBFPiwc4wOsMP/dzP8cLL7zAhQsX7rxxCxgcHGRjY4NSKZo8eK2w0zdsL4wn0k0fBHK+SyUIQNbfA8KE6bCpMKHpJDQdW2jERfhvBViajoUI6yMUbs2vUwiBpuvotXrQ21HThDWJqk1/NfM5VDsSfPXt7SLSmAWAGT2ho1vRXpzJRNRkmkK3otPQo1IWAWQTMglAt2u+oULHiHdjpoZBJCjdeoH86iKVxQvI7DXUDvW3X15BSw9HjKZI9UUnLVfXrpIefLR+3fpN2ocebdi2vHiBeFs9CVNen6O0scit65cxOya4/uL3KG0sUlqPtl/ItHfjOhUuv3QKp7JNrqbaG4nAUq5ReQewejuaMF6cbU6CTDzyJIHnUKwFVcXiSR574s1MTYwT1xzGHzqOX1xhdPIIQWmNdO8k/Q+9BdOOYRkaI8ffTlC4hV/dVqjHOwZpG5gmVzPqL63dRNht7G68TR06SjUX1j9ucRWhR6f8OflFrGTjxKhb2ia5MsMnWb1+uu51M9lB/tbLoCRLM6+wPHuRzMjjVNa2J/bSQ49TXatveZReBRV4yFrL7NqLX8XJzuFk51GyfkJP13UqlQr5/B612h6YmppiZmam5Um+3TVab28vUkpWV1uzANmJ+fl5RkZGeP/7379lh/N6xEENdn9wQHK9Bvj93/99/uZv/oa1teiL/H4xPj7O3Nzca64iUEqxsrLCysoKa2trHDt2jPHx8Vcl40+bFhMR6Sx7wVeKZIvphDuxXCnVSC2blG7gR5BaUbhdLaErELuJLqUwEcSFRkLo6J6PUyjgV6sUnSpXizlylTIblTLr1XBZa7LMlgvcqBTY8Bzyvkc+CImokgyo1Mzdd59dmGqoKAcBvpRoSoFSBEqGHmE1xVhg6qGSbAcRFrC3aqsZnBYUcZvYnLH1laBarZIvFHAcF6WgVCrj+X6tR2KHyZiWqhnKmrVlOzpc1chGpYKan0eYWEiQry0FkOWQ3IrwxdB3k5x3QlBAs1ono8wmfg2NUMS7mxvKK6UItATYfVjpYcDAygxQWnyZyuqVpv4PO1FZvUyitz5620x2kRk5Ccqhunq5lgDZCM2Mkxo8Bu4qBA5WZqjub263DxPvGSEo7028S7sPwzS2DH0FAbkr/5XAye65327sVGMFQbBvkgvg0KFDlEolstm9j62UqiPr5+bmGBkZAeDDH/4w73jHO/Z13Ncam6b/Qoj/AHwe+I/AXwsh/jchxJObJqdKqUAp5YvXc4TRAQ5A+ED3R3/0R3zwgx9Eyv1ZFERhp6L+QUN7ezumabKyEq0wAUgZJscz9RM2ttDoNC3SQsN3HITnEdN04rpBTNfxgUJNMb6pPHeUpKLCf0sgF/h4KFwkBgJbCAqBh9x1XwlQdSqyTbJLCD28f0u/Zijp1iahJGg2QjoIWQ7v6VBLY26EaFILNiMjhCwjo+yKZfSkpAoqkcos6eYQepTqTKAkCLMDJ7dKafESxVtncbJhKFU8vUfbvLOM1BtJuOraFeJdNbWU0LEyg8S7Joj3TGOlukkOHCXePYmRDNvRbKvxM5G+Q/cuxZVbzpLqOkTgVVm6vu0HVcktE4+oy2WTR0rlN3522eU57Fhja5nrVGjrbLQaKOY3mD7+5ob1R068mUtnv8vs1ZcYGn+YqWNv4NFjxzENja6BEbSgxOLVM6TTaW6/8n3WF26glEtl6RXc7Dypti6WL/0b6DHaR09ixNvoGn8Kv7hCcan+N52df4l458jW/1upHjZu1SvQY22HIj8DAL1mNG+ne8gMP46V7sWrZNHtNPHeh0Ifrl2/j7b+qTrFlpIB1UoZxwu/o2a6F2djJvJ4yq/i5hfQE90kB4/ilW5TWn6Z5bP/hcrKpa1J0CAIeOihh7h8+fJdXRMty2J0dJSrVxu94aIQVaMdOXKE69ev7zsYZG5ujrGxMTRN4ytf+Qptba3X5A8CDmqw+4sDkus1QCqV4rd/+7f5+Mc/fk/GMwyD0dHRlpQI9wvZbJbTp0+zvLzM8ePHeeSRR5iZmbknYx/vaL19axM3CjmSWnNyzUDQYdr02XG6rRgJ3aDgu6xWy6w6FVbdKn6LHl0aUA48gkBiKYGSARXPZcOtsuyUuV0tcataZFV6lC2DglB4hoarJLOVAjmvSjgvujcCpVhxK6w65e3efqXQa7OlptAwEWiECi9Xya22x/XAo1QrRP0WSaiKDJoSHU3PEQh27BIWrAIdgS7ql03fsELJIWYbZFJxbEtD4JNMWJg6gA94tf/6oIVqKyErtaUaLsoJ0xcJthodBArMO/ssbZ2rctDjd/Zx2gkz0bqHgXKzmInWzDWVn2fz8qyEgZHox8qMYiT6AQMqawT5GSqrl/GKCygv39CWsPcBJH5pCbvtEEaiIyS3hF9LS4z2sxOaSXrocex0B272OipwcQu3cLI3sdIDmJlBUoMngMqeRJXQLGIdE1jkGx4clF8ie/nvCPYw4Y1CLBZjYGCAGzduIKXct2pUCMFDDz3ElStXmqY9+r7fYOI8Pz/P2NjYHcf/8pe/zKOPPoqmaZw6te2Hsra2xo/92I+RSqX4tV/7tbp9Tp8+zbFjx5icnOTXf/3Xt4jE9fV13vnOdzI1NcU73/nOLQXb3WDH5xQDrgCngP8Z+BvgOWBBCFESQgRCiP+jlgB0UGQd4HWNN7/5zQwMDPCtb33rnozX3t6OYRh7kkmvFaamprh27dqeKbZdVoxMbZKn3bCQKHK+RxmFskxi8XjoxaVknaeWqySiSUoisFVr+CiqtUm0qLqq2mwMYQJ+48TUDkGDgG3CS0uitNhWerNCBzSU2YEUMaQW2hIoLRGOJ3YTXQKMDGa8h1IVlN6GZncizAwqqKBFtCwC6FYbenwA3e5EaBaamcJIHMJMDWAkh8L7tjDQY50oEae0+BKabkV6NOHurUSxO7ZJFoSOmR4g1jWFlenHSg+gfI/q2nVKixco3X6JwvxpDCtGafkybmGZeM80QjPR7UayrHj7PLpZTz509o9GnkffcKN6TI9IUgQo5yP80pSid3Ascvu2zuiaf7e5/PTxN3HpbOjfVSkWuHb+FIbwuXb+DIX1ZVavnaKwtsjEo0+SnX0RhMahycfw1q4RuGXa+yfIzdeUWuvzFFbnaB98hNzsGVSTOsiItwOC9NAJSoW1OvXX5ns1ExF1p9DRYylSg8fJri6yePkHZJfnkHYXFd/AqzYq/6xUZ6ji2oGOkcfIzr5IJbeEEWsj3jVRR4JtIjV0gnjfNGaqE83Ukb6LEYtTnP0OlYVnWXz2T1l89k/xy2uIoEwymaS7u5ubN6ODBe6E3t5ePM9jff3OXri+7zeIHzatJ1olyiD8DlUqFVKpaJXlJh7U+gsOarD7jQOS6zXCz/7sz3Lx4kVefLExivZuMDAwQD6fp1jc38Phq0WxWOTs2bPMzMzw0EMP8eijjxKPx+np6cHzvDsqI1rBQ21d2Nr+lFkSRdlzUVIS03S6rBgpX5IROramUZY+S06ZuUqR29USWc8JWwM9BzfwIxN/NqEBGcOi3bCwEORdl/lykeulHFeLGwQy9KRoBUopnCCg6LnEhCCpG1tLQjNIaDpxTSemhZHcptBAwVK1jOt5VJWkVPPaKgRebXY1VHbtLhnzgY/YpzirqoKWlFmCkDi0EPgqnM/bJrKIjlaqIZXO1IrRVqBA38dMjfJBNI9K3w2tSRJVU/iFBm8upRRoNhhphNWBsLoQVheY7RiJXjSjE83sQjO70a2e2tKLbveh230EooNiRSDaJtGMDLJaxNm4QWXlIs7GjcgUpsDJheqqfcBM9xHvHiPWPhCSW82+s0Ijdegx4p0DuLkbkRHimhkPC1yxd66nkejDTHTgl5unTUonS/761wj81ltHIWwbyufzd63OiMfj9Pf3NyXnd/txQTiLODoa/RCwE0ePHuWrX/0qP/IjP1K3PhaL8fGPf5w//uM/btjnl3/5l/mrv/orrly5wpUrV/j2t78NwKc+9Sne8Y53cOXKFd7xjnfwqU99qsV32IgdtdI3gHcBv1j7708BPwv8J+D/Ar5JWIDBQd1wgNc5hBB8+tOf5pOf/CSVSmNL991gk0y6F+qwewnbthkYGLjjw+tYPE1GM0K11a7X8r4bmtFHQBEmKEbB2TVRpoByECClrLvjS4jKUgTRzFtRoqJeEzrCz4P0wppCVhBBHg2F8jZQ7hrSXUW6a0h3A81OEUiQWCgtge+7+OUFlLtKzDKQldt4hTn80gKBU0TXY2h2L3qsBzQTzepAszpx8zdAKdzcLfxyDq+wgrN+DTd3g8AtUVq6iFvMUl6dwc3X/JsK0enGfmV9y0dpNzQzgWnF8fUOJDZeuUB56QqFuRcozD6PX8mhZL2SWwUulZULWKlwQq60dJHiwnl6xk4S754g0XuE1KFjZIYfJ941QqavXkUuggqdhxrtDXQa6wXLiP6O5Fbm6eprbLXMtEWThqlUdItpodbiaMdTTB57w5bZ/CamHj3J7MUzpNs70L0snlMh3dFDaeEVdDvBwORxigthm7Jm2FiWhgrCb55hpzAMk+XL3yMz1DxAxpdgdY6xcu05gghfMa+cJdYxGJ5npg+zfZyqb1AslKiWy6zdfKmuXbC6MY+bX0CL8EnN9NUTWEI3cHaEEvlamvVrzzfslxo6QWX1Em5uDr+8gpnsRlBFN+2tUtxIdOPmZinMfZe23H/HLSwwMjLC2traXbVet+pvutfzRH9/P47jtEwceZ6HaZp39Gl+UOsvOKjB7jcOPqjXCJqm8dnPfpYPfehD91Qyf+nSpR+KCX2lUuH8+fNcvHiRsbExTpw4Ucem30tTfEPTeLq7tYhupRTtps1gLIUmFSmhk/McFqol1jXFauBuRWY3Q9ZzqAb+VuGms01qmQjyrsN8ucBsucCqW63zoXKlZKaUh0p1+30rRVzTaTNMOk2bDsMmrZuYSuD6PnnPZaFSYqaYZ92tmde7VdZqBvbrnsOG55L1XfK+SyHwKAc+C065sUXyDsgF3r7UWc1UXxpg1kgtA2rx4aGRfoBq2toZCSEIIs1km0Azt2Zq7zg0gNm6QbxQbktqLqUUSrNAT6HHusHMoLQ4Uglk4BK4OYLqCn55Ab98C798i6CyiKwsIv0yXmEerzCHm5+tLTdxczO4uRmo3CYm14gZDl55hVYD1qSzgTD2btMzYu2kBo8T6xolqKxQXb2EX14l2X80cvtk/yMkew7jFWa3jOt3wkz2kOx/BOmsEjhZnPUbmMnByLHsjkkICk09TDbhkCFw1qgs7M/fSgjB+Pg4juPc9TV1aGiIbDZLodCYHlmtVutILqUUGxsbLSUSPfzwwxw5cqRhfTKZ5K1vfWsDebawsEA+n+eNb3wjQgje+9738g//8A8AfP3rX+d973sfAO973/u21u8XjuNsvQ+lVFUpNa+UuqiUek4p9Y9Kqb9TSv0poU/Ee4D/Vtv1wXqKP8AB7gKHDh3i3e9+N3/+541mzHeDWCxGX18fs7ONCXivNYaHh1lZWWlK6Lmuy8bMLNUmfjaBUuQ9J7J2KEu/aSSOjsBX4X8NoWEJDU0IsoG3pQhTvk+1VMaNVHKFlUb04BHG8coPg2SQodJ78wXlgJ6MeGMVUGGyclBdqVMWm/HddYNCullUUMEtzBE4RbzSAl4pJK30Jvde3QqPq6Rfp9wSQRkrE32v9CtraOb2+9OtFHbHBG4xS+Hmc1iG9v+z9+YxkuT3defnF2feV9330d3VPTM909M90z08xJWpFSx5tSaX8IrrNW3BloHFGoJliBJgCwKXMsAFLVCSLViABOgASAGSIO0uJUAwJa2OlSlxOD09B+fos7qrq+u+8864f/tHZB1ZGVWV2dNDDul6QKI7MyMjI7IiI775vu/7Hn59q1UJJgMSh0NoDiDe00oyWZuzKEJSXr5J8dGbbM/doLw6i3Cr5CcukR17jvz4JSprs2T72rdzZ+Em4+dfaHmsvP4QTW//HALfo2+ofR318iaa3v73vf/ON+kZaPcw3V5fYub5j5BIZZh9+5WW58xYgurWMn1Do5yZHGNgZILhM8/S25Nn4ulrFHIpGmu30WJJUr1jDJ9/gfp2OCIqFJVM/ySNndBiwbWqLeOsqpEgO34FNdHL2t2XEdoJIUhCIzXyHMXNdXYWb+JZYWPSre2QyEbXC7Xqfs2hxTIUJp6nvNiq4iqMX8Kp7vtWqYqC1yiixrLEesP6wixM0Nja9y8zc+MIv0rghOv3GlvoqSH8xjZSQnXpBkIIass3UBSF8+fPP/bvSNM0OTM5dOxU0S4xFYVdouw4Rf1BLC4uMjIS/R06iA9i/QWnNdi3A6ck13cQL7zwAlNTU3z1q199IuvLZrPE4/HHTsnoBI7jcOfOHd566y36+/t54YUXyOWiSYRkMkk+n2dxcTHy+W7wsYFRCkY7ESKlJK/HGImnGDDiaFKwXq8xVymyUKtwr7RDVugdn7B3ExR1KfBdlwQKpQOk1pZjHXt2kYHE9HxQNZKKiiLB9n12HJs1q8Fyo8aKVWPDblDxXeSBDkTd9wj8zs9dAZKyY3dnek/3Z8d64CGCAF0IdCH2OrB+k9SKWl833lwAUijdqbm0bogrF5TOY4Wj1FwSBakm8EWSSiMgkJLAKePbm0h7Hek7BM4O0m9wPCklieVPVv5A6OmRHGo3iD0KvlMhPfpc2+NC0UgMPEVy6GkQLtbWPbzagbEa6WNt3SM1tK8Ei/edIzV0Hq+2jNdoHzUQmkly+BJC8XGrB1VZErs4j57YJ6VVM4uRHWt6dB3z2QgVPTNOTK1BYGNtvoFbXeh4/yH020kkEo8tud8dW7xz504bUWZZVouPRLBrovwYgRonYWlpidHR/SJ/dHR0L1xkbW2NoaHw8x0cHGRtLVoRcBK+8IUvsLKystdJbCb56EIIrWlyupvqI6WUdZpii9N0n1N8r+Czn/0sf/RHf/TEgnsmJiZYXV3FsrpTob7fUBSFc+fOtfmG+b7PgwcPeO2110in01wbP5okCQDjiHOdHaECTigqHmEy8256syUDbBmgCQW/eRoRmkYsmcCTEjcI2DfgbJ5mjmyABci2cUMiySwBqMYRBvTxaEuDo8Qh6p5yu/U0GPjRBGLgtjdMDjwbWjAcgvRtYrmwTjBzk7j1KtXFN5G7qp4jJPnWzvyRtgWK0hqQ5FklEskIT6zqJvGYydbcG2w8eJ38xBWEPKjMEQhFxfdsdLV1O+xaifHpaBP8RqV9jG390V3Gz0TXOIMj4f7HEmn6R6Y4//xH6R0cZ+7W65FjjmfOnWd6MEWPXMFav42pCcaGexmfPkcqGSMeN+gbnyEej4G1QXH+9XBvFJXeycuUl/e9tUrLd0gPPQ1AZuRZfGGwMXudRjE0mK/vHH/NDYTB6p3r+E7rMVFdv09jZxnNiKhHA4fE8HMUJi+DX6e89PaeaXxzQ3EOqP/i+RFqq6EqzapU2Lz7DdRYBqGIPfJTT/aiqAHykC2EU15EBi5mfhoRK6C5m1jbsxRn/5R0Ok0mk+nunBgEewR4X15npEdSKkVP8ZzkmRqLxToOAlpcXGRsLCqM4b3h21F/wWkN9u3AKcn1HYQQgi9+8Yt86UtfOjbmuRucPXuWBw8edMC/ZvQAACAASURBVMSCd4ODxVAmk+HatWv09fWdKBOdnp5mcXERxzk5SfA46IrKPxw7C1JSMGKMxFL0G3FUCWv1KnPlkNRq+O0y2QflHbJCayNdhISMqtOjmuQUHcOX1BoWC6USs8Vt7pV2uLW9QfYIg9JdJBSVdACG7SJlQImAJafOvUoR5bgo7AisWDXMLr6WVd8NDeW7QMX32rgGQajMMoWCV2+g+gGmUNBCC1h8IcJYcXlUz7YVklDV1g26UnMJpWM1FwBadIEbicBFiQ2ClkaqKQI0Aq9OYG+Du0VCd9q8pPRE575x0i2d3AlsQmBDFyEKTvEBiYHQUN7MjZEauYSezOGU5kNz0mOOlcbmXdITL5EavkhgbeBWoy/eif6nMNO9uOX5aE8RGWCXl9Di/Zi5SRQFAuv41BzFyKLGC3i11sKq+uhP25KAjoNt2xQKhceW3AN73hSHFRmHlVzr6+v09e3/3X/wB3+Qixcvtt3++I//+LG2oxOIpq/d4+Bv//ZvuXz5Ml/84hcRQuSbST6ulNJrmpy2/HF3vSCEEONHrfMUp/hugmma/Pt//+/53Oc+90QU8IqicObMma58Zb5dKBQKCCHY2toiCAIWFha4fv06mqbx0ksvMTw8TEzV6IlQ4uxCSPAjrut2M/1ZSklchEmL9WA/usY71PSyZUDRd7H8/XFGIQQNaBrTH7gJODJpMbJmkJHkl5A2SoRHp3IEYSTdEmo84rruRxOYgVOMJMwCp4RvRKt3fLu4R2ZFbAFmbprq0tv4ditR5lZWI8fbArdOcvDpI7avTn7qJdDTaOkREkPP4lY3yQ3NtC1bWbmFHgsnMzbuv4pd3WHo6Y+RH38OtBh+AMneSYRi0D99uaUm3Jh/l1SufX8dq87Z5z6CEWslIeOJ1vs9QxOcfe4j2LaNqulY9QrrS3PcefPv2Fh+iOfYNCo7jI5Pc+780zx3fpxrTw3TI5fpLaTon3yGsbNPMTxUQLXXqcx/k52H1wmcGo3tR2ixNMnhsBmY7j9Dum+Cnfk32rbXqmyTHnmezbk3cCqt9UtteyH0VItAeuhpdpaO//7ryfYxzVR+gPLit5BCiazTChOXsMv7KdbJ3H7YgV1eBekT7z+PUw3VmKqRwkhlI+0ttOQARm4auziPVw1FEfb2PYp3/wQZ+ExNTbG8vNwxWa94q6jOPEiJCCziiTSzR6ixomwfDmN4eJhqtXpiUuJBu4jvtvoLTmuwbwdOSa73iD/90z/l/PnznD179rFmcwcGBvixH/sx/uN//I9PZHsMw2BkZOSJmb5HFUNDQ0Mdf7FVVWVqauqJFH2TqSw/PDzFWq3KXKXIYq0SFkkdYL5cIo9Gj2KQ9MCtNijW6syXisyWtpkrF1lr1JudxH34UnKvuE1O2VeD6ULQa8To1U0U32fbsdgIXMpq2K08iB2n0fWXbMuud1Vwr9v1jkYQVQQxoZBQVISAmFAxhILaDPD2AEdK9ESCQFVx5H7CYmhC391+WL7XVeJnqObq4tPqwptLSBcpkkg0pDCRIo5UEgQiQUCMAINAKgS+jwwcFOHhWxsE9ibS68Dnzisj9OhI8jZID5Fol+JHIXAqpI/xhzgIocYwc5OYmT5SI8/hW1tYW/ciPbQOvZJ4/3kS/edwi7MIBdRYu1LOyI6Q6D+PV18hcI8nkBQthtBiBH6jrYt4GFpqDCntkEQ8BN/extpsL0CPwm4Bdf78eW7fvv3YP1zHx8fZ3NxsIcoOF2e70dW7+Iu/+AveeeedttsnP/nJrt9/ZGSkRQF7UJY/MDDAysoKEMrq+/u7C0vYxV//9V/zuc99jl//9V8HuCmE+F0hxL8UQnyfEOKcEGJACGEKIeJCiB8QQnwJ+P+A73usNzzFKd4HvNca7BOf+ATFYpGXX3755IU7wJP0I33SOHfuHDdv3uSVV17BcRyuXr3K+Ph4ixp1It56HRNAQg3tFnwp0YCkqpFq3tLNmxAQU1QaMgy4OQiref/w1b0aeHhShuRZ08fHa2le7RJdeljjtNwCkEGYqoy6l7QskKC2G1ELiB7nDyy0ZLQdRtQIovSqqLGeiKVBM5ORTalk0myOXrZD0Yzmv3G0RB9mbhotMURt4wG11duRrwGI5Y9SsLRf88zcGFZpFae0iNcoYe0sUF74Fq5tkyy0pwFK32X47CXSveMgBJWNhyhOESE9PLtO4HuU1uZYufsK8ZhJqjDK8IUPkRs6i1BUBsbOopkJ+iafZfTCNRK5QbbWFhECPKnQO3aByWc+zNTFj6BqGmef/z5GZy4Tz/azvPCQd1//O2bfvcHUU5dJZgooqsL0+ed49sqH6espUFq6jeGX6PEfMT5UYCBnkNIdnJ15hkYnkJUl8B2s0mrbvrmVVRqbc/TNfIzG9jz1zWjVt5Yo4LlH1y5mpp0ANZIFtlYe0Civkht//ujXJnOkB86RG79Ebuw5ChOX2br/CsiAarVKsv8sZqb1uu419n2qzHQvtdV3W56P90yydfP/pbE5hxYvkOi/gFdvV+Hr2Smc8nKzNmy0hvzIAK+xjaqqnDt3ruOxRSEtBD4EdXx9CCU+TF//QKSivpP0692xxZPSHhcXF/dIru+2+gtOa7BvB05JrvcA3/f5iZ/4Cb72ta9x8+ZNfu/3fo+bN292vZ6f/Mmf5Gtf+9oT83IYHR1lc3PzPRmqSilZWVnh+vXrRxZDnWJgYIBGo0G5fLwXTyd4qX+E//XMM2hHFAwASElWNxg0E/TqJnog2apWub6yyJ2Ndeq+R5WAdmv2duhCUDBi1G2bPtUgpWhUXYfFeoXFRpX6Cd5TZdchdYIS7DAqnkvsuP07BE/KvY6JKgRxoZJSwsIzpYbm9boQIMBtphyVfW8vKalTvZUtuxtBFIpC0OXxEnTlzaWFf0EpkVI0CSwdKYzmv3pY+CKQMkCocaTfQHpVpFdGuiXwSuBXwA9H5PaGOaWDlujMB675AoyIGPGjYOpOm2H9UfCtNYxsOymmJ/uJ9Zwj3jODlugD6eKUF7C27kBQR08ery4Tqk5y6CKxwihedQm3Gl643coKMnCJ9YQdXsVIhZ1Pv45XP0maLTALZxCqaPp4NSAyRh0CVPT0GH5jGYKjydD66jcIOjSht20bwzBIp9PkcrnHHpXe9aY4SJQdHld89OhRR6bzj4OhoSEymQzf/OY3kVLyla98Za9Y+8QnPsGXv/xlAL785S8/VhEH4Tn+J37iJ/j6178OYXT1OeDzwO8A/zfwF8AcUAH+H+DZ5mNffy/7dopTPCk8iRpMCMEv//Iv83M/93NPTAH/pPxInyS2trZ4++23MU2Tvr4+zpw505ZwBpDVDYbNBBlVJ63qCEKz+JLnUvIcar5HzXOpeC5V36PSvNUDH/+YesgKApRD6msJuLLp4amq2I5D3fOpB5KWtBql6fwp3QO33cRlExHUEH4V4VdDhffudV8YSCUe3oSOkA5RP3sU5QhTfa+K0NpHyzQ92v4gcMvo6fZrtfRqbTYFqpnDyE7iNio49QaNnRVqa/coL7xObe0WvlUi0Rc9+gdHj1PaO/No8V2lkCDWM421s4RvVXAqa+jx/eag79QoPXyFgZmPkpt4gczwMyT7z5Ibv0J55Ta5gXGMWJLC+EXimV6yfcNtn9Xm7Cv0DY8z/+7LbCzew5cKiqKg6jGW77/F3LuvUNoKa4vi+jzTFy6yNHeLO9/6Brff/Dtu3vgb8B0e3n2L4lZrfTH79nWmzj9Hf2+BTDZNKmHy1KUX+Xs/+EOcn+hjcHwavzSPZ4WKn8GZlyjNv4oMPHyrSrLQSgRqsTS9Z66h4FPeXCKeja7x9ESWzfm3IkdJ99aVOER0CoE0cji1Ir5rs/nwTVSjVbGfGb1Iaugpiks3cRslio++RXHhLbYPKMmkW6da2iKW2z+Oes9cw9rZr2XSfVPIQyPCRmJf2Rf4sH3nbxBaa+2lpYaxtw8JDrw6KPqBu6GVRT6fxzTNzsbxdknsoBaGLhH6AO7s7LT5m3ZCcgEkEgkGBgaOFWx0GvzTLb4d9Rec1mDfDogTLsIfnCv0BxAvv/wyP//zP8+f/dmfAfDFL34RgJ/92Z/tel1f+9rX+M3f/E2+8pWvvCf54y62trZYXFzk0qXOVCC7kFKytbXFgwcPyGazTE1NYRhHJdx0jmq1yq1bt3jxxRefyP49qpb5ndl3qLkOKU0nqekQSKquw3q9hnNMuqEALhR62fTCLo2UsrkOA10RSBmqkMqOTeVAJ0cRgrF0hnqXEYWmopI1YvhdfJ2SqkZ/LImuhkatCqEs9nCPM5CSgHCMsMeME3Tx2caaaY3d/D1SitrV8gJIqdqRrzkYvLi7d8KvIPaK5bDYlUhs20bXddSDxJn0wNvpeHDRd+sQdDgaLEzsSneeUJ4X4J8wmrcLxeihuvLmyQuqMbRYD1ZpHUWLI30Ht7aOPIH4UYw0vm21dAABVDNNvGcKt7Z6rMpLKBrxgWfwG5sEzskEtZboQzFMvHqrJ6CeHCDwqogDdKrUcyA9FNkZER/vv0Zy5OMnLnfr1i1GRkbIZDL4vs/rr7/OxYsXicc7Gw89jPv37+9FW1+/fp2rV6/uHcv/6T/9J8bHx/dMSI/DV7/6Vf71v/7XbGxskMvleP755/euG5OTk5TLZRzHIZfL8ed//uc8/fTT3Lhxg3/+z/85jUaDf/AP/gH/+T//571xo09/+tN7JNsf/MEfUChE+8p0CiGEDpwFLgPPAGOEjO8K8CZwG9iQUq68pzc6Rbc4rcGOwZOswX7qp36Ks2fP8i/+xb94Itt279494vF4i7fLdwLlcpnZ2Vl0XefMmTPEYjGuX7/O888/f+TYkBP4vLyzfmQjsGCYIME+9HxMUUMiK+J6L5AkFS1MXCY8sBUR/qsiiB2qLbKq1iTFJNAca/SiUtcEeKU2YwgZOBAcvr6o+OgHrmcKQjWAAM+uEbgVhFBDO4TADW0J1HSkN2SAgddoJwDqXhLdbk8P1pOjNIrLqGYGp7qFU9onLYzMOPX1dtWWosfx7XqkLYBqpnCr++pnLV5AT/YAClKo1Nbu4NW22sgQs/c8pUOG5noiz9b6cttEgJ7IUtreJvBbm1DJ0SvMv7UfDJMZmGJpcX5PjScUlVR+gK3tEnajVQmfLgzi1IuMzVzG9iQbK0tsrDxi8uwFvJ37ZOMqjufj+IJ4IgGeRTyRYnDmGor0AQnSw7MbNNZukcgNkeodRtOMPZ+tPQhBdvQ5FFVFeg6VtVl8d79uSo9eorbcnnCfHX+etXvXUY0YpqETuHbbMgDp3mHs8jrx/CgilmN99kbL872Tz1NeCj/r3MQVNh/sJyEKRTvWiqFn8gq1lXfom/k+Sg+/ub9tI0/jFhdaGn/x3im8yr7VQ3biKrXVdymc/xhuowJeEYlAi+Vxa+2ezXp2DBF4KKpOZvq/JzX2EYQQeJ7H66+/zvPPP3/078DARnPDY9nTh0HZr7Wq1Sq3b9/mypUrewKJW7duMTo6Sjp98uSDlJLXX3+dmZmZyOU/+clP8ru/+7sMDh7fYP6g119wWoO9Xziapj7FiVhaWmoxvRsdHeWVV1455hVH44d/+If5tV/7Nb7+9a+3xZw+Dnp6elhcXGR7e7vjL2CpVGJ2dhbTNHn22Wcf+4dhFFKpFJlMhpWVFYaH2+XR3WI8leF/v/A8/9fsLd7cbJckH4aUkqSmk9YNDFWjbjmk/QBXE9RkwKZbZ5PjyY9ASkrVGnoqfnQbLQJ24JPUNMpHpDomVZ2kphF4Ho7ngWFQ810QUD+GrDsMy/MwjkgtiVxeBsQUtSsjekdKzK5ILoEfSExVOfDY7n+OWI+SAL91Fl8AMXN33w5scRd+VRB6PwVWhySXtFHjA/gRhexR0GOZSJJLSglCQ9FMhGKA0EAoxHufwXdqyN3RC+khfRcZOEjfRvoO+BZebYlYdoTKQueje4FTwUj14zt1pG+jp/oxM4M45UfYxeNMPQXxvhl8ewdn5y56aojA1Zqd84ilFR2zMIlTXiDw2j0U3NoaRmYC31pDCIGWHserL3fsVucGBkpxjnj/VRS9fQzlIBzH2esSHpTcX7p06bHI9cnJSV5//XV6esKu7cF1LC4u8rGPfayj9XzqU5/iU5/6VORzR3UqX3zxRd555522x3t6evjLv/zLjt63U0gpPcIi6uj5GEAIIU5NT0/xQcGTrME+//nP87GPfYxPfepTR4bpdIOpqSleffVVBgYGjkwTez9Rq9W4f/8+nudx7ty5lh+JZ8+e5d69ezz77LORrzUUlelEmtl6dHOj4XnoirJ3DRc0XbOkRFcU1L3mWXiq8KTElpJy80d9crfuOHAm0YRAFyqu46AbBlXfJ6EqoWpfaiACwqTFw3WUBCUJwSFbATUVQXL5KEgCBLKZ8iubq1O0DG7t0P4KFUUJEHoWFJ1atUIyEUcIiVBMpPRBaNi2jW07xEyDdCqOq+zvnPQ9AreBVVmjsfmQKN7aP6KJFLgNzOwodrF9ysO3q6RGr2LtLNDYWcCuPoCN8Lqup/pwK0cEULkVMiPPUF7aH3dz6zvkhmYoLt9pXbReYmjmKku3vtHyuKZIxp75CAvvfgOEIF0YZNipoMR7EXoMRQ/J00SqSLlcxkgVqOysoWkmibjOdm0Nq7SG7UlGh/sx7DWy7jzJ3vB1XqBhGBoInWR+FLe+gyjP4dhVAs/FzI/S0zdCxVkn2TdCceHttt3UE1nSA2fxXZudh9+K/CjqxfbaLtk7ydpsSEb5jkVy6ByV1TttywHEes/iYbKxeC/yeaU57pqbeIHNB9dbnpOBh5nuxUz1Ul5pv+xWS1soWgzftZASCpOXkVYRa7P9vYx4Cq8pmBKqjrUTHi/bd0LBT27qObRkP42N6Mu70BLg1XEqS2x+6yvYxXl6n/snaJrG1NQUd+/e5eLFi0gpW+soKRF+hQAVKUULwQXh776enp4WxdVhRfxx2B1bPEyUhW8dCjI6GRf8oNdfcFqDvV84Jbk+IBBC8Iu/+It85jOf4S//8i8jZeTdYmZmhrfeeourV68eO2ZYq9WYnZ0lCIIjGfMngenpaW7cuEFfX98TKfp6Ygn+t2eu8NrGCv91aZ7Z0jZp3WgqspSw0+h71ByXHafBuuWwzr7HjgAm0mkcrfORumLgcUEz2fK7M9JfqlfpN+MkdB2jOWbnBD5V38WSPpbbJLNUAc2u2UqjRn880TEJteVaDGtaS2LjSagFHjHRuTrLkQEGAhGhnRKE45K7Iwm72+0gMQiVcB1BqEi0pkvYSZChN5d/vEHl/uJuaFYbdDb+pmkGPvvF+rGrFgZCqBjZs9j1bexGHUUE6JoII8wDh+BQAIPQEri1FaQf3Sk8CK+2RGLgKeprt05cdhdufYv02GV8uxKazx9LbkGs5yzSr+NW9otqt7qCkRnDqay08ZJGdpzAr+GUj08zdMrzGPmzqCp49aWOlHdSSrTECEFtBb+xSn31OqmxHzj+fRynpeOYy+VIJBKPTa6rqsrMzAy3bt1q62S+X1L57xREeBI4KK4MDhdTp8XVKb5Xkcvl+Mmf/Em++MUv8gu/8AvveX2apjE5Ocn9+/e5cOHCE9jCzmDbNg8ePKBarXLmzJnIJmdvby+Li4vs7OyQz7ebYAOMxpKUPIcNZ/9aKZrNQoGg4fsYqopAIEUY++XJAMsPyGo6dnD0qUIRomkwv4+S75EGhK5Ra9RJxuLUPElapVm/KqBnwSuDPFR/KUZbbLSQDlJJQtDqHykIUM0MnnuIWPLraLE+PKs1eThwtlCMPPbOLAawa0cp1BhWeWWvWWMCsg52HSTJSGIq1jOFtdV+DfatInp6ELfS3rBVY9GhOfHeGerr92hsP2x7zq1uECtMYG1HeCKVlkkOXWx7PJHpoVHuwa62+jiVHr3OyFMfaSG6pFvDrdYYuXANp15kYzYklvP5QRzXYeX2fjNO1U3qdpVMOsf26kPsksLEzGUSqTTxZBIVSVaM4lTWMdM9xOMmXn1foaa4JfKDUziNMsJXUeMJvMoixUqoHpKHa3GhUpi6QmnxXbbnXgMgO3qxTb0G4FdX0VP9+M0RPYSCT6vxe6NeDv3UDqnpshMvsL38gMbWw7b17sK1a+SnX2Lj3jcin0/kR3Fq+/saywyQ7psk8B0qmwu4Vg3PtchPv4SCS63Ybr+Q6Jumvr5PwqWGLtLYaCXl1HgBa/t+9DYag8iNm8R690dj62vfAv4JUgb09fWxtrbGxoO/QbNuk57+R2ix3vC7UXtIUJ/HDxSElsSItddDExMTvPbaa/T19ZFIJHBdt6vff7tE2aNHj5icnNx7fLcUeRKTQR8UnNZgTx6nnlzvASMjIyws7MuYD5rTPQ5mZmb4+Mc/zm//9m8/ic0jHo/vFTNRsCyLd999l5s3bzI+Ps7ly5ffN4ILQNd1JiYmOoqG7RRCCF7sH+azlz/MP5p+iuVyhXvbW9zc3ODm1gb3izus1qvYEV4bEijZTtfphI/KRdQOTqxxRWU4nmQ0kSah6RiqStFzWXcabDgNSp6zF6MdBVcGKF2ezmyv8yS68D1k5+TT7nv4PlJKXMtG8QOM5tijKsIkxoC2ejMyYvxICAFaewz4kVCMjmd6BKAYXUiLpY2WivDYkICWQuh5arZGIFXwGwT2Np5dJrC2MZUaumiAXwcZvf/Sq5PoO9/59vgV9NTxnSupmCjpCWK951DNOPbOXRTdONL4FsDMTxIrTODVlvCt9pEQp7yAmZvcu68YaczCNF5jlcA5LiI9hJGdIGhsUK91YOIPoJpo8UG86sJefHlj/TUC7/jxxrZOI/sJr7Z9MpEYhWw2i2mabX49q6ur7+l8/0FDM6Y6aKb6+KfF1Ck+6HjSNdiP//iPc+PGDW7d6ryRcBwGBwepVCptvjTvB1zXZXZ2ljfeeINCocCLL754rIp/1zfsKGNnIQQXUrkwPVrVSak6AYKS51L0HKzAb1772+sHL5DoiCM9yQIpm8GJAlUIdKFgCIWq7yGlRI/FcJBYBOz4Hg3fJ5Ay9NvS0k0ldPOHslDDxphWQCox5MEkRjXaOyuygSY9VC36x7d0y22qcelbEI82f1cUFxHhRamoRyvPjVS0h6aMuObFCmco3v8mdmkJxYyu2cPRxWhYO48O1QOC6tKb5AdbQ9sUzSA39iy6rjHyzMcYPP8heiaewyquYeigawqllVn0WIrBcy+SSOXI5Xt46qW/z+QzH2bozHP0DE0weWaGnmyMmelRnrt4AaM2S1yWqK+8ja54OJV1hi98CFNxWgguAD07gr31gES6B+lZeI3WhmZl9S7pgWmSvROkB2dI5IfZfvAqvrOv2D9M3LXsY7KP1MhlEv0zeFKhuNQqpKluzJMo7NeAqpEgOfgUK7e/gVXeOLy6FjhWg53le0dOLew8ehOrvI5qJOg7+xK+VWLn4Q1KC28RNG0mhFDZmbtOEICeLKAc8tnSY63HeOC0hwJ5lo0e4Rlr5M+gWeGYo2zat+jJQXyriFPfYefWV7FLCwwrL6PVvwWBjVO8DYGDrM3iVB/hNrZQk+MoZnSCaJS/abfE1MTERFsQ0ObmJj09Pd9TJNdpDfbkcUpyvQdcvXqVe/fuMTc3h+M4/P7v/z6f+MQn3tM6P/e5z/Fbv/VbbG+3J409DiYnJ1laWsI5oCBxXZe7d+/yrW99i/7+fl588cUju3lPGkNDQ5TL5fel6PuBsSleGuyuwN1xbAy7O7PZuueRFe1Ku1iT1BpLpMmbMWwkq3aDZatG2XNYaFQxuzRiX7ZqR4VnR2LLtRBdnhfrgRdZjCqExvvC8wgsOxxTdFws20b6PvF4HEXTCDhZ5+RKeSyh1w4VKTqTNEPQZdKiA1p0dzQKinSbhFYWoedBTYZm906JwN4kodtNQ9sQmuITz093vP7A3sTMTXW0rAwczEyhzRBVS/QR77uAmZ9AVQFrBac0v9fldErzxApTCKW1iDcyw8R7z+A31tq8tA7D3rmPlhrG7DmLUCRu9WRTd0VPYmQn8OsrSL+BJu39HydHQI0NQCDxaod8TaRLffV69IsIk2CjFKuapnHmzBnu3r174vYehZ6eHhqNxl6ktmyGPXwnxpBOcYpThHjSNZiqqnzpS1/iZ3/2Z49N9eoUB1PC3q/fK77v8/DhQ27cuEEsFuPatWsMDAyc+OMvkUgc2wQF0ITC06k8FT80nT/s0bXpWG2J1BAqxBuBhxHRWIkLlbLvUQ08gmb4jS0DLBnsKbwOflYeMjS1J1SL+ShIoROyZEr4Lx4ICYGNwA8DZ5RYc8Q+QnUuXRQjomYIbBQjohaWfhjucgimaiHUiATGwIlMP/Tq6xjZo2rU6OPN2nmImRtFqAZaoodY4QzlR28SelMFJHuiaweruIAaa99HxUwR752m99xHSA1eQCgaQlGJ5UdRNYO+cx+iMP4s2cGzaJpGdeUmxfnXqC29iWHouOUV0oU+0oUB0vkCT3/4RyjkkhhBGelU2HrwKpt3/xZr4w7Dw4MU4i6FtEE8HgOnTGN7ESEE1Y05RODgVLeYuvL30Q29zftLTfQgGpuApLZ2m57pqxjJduJWUXXSfVNUVu9S3273T7OKK6QHzrau20iQn3yByspNSjtrbC+8g2dHW1kYqT5UI0Fu4gqOBxtzoZeq2yhTmHwh8jWKZuLaNeo7K2QGZyKXiWUHyY9fIjd8Hun7yMP7b6awiovIwMdz6mDkSPTvrys79jz19f26JtF3DrvU+n3W0wMUZ79ObaNV1WdkRrG39kcf7dIiemoI0QxVqD36K9zGIpW5P0Bp+hCn+58hsNag8QC7vIh0dlCTkwg9g3JE4ihAJpMhnU6zsLCAegzRexQURWFmZqYl7fF7TUl/ivcHpyTXe4Cmafzqr/4qP/RDP8RTTz3Fpz/9aZ555pn3tM50Os3P/MzP0CkgPgAAIABJREFU8IUvfOGJbKOqqkxPTzM7O4vv+8zNzXHjxg1SqRTXrl2jr6/v28qEv59FnxCCf3nxCv3xLlRAwHyjhul1V9DOlXZIKRrD8dQeqeU0Sa0lq0YpInrY342+7gK+lATHyP6jYHWh5lIJRww1ERq/mkrYUVVFaB7rA0LX0eIxXEAxDfRYDE9Vuv77WU0FWEcQAtRk567Lit7awT1p8WZikpQ0o8fjSCWJVFLNWwIpTCShF5UeyxPY2wT2ZtPL44TjxStHF9FHQDX0Y5VWB+Fb26SGn8XIjhHvu4CeHiBwS9jFB82ExOhPzSkvYGQGUfQUWrKfeP8MgbODW203yY2CnhpEUVV8uxia+p4AIzsJAvz6AZ9Mv4ZqRhdDUmho8WG82nJk9xqgvvoK/hHKsd1kxSj09PSgqirr68cTeUfBdV0GBwf3zluVSoVUKvU91UU8xSm+2/B+1GAf/ehH6e/v57/8l//yRLYxk8kQj8cf+9xzFKSULC0tcf16SPxfu3aN0dHRrhKwJycnWV5ebmmCHkZaN3gufbQirOK5+L6Pf4js8qRsSVMUUhIXKtUDZtuHFfGulFSaqm/h+zj18DrgI7F9nyAICABfTSHViDpPzyMRCOkhAguQoBWQItZSe0hhoBpZhNkDWgr0DOhZpGKgmdENMMtRaOPzpIcRkaYItCXr7W3iEQorp7SIoscPLdtHrHAWKTWs7RVqK3cpzV1vGdGTR1zvvfo2ib4ze/eNzBCpoYtI16K6+CbVR69gxGKgCLLjzxLP9KAogkQqjRFPUtucI/D21c9mqodETCebVOjp7yeXz+Ouv0N96VXihkTxysQMwfSVH2Do7Av0Do1QnL+B2yiha9DbP0DhzDUGz11haOZFBs9cJplM4pUX2br3t1g7yyAEsfw4emaURN9ZUukM/gFl0s7DG6QHzpIZeYb8xBUKUy8Sz49SWb3LzkK079beZ9lMldTMJPnJF/B8WLnzMoFrY8SOn2BxfR/L8Vi+/TJ2rVXpXlqdRdHa647syDPUt0OVlHZo/fH8CLmRp3HK62w/eJX61hJWOTw/aEYc1YijaDqF4TP4TUVXdWOO0vKtve9ZevR56uutitPD7wOgJ8LvrlPa9x8TegK3vk1LrSh97NIy0rNJDl/Cb6yjiAZID6GnQOiYqQHiqR7qa28Q+BZCjaH3vIDQEij68Y3j6elplpeXH7sxmMlkyGQye6T8o0ePGB8fP+FVp/hvHafpih9ABEHA93//9/NLv/RLXLzYPjvfLXzf34tCHRsbY2xsrKtC6P3ArVu3yOfzJ6ZiPA7ubG/yf77yX7s6eAuKhp5JIIRAAVK6QVzTMVUVVYQeVL6UuIGP1YzQHk1lqHY7TwhMJDPU/M6JKAGMJtN4XZBKw/FUmzeXuUteAQESN5B7iY8qgrSqdWWon1LU5ohi54gpCoZyMhm1d14K3DCWOHx079+QLzys3BEId3Nvj0LSqPn83n7JpreCJAg8pNW5qbzrugR2VLpTNISexdqJNiSNXr5Aff2gb4RAMdKoRipMgBICGbgEbo3AqSD0Ao2N7tRJemoILZbDqSwivc4M+BUtjpEd2/PdUmMFAr8RJk9FLa+nEEYGnKNTJvXUEL69P0KgmAWkZ3eU4qimZyic/5/bHi+VSqyurnL+fPT4p+u6vPHGG1y+fLnrQuvu3bv09fWxurpKPp9na2uLX/mVX+H3fu/3ulrPBxVCCFXKiAivU3yncVqDfQewtLTEj/zIj/BXf/VXRyYQdgPHcXjttde4du3aYykZDkJKycbGBnNzcxQKBSYnJ9+TonR9fZ2NjY0TycE3y1tsOvtK1kQz/VCI8H5c09CEunfAegToKDgyIKXpNAK/rTVkCoWYouIeOswNoRBX2pOf40Ihru6rmBW/gYKzN9LefBThto6nBb4b2gao8fD63zSklyKOWzuk/FGTBL4k8GrN61FAwzMxDR1FUXHrGwgtSa1WJKZYaPE+Glvt12FFT1PfeNj2uJ4aprYaPQ4b730at1Em8Bzc2jbW1hwAWjyPW42+nipGEiM9QH2jvdZQYxlkIEkUJqmuvN2WtKiaKRL9U6hmmtryuwTugeaS0DBzw9i2hwAa2/N7SiMzN0ws049qJLFqFRCCwHPx7BpOo4ztBBjJHEYsQWNr/oDvlMAsTFJZbd/WeO8kgdvASPfTWH237XmAZN80BC7V7WVkRA2dG3uO7floskvRDHITL7J+/1U8u3WkLzt8nspqdC1lpnpoNCw0EYRqqgj0n3uJ7bn9ZMXM8NNsP9o3w0/3TxJL91JaeJP8+CWq6w/wD22DUFR03SA3fpHq0tGEXXpwBllfIzPyDLWV8D3M7DCx3Ai1tVst3mGqmcG3K2FSKJAeOUvgu0izH2FFBXYJ0mOXwrFdUdtbl54ex4hlSOXHePToEYV8FrP/o+DXUPTOLW7m5+dZWlriwx/+8GM1CA8mZv/Gb/wGQ0ND/PiP/3jX6/kg4rQGe39wquT6AEJRFH7pl36Jf/fv/t17ksxLKVldXeXVV1+lUChgGAbj4+PfcYIL4MyZM8zNzeF16SHVCc4XevmR6X1Jr5SSmKpSMGMMJ1NMprOcyeaZTKQYECpDuknMNEnbPnE1HL8ruw5rjRqPqmXmKiUeVIrMV0ss16ts2xa273O/tENa6T4goO65XamgJGC74UVKATQEhlAwhUJcUUmoGklVI6XqpFWdjKYT+D5JRSWhqOhNIsqWAfXApx74WEGwR3BB2Cnt9kirB10os5qwggCvqegKDtx8GeDLAK958wkJOF/RCIQOe0ORuzP9RB7HUkkiCBC4CGkjZCO8BfXmrdF83Akl2OKIWOQI6Ed0eI+CdEuo8RP8s6QENYZiFlA0AzN/Dj0zhpboRWg60q/hNdZwqwu4lUd4tZVm4S3Br6AljpaI70EoxPLTmLlxAnsbp/QAI3lyIg1ArHAWoZstxvK+tY0W64n826upsfBIOobgAgg8m11ho5oYxq9vdkRwAfiVu9jlh22P27Z9bGqPrutMTk5y717nxOMuLMsiFotx9uxZPv/5z/P222+3pLp9t2O3uBJCaEJ0yVyf4hTfYxgZGeFHf/RH+dVf/dUnsj7DMBgZGTkyyatT7OzscOPGDTY3N7l06RLnzp17zyPTfX192LZNsVg8drmLqTx53SDZJJlKnsOOZ7Pt2ux4Do4fUPQcSs1bzfPCYB3fw4oguCCsSRoRfp2ODIgaM3QOjTIGapxAxMLruIg1b0boz3Xg2i60JBCAX2tJXBRtSY2EP9wVGSq3Aw+JRlxtoPhlCOr49jZebQGTCl4gkEJFj/DtFEIlMXgJIzOJkZ3CyIyjmlnc6gpavH0kMlY4S2NrntKDl6k8em2P4ALwGjvoyWjPo8Cp0X7KFui5KeKFKRK901RX320nuGIZEn2T2MUF6ms30ZM54n375uNID3vnEYahkOwdJzd2idzoM8TTeWR9ncbqO1QfvYJGg+rim9RX38XZeQjWNtlMClNXKT16o8VYHST29hw9U1cA0OMZkn1TxAefwi8vIRtbyPp6m6INID9xGbu4TGNniZ7Jy5GfRXHhLWKZ6NomM3IRx661EVwA5dVZVDM6uVlL9uLUiqSOGDkECJrJ6UI1yE++SGX9YcvzlfWHrM++St+5j1JaeDuS4OqdugzSxSqGynczO9SyjJ4epDD5ApqZxGnU8f2AxOCzZMZfxLeK1FbfbTPHj+XH9gguCBWMrj5wBMEF6bHLSN9CqE7LutzKI1RVxXE9gtQllmtDKIrSFcEF4XnQMAzW1jpvLh+EqqqMjY3xr/7Vv/qeG1c8rcHeH5ymK35AcfXqVcbHx/njP/7jI6NPj8PW1hb3798nk8lw+fJlTNPk9u3bj50y9qRhGAZjY2PMzc1x7ty5k1/QJT515gLzxR3ul3aoOjYVO7xFwg0fT+k6PYYKSucdhnK9DqbeVVdiw24wlcxS8dsLLCklSU0jpmgIKalWqxjJBJu2xaiWxkWGvhi7hd4RHFMZl2FV7SppseK7ZBQN0SEJGhAWqSbtHdfdfVGgqaIK1VcBkpqUJETn5pO+Gkd4btPS/jhIUOPIoNJRep9Aosb68BtLHW0HQQNbZjFFh0mOgG6m8RvrIHQUPRkqslDCZBrfQno1pFfD98KiR4314Wx0FswgAwczXcC3SsignSxWtBgyPohwSrjV1n10KouY+bPYO7OR69aSAyiagVNp97cAcCuLmLkpnGYKo9DT+CKGaq911DnxrW2M3DkCp4x3xHscBU/NsjP3Fwxc/LHm5xniJJIL2EsK2traoqenA4Lw0LoVReHjH/84v/Vbv8U/+2f/rKvt/qDirbfe4tKlS18GBoAisCyEeAQsAJvAvJTy4XdwE09xim87fvqnf5oPfehDfOYzn2FoaOjkF5yA0dFRXn31VYaHh4nHo0fZjkKlUmF2dhZFUXj66adJJruzZDgOuxYS7777LlevXj3yuqwpCueSWf52O/oHctV3Sao6rgz2lNO76nM1dMqKfF2Y2KwQsN91D4Cq75FW1RZ1uY/Ek7LF7D5QDPBB5YCyWNFBZJB+NTSpDyzQsuAdunZLDz01hltba01sDBpIJY4SNFof923UeB9+Y6O5Xw5edQHFyCGlhp7sRwgF37OxS48Qqkl9c7kleEaNZTGzY2jxHnynitfYRk8MUp6/AQhUI9kyorcLMzuEW4tuHjU2Z0kNX2oqqmzsnQXc4hxuERQ9TnLgAtXlt1GMBGZmEC2eRQZOi3+TYiQws0PoiUKznpD4bh1r5xFefQuEHqkms7bmyI49S2lhX7lkFRfRYhkU3SRw2+tux7boP/8hinM3cEut6iinuoVqJDASQ1ilFfRElnT/GYqP3txbpjj/OunBc5GKsGTv+N7o3y5yE5dZvftN4rno77EMfLJD59mau9Fy/BemrrLcTJXcnHsTwzRbRjh30ahuUZj+EKWVO3tJk4fRM/UCvmOhxVJ41n4Aj2YkyA6dobQQ7p9VWiU3ehG3qRqL50cwE1nq6/eoLu0rFH3HxvPqqPno33NCNWlsttZ3XmCQTMWJmk5OjVzGr68R6x0jcNoJb6ElUDLPMZpVeeONN6hUKl0Hldm2zcjISKgGawovusXg4CCapjE3N9eStvjdjNMa7P3DKVv4AYUQgv/wH/4Dv/ALv0C93tlYEYQjO6+99hrLy8tcvHiRCxcu7P3wO3PmDPPz8++LeupxMDIyws7OTktixpOCoWn8L089S9U5PsHwIKqui2ZFdPaOwXqjTo/a/Ym65FgkhUqPbtJnxOjRTZKKBhIqrsuG3WDdsagbGkXXwZUBK/VqV2mLm1a9Kw8wCZQadWQX6kFbhh4ZUkqE3M2+DbutPhIXids0l/WaajEfSaMbfy4EvprucG5HgtY5eSFwEFFGs0cglU60pSy1vLuUOJ4CWgah55EyQE+OIgML397Cq6/g1ZeaqYRFCFqPt8DawMxOdrw9vr1NcvDplse0RA/xnvMoqoZirSCC6POHU15AjbV6rQjNJNYzQ+CUTjSid8qLKHqGwBgEPHTZGfknUcIfFuUF/A7VW+HGqWipMTS/hOquU3z4Zy1PH+fJtbcKIZiZmeH+/ftdnQellHvKwX/8j/8xruuysrJywqs+uNhVCP/d3/0dn/nMZwD+B8Kv7zDwPwL/B/A7wF8DX4BQTv+d2NZTnOI7gVgsxuc//3k+97nPPRH/UEVROHfuXFcBGI1Gg3feeYe7d+8yPT3NpUuXnijBtYtkMkk+n2dp6fiGT1rVKejRjQQr8HGkjxGlsJYBigzajOhNoZBUVIRs7bjHhYImRGTtVg/8Q48LAsVECq3V13I3gVF6IBSEngElAYoJWhqh58LrtHRRI0zlj1I7G/ECaqxVVRU4RRQjjbV9n8bWPZxS2PyRvk28Z7JlWd8q0di8TXX5XWort7FLm1SX39r9pIj3tRqk78Kzyyh6+99eTw8S75sB6SE9i/raLXynSaIIhVh+FCOVI9E3Rm7qBXyniF18iFNeQtFjpEeex0j14je2qK++hVOep75xh9rqu6GaLPBBBqi6gRY/Qs3u1trqIs8q0zPxXOtyQiE/eQW/OEt1+SaqEX0s+06dWG6YwuQLCGQLwbULMxnteRq4Vsv9zPAF1mbDUcJGcYVkb7T6Z/PBjRYVWHrwHKt3X91fr++RHXmq5TXpgXNkx56nvL6EVS1iV6JJyHh2kOLSLSrbK2ipfYuWRH6YZL6Pykrr+GptawGruELP9IdIZPqpr7eTeYqi0Nh6SOA5xPufQtFbUxbjfWfbyFLFd7G227/jycFn8OtLxPrORBJcZu4cSnwkDClokuJ37tzpetLItm0SiQRTU1PvKQjoF3/xF3n99dc/EFNJj4vTGuzbg+/eI+S/AQwODvJP/+k/5Vd+5VdOXLZWq/HWW2/x4MEDZmZmePbZZ0kkWk96uq7vqac+CNj9wXkwMeNJYiKT49MXujOhfVCrYLjdpS3Ol4rHSiJ3/SsGzDhDZoKcqrNp1QlkwLrdYNWqs243KEekFx2EFfhYntsxceXIAMePTk48EqZBcLiR21RkqYSjklathvS85slDUvbdkMgi2CO0jura7sJDYgVBx9smhUKgdFjcCwUpOvdRUSKivo9cFh/jwGiCFDrspS6mkShowiawt/CtdQKniBDBscRY++YHYYHeIbz6EvG+GczsBLHCNNKt4pQftsjUIyH9liLTzJ9B1RPN0cST/y4BOjVHQwu2UejsO6PF+1G1JE7pIYFbQ4tFx6YfhhrrR9ESeNV91Ze38zbWzp29+50ouQBM02R0dJQHDzpTzPm+31ZMDQ8P84d/+Icnjvh8ULFbYH31q1/dHXf6UeB/An4E+AhwBfgw8A+BX26+7NQf6hT/TeGTn/wkW1tbvPJKtDqjWxQKYVNha2vr2OUcx+HOnTu8/fbbDA4OcuXKFbLZzoNMHgdTU1MsLCwca0IvhOCFbB+xQ76aplDIaga6UNBQSClaaJ2g6iQUlZLnUvE83MBHEHpzxoSCLQNqgU8l8DiYr2PL/VpCR3DwSR+Jc/jHtYBgdzxRaM2bEprKq+mQ3EJBGDmEmkCRFiKoocgGQghs28MO8gijB8XsRTF6ENJGT7cbW0tnByPRPjpoHGEor8dz7esIPOK907t3WnfliEkCu7hIYuDA1INQSY1cwq1t0Ni4S33tNnZpmUTfOQoz30924nnMbB+BvU1j8w7St2ls3CFeGCcx+DSpoWfRYinq6zfx7f1mU+BZLQl+u/DtSltDbReqkaD3zFW0WBo9kcdI94ejck6Noaf/HoXJyxSmXiDeM0FlsUlY+RaZoXPE84dGPYUgN34Jv7GFoqotqqeDaGwvtr0uURhDqDq95/87UsPPYOTG2Fy43TKqGc8ORK4PIFkYBQSx/qfZfHS3Le0xaDbG9GSO7OglthZusXH/Br5rsfnwWxQmr7StUwJaPINn16lszLO98C7x/Ci50afBq9PYaSed9ESOVH4wNIOvt9cY+ckXqK6E/q2eXaOxs4yZ3z9WJWF65WH4Vgm7HI4KGpnJ5r/DSLeElhwmsNsbm1q8j1h6CCWxv/5kMklPTw8LC92p8C3LwjRN+vrCum9jY6Or1+8im82SSqX47Gc/+76l1r7fOK3Bvj04Jbk+4Pg3/+bf8Cd/8idHnkwsy+LmzZvcvHmT0dFRLl++fKyE9P1UTz0Ocrkcpmk+9snuOLiuy3lfYUzvnMSQQLHeQHRx4qx7LskDyX4agl7dZNhM0KfHMBCUbIvFWoVHtTIbdgNfSh5US2TU7vw01u0Glut2NI4HsOVYuF4E0SUl0vNwGw2E62FIMABdQs11cXyPIAj9sVz2I74b0kdNxPFUpVmIhoRWJfCOTPk5Cm6zkO0UgWIg6fDz0rIdb43AQ4m1Fz4ykPiBEqYsKglQUqCmQKgIsxeJivTqBNZmk9DajjRjl34DMxMd8x0F6dWJF86cvKDQURODqIlhZODgO0XcSoejl0241RXMngsYuQnc6iJBh2b0tiiAdIkFG2iJk8efhRpDS47gVlfw7f2izSk/QmjRXhjhCzW01Fj4+brtqYrlB3+EU7ofrstxOiK5AIaGhqjX6x2RVLZtt5lPb2xs8G//7b/lZ/5/9t49SLLzLPP8fd+55T3rfu+6dFXfJFndrVa3xMhgwjD2yDY2LDGwsIFtIhxBLKwdMYQHJC7GZm0wGIg1OyaCCc/aZmaWtQk8BseOCVkbeDCMhKTulkS3ultdVV1dVV33qqy857l++8fJyqqszKrKlnVpM/VEZLR08twyK/OcN5/3eZ/n4x9v6Xj3GrZGMiYnJ/nBH/xBlFLfUUqVlVJ5pdSaUmpGKfWSUur/VUpdgm3PiEMc4n8USCn5oz/6I37t134N37+75tdeOH78OJOTk01VEJ7nMTU1xaVLl0ilUpw/f56urq43JcVV13XGxsaYmpradz1DSh6opi1GpU5aN7FVQNZz2HBtCr6DpwJynkPOd0Pvzq1jCIki9Oas7LqclAKvoYHnVGsEKSV+xQY3bK45qrFBFgiNsB0XsO3hSTi6qLzQ7kDoYLSh9DaUCt9TISARlaTTETTDAnwQCmkk0IwowkiGnpmRbmSkC6SFcvPs/vnk2yuYqUZvLs9eJdp5tGG5ZsYalgFUNm4R72venHVy4aiokegi0jYYGpTvIHAEAjOZopKZQo+1Y7XX1x1Gohsz2YPwS2FScqW5kjpMT27iiZarry+EZpIYPE1p5QbFhRcRQuKWMjj5FSqZOUqrU+TnX6SwfJPc3GXczdt12+fvvIxhWUjdItl/go6xh4m19ZObf5nyxhybty8SSTcPp7Lzq3Sf/CHSw2exOsZwA8HG4hRLN58jvzbL2q3L5JanCLz6mmxt6jninc1T+YTUiXaPszn7UlMLiPW5V+g+8Xacss3q9MVmO2hY1DV+gezCjbplkY4j4dii3UjgdR49j19aoZyZI3P7IuWNeYRm0D52nmT/SfRomvz85dr65fVZyuu3axMYSgmIDyLc+r+t2TZEZeM2fnkTKz1G5tW/R5oJAk+hRdK4xQzSrFf2SzONlRxERroRsr72HhkZYWVl5a4mjRzHqantjx8//pp9mYvFIr29vbS1tfG1r33trre/F3BYg705OCS57nFYlsWnP/1pfv3Xf73upu66Ljdv3uTFF1+kq6uLhx9+uNYl3A9b6qnvRSr6emNiYoKpqanXrYj0fZ9bt27xwgsvkEom+bV3/EvSVutE17pjI4vbKUJGNdEnbZh0mhF6IjH6o3EGowmGYgmOxJJUbJc+LUJC6lQ8l8VykdvFHAvlAqV9khSn85skpV73RVRKoSGISo2EppPWDXTbJRpAQtMpug7LhTyu4yJ8BYFC7HwohVQgFWgKNp0K2UoZ5fu4nkfJsSl6LiUV4OgaBQIynkPGddj0HPK+y6pToeR7BK0qrYB8i6oxVXUcF4TFru37dSb0ux/+joejxapjCVsPA4SB40GhZFeXVQlHrRMVOm+h0FHCrBJWkTBSXEZrDyEtlJbE9TTKFQ/P80F5SGWDVwA3i3IzKCcDbgbdiKD8yr6vs+41e3mE1roPi/KLDUWFUgpptqHHh5BmJ75bxsnO4mRn8CvrWG13Z4Qu9Chmehg3e7uuk7sffCwCoxNLbSBU2OW0M7fQzObXHqVUOK7p+7i52WYvFKk3V+hp0V6kHqlTbzW8BuWTnfwL7Mx1XNdF11tTwG1J7m/evHngdWfLdH7nayqXy3z4wx9mdXWVp59+uqVj3kvYSnj70R/9UVZWVhBCtMYOHuIQ/4Ph/vvv55FHHuE//af/9LrsLxqN0tXVxfz8thIlCAJmZ2d5/vnnMU2TCxcu0N/f/6aQWzvR29tLqVQil9v/ftBpRhiNJCkHHtldJELB99h0bawmo0QC9mxT+dWaQCqFKQQGAqUUuWpdYUQiSMMgpHK2198JT0abNLcU6CkUAoFCKA8hI5SCNjyRQlUV2EpLoZkpjGgnupFA1zV0XcNK9KFUQOBsEDgZwEPoEaKdJ4i0H0eP9YV1hwrQLYPd5JDyKmiRxsurnb1db/Re2yBAiOZ1lFtaJzn6KCCoZOrvp9KMEx+YwMktoEdS6FYUK6pj9tyHHk1iJjpRboHK2g288gb25i1iPSebHsfJzpM++gPE+x8g3nc/8b4HiPc9gJkaoH3iB4n1HCfSfgSpm7UkQBX4JPsaX0/glol0jTdVs8t4D5qZID1wkuLyq2zevkglW28DEOtoJA4B0kdOk5m/xvLNfyS7eAPf2a7H8stTSH3vW1q0rb6pacbbaR89x9LUS2QX91Z4W4lO8uvLuJXGhhvA6q0X6Rw7B0Ak1U3n2MOsTb3QsJ5dzLB441msZA+iqorUzBido6fJz1+upVgCRNJ9VfJwM0zT3tXs2xpJLOY2UUphdoxCfqbhmPqO4KT1a3+LCjziffdRWr5G4EncwlJdzSmMOFbyCAEWMt7YdJVScuLECa5fv976RMYO24etILTJyea+sPthbm6O4eFh/uiP/ojf+Z3fYW1t/6CjexGHNdibA3HAh/NQGncPQCnFj/3Yj/HRj36Us2fP8sUvfpELFy4wMjJCf3//a5pLvnLlCr29vTXZ6FuN2dlZXNdlfLwFBcseCIKAhYUF5ubmGBgYYGhoqHYhuba+ylevvYxTJVRUlTSpkShBNd0vCB+O7zEcS1AwZctfgqiuM9LeQb5JB2g3NCGICx1dCFQQYHs+upREdB1HqNo+PN8nVyoRBArT0NGkRNc0dCmRUqJLyVgijatAHVAPmwja0IgmWvf0SGoGEV1vudg2hCAmtNr6Sink1n8TFqZ+E81XSuroLX6OJQqjRXN53E2E2iNwYBeyuTIxmWlpXQiJZt9ufX1htGFnW7+hS7OL8sYUWiTs5LvldQKneXEVHkDKGv91AAAgAElEQVQDDIIDCCuFxEoP4+TmUH743pjpUbzS/h5Tnt6FofK1bXZCi7SDcBE7DHal1YZAxysdnKRjJAfxK1U1pzDQoj34pdZUaa7eS0QWKPjtDN3/E0izdTPUrfGc/a47CwsL+L5fS1N0HId3vetdXLx4kTt37vAf/sN/4BOf+ETLx7yX8M1vfpNPfepTXLx48TPAXwCLQBmwAVd9v84C/PPB4ft/DyCTyfBDP/RDPPXUU6/L2KDv+zz33HM89NBDbGxscPv2bXp6ehgeHm6ZqH+jkM/nuX79Og8//PC+931fBTybWWkgubbQbUYo7xr1NoREoTC1+ppCKUVUSvK+F3qTVp8yZJgirSMwtfr3RUOQ0HS0nSb0KLTAQTZJTQx8D02V65YpNPCzDaE2CglCR9mrtfXc8jq+7xN4FZRvo0c60Yw4ysuhtATZzU10iuAHeFv3YKUIvDJ+JYtnezi5hfrXYKUord5uGFkE0GK9+JUc0oigGTGkblLJzKKCgEjbEOX1SZIjD0MQIKSGb+eQhoURS1U9wbYvHbZKIZvcTyMdRyksXm1YHu0+SWV9GrecR/m7/74CzWrDzi40bKdZCcz2o2RmGlVOycEHWZ+5iJAayb6TCCnJL2wfOz38EOvTzzdsZ6V68Bwbd8fYXnr4DMuTz4NSxDrHyC031lWpgVNszr/SsBygbfAUueUpUv3HEZrJ6q2X8ezws5Hqm6Cw0qhmVECq9xiZhRuke0YorjdvvkXSPXQMnWB18vk6smoL7Ufur3lw9U6cR0hJUNlEkwp7F8GX6DuBm1vEt4toRgyh9m6sGukjBJUNoulOvNJG3XPSiBF4DmqHab7VPozyws9pauQ8bv426bFHQAh8PyDe1oNXXiM28mPokebJngA3b94kFosxODi45zoQ/j67dOkSDz/8cG2ZUoqXX36ZI0eOtCTS2MJTTz3FM888wx/8wR/w13/912iaxnvf+96Wt7+XcFiDvbE4JLm+T3D16lV+7ud+jmw2y0/+5E/y5JNPtjya0wyVSoUXX3yRCxcu3BPmfUEQ8Pzzz/Pggw/edfKQUorV1VWmp6fp7OxkdHS0aaz29OYGv/0Pf0umUm6yl+Z4sLuXDeW2TPJENJ2Jjk42AxdFlfSROpoSBEFAyXXJOhWyTniz8X0fqUJPCy0A3/MoOQ65SoWVQoHNcincj6Yx3t6BrW2HXgvA0nUimo6paZhaSBTpmqQ/lWawvR1T1wiqpJ3SNKSUDMeSKCH2fE2q2iEVhOskNZ2IpiGlBtV+aHjuHpVKhXg8UUc4aQhMKWsm860ireloLSbn6spDV63IpAU4Sy0RYkqBV9kEv0X5tRankr0LfzstgpO/w36XVaFFqrHMEt+rUF6falJk7g0zeYTKevNxE6UUvtGFpkoN3UAAs30Mr9BYuAaY6LFOggNIMKttDL+yhEJiJgZxcrNNi/dm0CIdBF4BI95P4OYI3IPHqaWZQrNSBHbob+Mrg7bxH28a574XlFJcunSJ48eP7znmPT09TTKZrDUEbt26xW/8xm/w13/91y0f515EEAS1H9TVQmoWmAYWgDvVxxrw54eF1luGw/f9HsGf/umf8sorr/C7v/u73/O+lFJMTU0xPz9Pf38/Y2Njrylp7I3CjRs3SCaTByZxr9plns82t5qIazoV3yOmmw21RtowcYJwDNEIo2oo7xi7S2kGUgi86sffEJK4CJt7O/cVk1qDPxgqQNtBBijCerdSqZCICAxDq96Xql8toYO3CcJs8MIMx/YCBKCUC8IgcMsEbjhmphnxam6kIFCQzxdIxXUqFZvK+lWkHgtHJaVEiJA4k1KiEPiug1vaQDOi5Jdm8V2FEALfLeOV1tAiaTQjTnntVdqO/SDKDwicdYSRRkiBnZnFSg+iWQlAoTyX8tr1pn8LYaaxM/ONdZAQoMXwdqQ2xnruo7gYJiVaHUcpVr2fdiLW9wC5mUZCKvxjxakU8qHnZyRJJNWLNCJIzSSQGvk7V/GaKKGiHcPkV+rrKaGZJHpPII0YTjlbHSeFlcnnaut0jD3Mys1Gz7yOkbOs3brUsFwzoqQG7yOzMN2QwriFREc/pc36FNGu8fMs3XgWCMmp9ZnGfUfb+pGaSaKjl/VbjUSfkJJU95E6gsxKdNI9cAQ7u4jvbNee6aEHKS3X+4klesbQI0kKC+HfRCmFnhokkuyitDKJ7xTpnDhPeaX+cxDve6CmuKvt/+gjlFfDMcrk0H14pTWkmSCS7kGPdWLGYhiRJJGhH2v6Hm3B930uXrzIgw8+2GDrsBPlcpnJyUne9ra31S2vVCq8/PLLnDt3riZKOAhf/OIX0TSNj33sYy2tf6/isAZ743FIct3jUErx9a9/nd/5nd/Bsiw+8IEP8NGPfvR12ffMzAxKKcbGWvcLeiOxsbHB7OwsZ86caXmbTCbD5OQk8Xico0eP7nuRBSg4Nv9tdobvzt0iV6mEZE61aBLUC82VUhQKBXra2zF0HV3XMTQNJQSgCFS4Tt1oXRDgBT4l18VzPRzHoey55G0bx3Hwg4CkYdIWi5GxQyLrbr5kUanx4MAgmcCj6O1Pvo2l2kgm46BLOqwIaSuCrmlIESrGOqwoSort86++pmbm922GSdq0wkKtBcSkhiHkXY1bSLaL21ZgBsXWDM8DB+G1prgKMPALtw9esQrX9fDtjYNXrELJGG5he8xAmmmEFkEFPr69ie/UJxRKs5vKxt3IuQVCS+CX642NtVgXAonbhMTaPlYy/ALs8BUT0T6Em2vZqyvaeRLfXse3W0tarB1Hj2GmR3Fzrb1WMzWK72zU0inLfoJN7TT3P3jhro4Lob/DtWvXeOihh5oS/teuXWNoaKhGgv3d3/0d//W//le+8IUv7Lvfv/iLv+CTn/wk165d47nnnqt1ML/97W/zxBNP1PwpPve5z/HOd74TgIsXL/LhD3+YcrnMe97zHj7/+c8jhGBjY4Of/umfZmZmhtHRUb72ta/R3t56KmgzBEHAM888w/r6Oh/4wAf+DXAKGAMGgW6gC8grpd5Yx+tD7IfDGuwege/7vP3tb+cLX/gCJ082H/NqBdlslsnJSUzTpFKpcPz48TfcVP5u4bouL7zwAg8//HDThuFOzBRz3ChuYgoNXQokYU1RCXxKvke3FcVHoQlBRGpIJLoU5DyXpGHWkVs7Edd0vCBACoEuJKDo0C1EoAhUgGYYaAhSWqPSXAY2oomaCwT4TdTQSjRNIVYAwkT4m7tWN0K1l5Oh6VfUaEf5ZQickABzyygU0khhZ2cRRpTAKUBggzCqjUUPp7hJ4Dt4lQKeZ2NE0ii/seGjxwZqRFTdYZPDlJabq5dc2Ymw19HNGEIzkUYUECgV4ObXEZoOSlHZ2CaaIh1HKSy90tCs0qPtgL5rZFJCrIdoqhvNilNYehUnX08iRbvGCYQkv1CfJriFRN/9lDYXiLQN4fseucVX8Z0SQtPx/aDBCB7C5MLi5krT59qHz7A+s+1f1Tb0AJurC5Qyi/RMPMzq1MWmNWrv8UdY3UGkxTuGyG8s1vl79Z/4AVanttdJD56iuH4Hp5Sl48gpAqdIKVNfb/Ucu8DGrfrxxe7xh9GEQjMsykvh37Rt+GxIZO163+PdR/EDhZe9jdJjyHgvfqZ+vLLr2KOUlrcVckopzEQvTm67SSl0CyvVEY6Sdowg2G7899z/w7iVHLG2AXxzhGjXroTMJtjY2GB+fp63ve1te9b8m5ubrKyscPx4Y6jB/Pw85XKZY8eajO82wSc/+Une8Y538P73v3/Pde71+gsOa7A3A4ck1z2M7373uzz55JM8+OCD/OZv/iaxWIy3v/3tfOtb37oraedeCIKA5557jjNnzhxIDr1ZePnllxkYGKCra295LEChUODmzZtIKZmYmLjrWG2lFNfXVvjzf3qRb0/eaP5BV4qormNJjaQVwdAkupCkLIsAhfIVFcemUCmTiESZy26St22ylTJuC/5iCcviwYEhZgs5yp6HISXtkShxw8TSQxm+ChS251J0HLKVMhulIr5SPDp6lAW7TGc0TtKyiBoGugxHK23fJV8uU/B9kpbFiYF+Sqr+fCKaRqcVpc2KEjdNDF0LrVr3IZiiUqMnEt8z/afZ+uZdEF1KKSwhSWh62GE8AK2PLQpwlhEtXM4U4JVbV3MpLYGdbS2lD6EjjDSevUngVfDKa9VO8d7QrHbK6zOt7b8KIzGEvRGekzDiGNFO7M1btHQ5jw0h3VUQJkaiDzffxEerKSRWegSvsoEKHESLaYsgMdMjeKUlVOCix3rwK3v7Kwg9ihHrwa9sF89W52mWK8Osrq3zyCOPtHjcety6dQspJSMjjfHily9f5oEHHqj92PvP//k/k8vl+JVf+ZV993nt2jWklPzCL/wCf/AHf1Arsi5fvkxvby8DAwNcuXKFd7/73dy5E46SXLhwgT/+4z/mkUce4T3veQ8f+9jHePzxx/mVX/kVOjo6eOKJJ/jsZz9LJpPh937v917Ta90DdV8jIUQEaANSSql7x8Dxfzwc1mD3EL773e/y6U9/mq9//et37ZdVLBZrhvMTExMkk0kKhQLXrl07cDTwrcDCwgL5fJ4TJ07su54fBPz3jSU23Oa2AL1WDEcFDapuAcR0o+FeryMwhKDi+1QCnzbDwiZAEhJfMamD72NIDUPXQ8/Uap2x9R76vo9OqXnDLHBANY7goQJE0DgOpgCUB8qtWgJo1bP3q0TXRm0fSlpU7IB4NFSkBOgoNwe7xiEDYeGXlqr72fG+aEnc0grKK4HQkGY75cwsXqVeLSfNFHq0B7eYIfBsvNIagVtEGnHcYo7Aq59W0OM9gKSwPFXz0qx7PtqFnWk+fme1j1JcaiTOjFgnlc0llBFHWV3I4nzNykCPdWC1HWk6tmilB8ivNdYVSugk+++jmFmhuN7YaIz3HSd7pzk5tpeaq334NOszLyJ1i9TAfSxcf7bu+VTvGIXVJsfqHCIIPBIdg5TLFcob87il+kZpum+c4toMic4jWMlu1m5dZqcveMfQKYpr0zUlVqxjEOUWccvbdhKpvmOU1rbrx+6BUWIdR7YTKJtAGAkS3aHyyys3NhM7Jx6lvBKSXNJKEG0fbVBxpYbPUcmEiv/00UdwNreJzYGH3ofyHYRmILr/FZqmtaSwunbtGh0dHfT2Nk+vXFpawrbtpjWWUorLly8zMTFBKpVqsnU9PvKRj/Dkk0/uK4j4Pqu/4LAGe0Pw1g7/H2JflEolvvzlLzMxMVFb9su//Mt85jOf4Q//8A+/5/1vEUQ3b95skJC+VTh+/DgvvvgiHR0dTVUV5XKZ6elpyuUyExMTtLU1xjO3AiEEp7p7+e13vpsPnT7Hf7s1yZXFBSbXVtkslyh7LnHdIC6hkstSjNgUHZuVfJ7lfK6p0eKx7h762zso2TaWqZEwLaK6gaVXxwirr0cpFfp+eR7za6u4nsdQWzvX11fJ5PfxXNqBZ2emeezoBNPFPEvFvbcJ/IBX5xc4MzSEMjXWKmFkdsX3uVMqcKcUyu6PJtuIRSOYUsOS4ViALgSaEFW7VkUArFSKdFsxpHawoqsc+AgJBmEBumWoL4SoqeYUVBVkoU9XRQVYVbLrYAgCYaEpJ5w13PM3gkLpHQhv/9h2quekRbtbVnMJvwgyAk2KY6FFoarSKuZWMEUFWMVXEDgtGr3bGczkAE5+bwXWbriFefR4L1KP4uRmsTdbJOEASvMYnSfxyystE1xapAMhJHZ1dNNqH9/XLH4LerwflFOnbAvcAmgRaGLqbyQGUX55m+ASGvGhH8Vqv4/o4iKwzsbGxmtqAIyMjHDp0iW6u7uJxepTr3Yb2m91LA/CqVOnmi4/e/Zs7b/vv/9+yuUytm2zsbFBLpfj0UcfBeCDH/wg3/jGN3j88cf5q7/6K77zne8A8KEPfYgf/uEffl2LLCFEN9ALFIFlpVQJWKo+DnGIQwBvf/vb6ezs5Fvf+hbvec97WtqmUqkwPT1NsVhkYmKiTgGQSCRIpVIsLi4eOBr4ZqO/v587d+5QKBRIJPZOwNWkZCKR5vnMSlNGtuA5RDW9oe0hCQktlKISbG9Z3qVecQIfo1qHlD2PEh6WkLi5HD3tHbhSISWYQiPwPMqlEtFoFCVNaKbmkhY0WAAoEFrNnL7xRaZA+YigGK6Lgq3KyOoGv1ArP7YILgCBjzA7CJz6xo1UDiI2iLfbJ8vPY0SSOEUblE9gr2HFYlipB3HtHEJoqMDDr6zgl++gmR04ubBW0aJpNDOFmRrEKeYJnAJuYZlIxziVzVsQeJhdx3BXmxBW8b1JLjszg5nsxcmH3ppCj2Cl+vCERSA7EetXEHa27l3zShv4dh49ksLbleBoZxeIdx2luIPcMaJtuCLC5uxl0sPnmpJcZmRvr82dxNFOlDJ3iHeNYFfsBoILIJrubSC5IqluIm19eHaZ5Zt7jGQC2ZUZhh74YRau/C2sNtZKG/PX6Bw6Tn5lmu6j58gtXcd36slHbVcdHekYJVB7e/oqpUj0juNk5/EqjcmMACoIiA88hFdYppKZpVB6qck6VUJaSLzSdsNQ6CZS0xG6gS8SmKaJ4zgIIQ60tZmYmODy5ct0dHQ0VX/ul34thODkyZNcvXqVc+fOHXisubm5AyeQvp/qLziswd4oHJJc9zDe/e53Nyz74Ac/yBe/+EWuXr3K/fc3jxm+G3R1dTE3N8fm5uZrJoxeT0QiEXp7e7l9+3bdRcxxHGZmZshkMhw9evR1jdUe7+xivDNUjgVKMb2+yuf/9mn+/OJz+E1ivrfQn0zSFYuH3UgVULJtbs7cwg08Uul2JjOtX5sWs1nuHzrC9Gbro2//MD3JY+PHmC7sTZhk7QpZu0LRcRgf7CNqmHRHYlQCj3W7UnsPp/Ob3K9345oC1w/YT4izZpcZT7QhNa3m3eV5LoYW+k3Uxj5FmJ6IAEuGSjEP1RATvhsF32tZARYIC+lX1VxKVjutkoptY5ihUT+A54OuoBURmsALUxubdDwboTATfTi52wgjGRbKno1nZ1Dl7c6fueO4RrQbu0WSC0CPJNnPb77u3LUIerQb33OxM3eZWiN0jOQgbnEV5RVbEdNhpkdx8vOwI2zBzt7GiKRqviW7Ic0UeqQNt9hohBu4RfR4H16pvP33Fzpmahi/vLhjH2kSI+9Dj/YA4fVhcHCQycnJu/J2qO1PSo4fP87169c5e/ZsXXACUPdZnJ+f533ve99d7X8v/OVf/iUPPfQQlmVx584dhoa2/cSGhoZqHcbl5WX6+/sB6OvrY3n5YDP//aBU6P/y7LPPbnkMPUNYD6wBfyeE+L+UUleEEOLQC+IQhwghhOD3f//3ed/73seP/MiP7OuL6rouMzMzbGxsMDY2xqlTp5re044ePcoLL7xAT0/PW246vxNbCbQ3btzgoYce2vd+3B+JMxFPc7PYqCwp+h66kCT0bYW2ArKuQ9kpE9d0/L06VErhq4A1x6bdiNSNNkpTw81n6YknyAuPoOLQkUgSSyQQQoS2CypA213MCA20NgjKbLfaqg8tgQrKIaGFqiU5C+VUU5obe2lCeSgt2XQMUqBQqgIiAnWm4QqhSgijHZQP0gR8BBIVOOixXrzijsZWUA4VzrsU+UL6tSaY8ip4XngMLdKLvbEIQlLZuFlbXw9yOOFAad1+nPwC0owROLsU7EIS7RpHi7ZhpQcpr0/j2wXszTAZNB7vRh8+S+52Y4Kg8l3Sw6fxPFUd81MoFaB8j8BzSA8/jO85SE2jsDqDlw/vdb7dvG7Izv8Tia5RCmszDc/ll29ixNpQvke0rQ8jmgIkTqVIMb9JfqV507KwPl+7x7cNnECPJlm99SKFzBJSMzBjbTilzYbtlFJ0jZwmsziFlejALjSv261kF7F0F+u3Gt+frqPn2JytV2yV8hnKmwtEdaPBtF5oJvGeCXJzl0n0nSTSMUJ+7jK7USlkKWeuEN/DYzTSMYKdnadt9G24LgTlsJaQhkX70fPhd8f3sHrDOkpKGXoHH0A8GYbB6OgoN2/e5L777ms8r0plT99TgFgsRk9PT8Nvv91QSlEsFltSfB2Et6r+gsMa7M3CvXNHPURL0DSNP/zDP+SJJ57gr/7qr14X0/gTJ05w5coVzp8/f09I5kdGRnjuuefo7+/HMAxmZ2dZWlpiZGSEY8eOvaHnKIVgoquH//Nf/yz/9kf/Ff/388/y9LWr2KUipm7gqYC1fJ6FzQ1mC3n20ro4nsdwdy+z2cYbZDOUXIeXZ2/z8NhRrq83N3Jthn+YuskD3T2sH8DerJVLdGcLuKkYuepYQbsZoTMSpeh7ZJwKVzOrnO7qJWhBpXW7mGU00YaSoVeG0HU8qPcQqF6Wy/h07WN0vxs+oZ9HVDv48qQQKBFHqCIQ1I4fMQE8nIqHH4AZiSK0DpRfqG1Z96/a+f8KGe0lKC+FBXGVOEOI6mphsYbyUcpH+RXcShbKrRGUqkV/qy14lZUwHcfdezvNakdoMSrrUzi58PNjJvrwyq19lvRYL75bxq76O0Q6j+MV5/dcX+gx9GgHTnam8cnAQ2gxlJOv/5tLDTM1jFtYaEpwbcErLmGmRnELc2jRHoRQdQSXkRwjfuRfIfXtEWvbtunu7mZwcJDp6emWvR12IpVKkUql6ood13UbDKHn5uYYHR0FwvjnpaVGMvszn/kMH/jAB/Y93tWrV/nVX/1Vnnrqqbs6T3EX36VmCKqGz9/5znf4hV/4BWzbBniKMM1nAvgw8EEhxP+slHr6sMg6xCG2MTQ0xE/+5E/yhS98gV/+5V9ueN73/bqaZWJiYt/vq2EYDA8PMz093dSv5q1EKpUiFouxvLxMX1/fvuseS6S5UylS8sOGR0o30IQk7zlkPQdT0yj7Lt6uS0nR9+g0IgRQR2JphNe6TLVekShMIXGq9/hACIoazBTztElJ0oqSDTxEAAlZtXuQESR2LSxn22weELKxkSUALYbwS6gqASaqyhqBj9KSqKBcW1aDClBaGuFnq0cR5IseiUQCgYs0IihfhvWJqP6rfHTLCsmsqu/W1jujSR3ZdgzPziKQBF6BSLIPz3XxytvKm8DJInUNaSUJ7G2STUhVO6+dCJwC8b6TlHaNH/p2DivdT3m1anOgR4n1HMPJ3aFStT7Qoh14drGO5HOLq3jlDHq0LbR52IHE4GlKKzeIdI2TmW00aU8OnWZ9tpGkKSzfID1wiuwu367Ac4gkOxpJLiFpHzmL6ymWrv93Ctn6scL2Iw9Q1M06P60tFNfnGXzbj5C5c531+V3H813aBk80HYPsOfYIizeeAWDw1GN1/l214w7dR2ZxilRXYxCObiUoZ+rrq3T/CbLzoaF8Yug4XmEVquOfRrwTLZIgXzWcLyxdJz10umG/0ojhVXL4dhGj/yi+3Ui8Wuk+ymtZkgNHcctlsreXMRMdpI+cQuoRPM/D6vlBNDNUbxqGEfoJ+/6BzcOenh6Wl5dZX1+ns7Oz7jnbtg8MSzty5EhNUb+XetTzPDQtTHD/fqy/4LAGezNxSHJ9H+KRRx5hcHCQb37zmwd+kVtBLBajvb29gcl+qyClZHx8nJdeeokgCBgYGODChQt3rc74XjHc3sET73oPH3jbad77f/w+K/nW1TfrhQKFSoVHj53kxdVlvCAgoutYuo4hNczqCKMhJYbUkFKgSUmxUOB0ZzdK0xChkr/O0N7xfRzPo2jbFCtl7CDghdnb/NCxE2CZJAwTJQRF12GlVMTe4Q12bXWFRyLDuFb4PmacChkn7Px1WlHaIxFms5uMtrXjHUCaeUqxUMrTH0+1dMHP+x7JJgaxe6EYeFhSa8mE3teiCK/YtBdsmluXOBcQ4G22lLQohY5daV1Vp0e78UqtKfeUVww7taUWu0EqwEofobx2o+EpI96P7zpUNmYat9MOTusKVGhWG5JV2/dQJzuHZkVRTccGB/DtLG5+bxLMyc9jtY3WutFG8giBm295BNIpzBFpO45XmkftOK9o76NEeh5t+BxtSeHb2tp48cUXyeVyr6nTNzY2xsWLF+nq6iISiVCpVBoKs7W1NXp6QgXZ008/fdfHgFAN9hM/8RP82Z/9GePj4wAMDg4yPz9ft85WLHdvby+Li4v09/ezuLhYO/5rwVat9PnPf57BwUF+7/d+j/Pnz//i1vNCiJPAXwJPCiH+SSn1vbctD3GIf0b4+Mc/zqOPPsrP/MzP1Dr8QRCwsLDA3NzcXdcsAwMDPP/88xSLxbv2F32jMT4+Xrsm7qc0M6TGcDTBkl3CCxQ5r55Asv3wnu41Gcdadyt0GNvX2YiQlAMPewfpte7aoY+XZtRps5QmEYZJWUC5Ooa47jtEhSStm5hCI0KALnf9RpQWKL2q6NoJRWis0ChnF8pFyWhNtaVqHl3VLWUMUBBUSMU1UFv79kCLo9yNOgsuEZTRYwN4pV12BMpD+EUCe4OtDYSewLBiSH07fVwFPoFfIWJ0UVrZTkH0SiskBk83eDEB+MUFRPsJyM3UPLQAnPwi0e5jSCNCJXO7zrwcwC9vEO0+hltYBgTKd1G+h9B04j1HKa1MYsTaQ0JP+RSXwvNxNmdp8B4DKnuMR1aP1nRpab2+fkj1n6SUz3Lnlf9O29B9CKnVpRECZOau0D32UNPRw7bBU9x55e+IJDsbngPILNyge/wcq1Pb3mKdYw/VCC6A9fnrGJEEbnV8UDNjdI48wMrNkPgqby7TOThKuWpCn+wdB69COVtfL+4saWwPUr0nKC68TKLvJKXMPPZafcPS36W6UwoiHcMUFkOyTuoWsd5TlJa3yTupR7A3Z9GsKFK3MOM68Z5R4j2jBK6DljhKrLc+vEdKiaZpeJ7X0tji8ePHeemll0in03XXi1ZILinlgerRhYWF2jX3+7H+gsMa7M3E9y4DOsSbDiEEn/3sZ/nsZz9LuR9grfsAACAASURBVLz7Bv3aMDY2xtzcHK7byojWGwelFCsrK0xNTeF5HuPj44yMjLzpBNdOnOof5Fv/5gnu6+1veE6XkoFUivt7+zk/NMxjI2M8dmSE830DjCeS3Lh5nXQhzwPJFBuZDRZWV7i9vMjNO3Ncm7vNy7dvcfHWJM9P3eTZmzf4h5vX+eblF3hl5haLmQzPzEzzj7dv8cLcbV68M88rS4tMrq2ymM+Rc11s38dXir999TrLq6tcW1ni8sI8r66usFkskpAao8k0pzq6ONHeyVwmQyJo/Nqv22UmsxkWinkm19dIawbthkVc05v6jwGUfA/X29s/YCdsFbD34GcjAqgrcPdDqOaKHrwiCvTWElGE8mqjcK1Aj9xd+Ilm7C3bboadCUtC6hiJI0CE0sqr2JmZpts42Vn0WHMTUAjVX5rVgZttNKUPvDLS3D2+LDHTY7iFBQK3MfFpN9ziClq0ByM5iFdaIHBbm7k04gNoZgonNxOOgAJCWkSG3ovV/UjTwmergNo5YhPsM2q8FzRN49ixY9y4cQOlFJVKpS6UY2uf30snb3Nzk/e+97189rOf5bHHHqst7+/vJ5VK8eyzz6KU4s/+7M9qTYz3v//9fOUrXwHgK1/5yvfU3Ng692effZaf+qmf4vz581vLdSGEppS6DvwqcD+h8ekhDnGIHYhGo/zWb/0Wn/jEJ/B9ny996Ut87Wtfo1Kp8PDDD991zSKE4Pjx47Xrzr0E0zQ5cuQIt27dOnDdsVgKN1AUmiTd5TyXTdfe066g4vu4vkdcamQ9u+n9XwG4Hsr1iPgBcamh2Q6lio3v+3W7rqgAOwjI+z7rAWz6EjfQUBgo9HD8UBgoYaHQoDqaCBI0q5qgKFDCRMlo7QEaSoujhIEgTEUMHy5IExGU9wi5aV5bC1WpHnf3i/XQI107/reActaQRhSvtIhXWsSvrITG9kGWxMBpIh3jWOkRNCuNW5gn2hUqmrVImkjHONHukxjxTmKWi0oMYCb7iPWcINo1gZUeRI/EUIFD4DS/v1vJDuK9xwmcfLUB5qH8CuXVV4h1D1Nen6S4dKWOWPHKWVKDjf5IbnGDeOdw0+OU16aJdTQ23J1ihkiqB92Kkxo6zdLUS+RWZgDYnH+FrrHmRuTNbteJ7hHy63MEnkOyu9EMHcAuZLCrI7i6FSd15EGWJ+vJskp+nbbBUyAkPRMX0AyzRnBtIZLqJ9E7QffEBezNOw0EV3rwfgrL2/YSxaVX2VyZJXnkLLnFVxp8zQDs4gbCjJMYPEOs9yQy1l0juAACJKuTL2CmB0FqCF0nPvQ2EkNvo338TFgHVJV+2bkr+CrRQHBtYWts0Wuh3rcsi6GhIaan671ggyBo6ZqYTCZpa2tjbq45CTo3N8eRI0cO3M9eeKvrLziswd5MHJJc36cYGBjgZ3/2Z/njP/7j12V/uq4zOjrK1NTU67K/14JMJsMLL7zA2toaZ86c4aGHHuLWrVuv6cfq640Tff08+5uf5k/e+z/xW4+/n/eduI9OIfByWRYW7nB16lWev36Vf7j6Mv9w7QrPT97glflZljYzzK6t8o9XX+ZkPE5/ojVyY3pthRenJ3mgu3Wi5ZWlRVTFJq5vmz6ulopcW13m0sI8Ly7eYWptlaevXaNXs+i0mhNDK5USM5sbrDmVajdWENcMOgyLDsMiqRmIag13p5yvS5TZD1nPvasCvhh4La9fdFokHbS9DXR3Q7fugrjyt/w9WkPgbN7d+m4es+0oRmIIr2JTWn4Ft9jCKGKT4lkphZkew6ts4u8zzmhnptHjoRmyFulAj7bjZA/+oRMeV2Ik+hCa2bLCTRgJjOQRvPIygZMLi223jB4fJH38f8FMHcXfI7V0p5R+p7fDa0F7ezuWZbG8vIxt23Uk19raGp2dnS2RXP/lv/wXhoaGeOaZZ3jve99b81j8d//u3zE5Oclv//Zvc+bMGc6cOcPKSjiC8id/8id85CMfYWJigvHxcR5//HEAnnjiCb797W9z7Ngxnn76aZ544onX9NqAWic2Go2Srw+6CNiOAVsBOgn9IQ5xiEPswo//+I8zOTnJI488wne+8x0ee+wxJiYmmpout4K2tjZM02Rt7d77yg0ODpLJZCgW929umJrGicTe901fKeKajrfVLFAQERpJzUCvBtK4we4cxhBKKShXcH2PiKFTkpD3XVxTpyQVa24FjXB/hpAECjK+g6uCaqiNYj0IyPgBAVrogyUt0JKgt4dJicgq2WWAFg/9uAgQyt1+4FXXa/z5JJSD0pv/JhXKA71Z/afQIs2VRFKPhCP7elXdpwLwsujxXaOjKiCw11C+jVdahKCMHkliJtqIdk2g/DJOfg57cxrfCU3po1oZX1rYmRmc7BxeaQ1783YYiqM1Km5ivfdRXruOvXkLacYani+vTWHEmoe++KVlou2NpJVpWnsmactdjUPNjBFp6yc1cArX11h6tXFEkD1+KxRWZxHV+kABie5RXLuEWw7vf4W1OZI9zX2gMguv0nviXyB0i42ZxvFKgMzyDF2jp1mZfA6n2GhRcufaM6AnyS/dIGgggAWVfOPUQDTdT2VzoennDCDaOYJbzhEEHoXFa/jFlbrnN2dDJZ5mJdFMCz3SFpKxQhBJduCUsmzOvYJbyWEkBug49a+bHgfCmsEwjNCvq4XfY/39/RSLRbLZ6vhu1X+qVYyOjrK8vNxUxDE3N9c0oXE37tX6Cw5rsDcT4oAfkfdWS+kQdbBtm0cffZSvfvWrr8uYoVKKixcvcuLEiX0NAl9v5PN5Jicna2mPO+X6U1NTNc+KewGZTIaZmRlOnz7N5775df73v/x/mq4nhaAjnqAtFiNhRcKERSGRAqLJJHnfrwm4t/8NUwzFDvJDKYUmBI6mYUWiaLoemndWxxiDIKBQLBCNxQhUuL6haRztH6AU+MxkM/hNvuNxw+TCsXE64wkUisVSoeEmdL7/CIHW/MYUjg3oRDWdhG7SEYniKYWHanq8LbRpBsZd+MilpE6kFW8upTD8DWQrejFnDaGax53X7RNJOTsfxoe3AD+QuIW9vaYaoMVxS4v7ryN0NKsLv1IIk6PW79JMHsBsR/phJ1AaCaSZxMntPWpYf/go0Y6jOIW5OnP5/aDHekCAXwnTLK30aOM4Rt1BJGZyOHwvdh5DaCQGHiM+8BhC6vi+j+u6ta7iFpRSvPDCC7VuGIRdw0uXLnHq1KnXNP7jeR6XLl0ilUrR09NTS2y8dOkSX/7yl/nSl7501/u8l+B5Hr/0S7/E17/+db761a/yzne+s/ZFF0J0Ap8CHldKjb91Z3kIDmuwexKXL1/mySefRAhBLpfjb/7mb14Xtblt21y+fJnz58+/per1Ztjc3GR6eroumKMZlFJ8d32x5qW1c3lCN4hIDVcFeCr03tyNDsPCVQG2CkApDAReuUJg6LhVGwUJxHUDtes8YlKvWTaYIkyIdpVPWjPryJSY1IhIiSbkdsqz8pH4aMqrGrxXqzO/2FSZpdARQaPCRiHr0hbrnhMWQeBVPbnCbOnwoeGWVqsEWpUUMNIovwzSwMnPgdCQRgqEJAgUzubNhv3LaB/Kc/HKG9upeULDsz28Jp6hntlHJJpAeGVAUi5lcV2FGYmh4aF8H+XbSD1Cee1Vti5H0a4TlDPzuPn6KapY9ymyt5+n2WUr1v8g6028q8yOo+QWr9e/Dt0kSB/D0sAu5Sis3sarJhMmusfIrd4m8BqVcZphESjRkGJoxtpoO/IApc0VssvT+G4TG4ZIHM91aobvVrKLdN8E+dXbxDsGWL/9T023A+gaewhN18nsQYL1Hn+U1cl/ZOi+H6C4Oo1b3g5oaBs+S+HOy/XnUjXRT3QfwTQ0SqvbdZ8WSRFtG6CwFL5n6eFz5OZeAhU05Qs7x87iFu+QHHgAJz9PrHsCMxHFLWygUETaRuk597+1REK5rltrKB40tlgul7ly5Qrnzp3DdV2uX7/O6dONPmJ7IZvNMj09zZkzZ+rO7XOf+xynTp3iZ37mZ1re172IwxrszcEhyfV9jm9+85v8x//4H/nSl770uhiy53I5bt68eWCazuuBcrnM1NQUtm0zMTFBOt3YAfR9n+eff56zZ88eOM/9ZuHKlSv09PTQ09PDXzzz93zl6W9RqlQoOzb5conNQoFMYf/RrH/xwGmeW7yzb3rjTpwcGMQ2LZYO6KRuwdQ03n78JPOlIuOdnUQMg6VCgbXy9hx/byLB8eEhXBR90TiWrjNXyNX+7p2RKCOdraVYnkx1IHcU5YHjErUsZLU7qxQEVQIsrYWph1vJjEJsaZpEQ2EogJSmQ7XM3O+CpAU2WpOiswEqQDgHkEtVuF6AW2iNEEJPUdlo9M3ac98iDZXmkmwt0k3g+lQ2bqG24s6FRCltz9TCvWC1jeLm5zBSw7j5JQKvtRFnoUcx4t0IIfErKy1soGG1jeLkZmEH2Sg0C82MNjXc12O9oDx8u777aSZHSI29Bz3aVbe8WZHlOA5Xr16ti4aGkDx/9dVXX/O1bG1tjRs3bnD27FlisbBz/Y1vfIPJyUk+9alP3fX+7jVMT0/zrne9i5WVFfL5/DeAq4ADvAN4FPi3Sqk/OTQ9fUtx+L7fQ1hYWODjH/84GxsbfOYzn+HcuXN89KMf5f777+eDH/zg63KMmZkZlFL7Joy9Vbh69Srd3d0HetLMlwtc3KwGoAhJXDfIeQ5Otd6RgCElfhOFR1RqRDWdiutSUv6eSp8uK4rtew1EV7thoe9ojInqPuOahoYkQBEAcak1NNwEYOLU6hGUqvpjNdZzCgFBpTryFdS9DoVE+Dvu00oRiAhClUOiy24UZygZx7fXEdIM1fH+1v1S4FTy9c02aSH0BJ6dJbBzqMDGiA3g2asIoWPnFpBmEs1sIwzK0ahs3sGvZPHtsEaKdp8MfTVVgC/TeLnbdfWXNONUNle3649dsNpHKdy50rA81nMf2Zl6s3alFNHu45Qyq6ggDOzxyllU4JM8cpbVyX9EGhGi7UNIM0lm/hV8p4yvJXEKjbVH59g5lm82UXIBHSNnWJ2+RCTVRSTRiRFNsXrrJXy3Qvf4ww3jhjvRPX4eUHh2mfW5K3WjtX0nfoClHV5ctW3GHmK56tnVNXSMwmq9grzv+KOsTG6/H3okScTwQQV0HT3P5u1GU/72kbNkboeEWc+xC5QWQxIs3jOBU8zgFtdr6yb7T4ESSDNKfu4yUoYjtmYshZA6wxfeQX5xHre8QmpgHCEMfLeMCjys9Ci953+p5fooCAJcN5zIaEWxOjs7i+d5dHV1sbCwwMmTJ1s6zhZeffVVEokEAwMDtWUf+9jH+MhHPlI3avj9isMa7I2H9slPfnK/5/d98hBvPY4fP86Xv/xlhoaGXhe1k2VZbG5uEgTBnukW3yscx2FycpLbt28zPDzM+Ph43UjQTkgpMU2T+fn579ns7/VCOp3m2rVr9Pf388DwKKfHxvnz//b/8crsDJuFAhWneVGwE3Mry5wZOsK6bRO0cO1ay+eJEs6rl1rwTfOV4tbaKg/0D3BjY52FXI5cpUJPLM7R9k7arQiLhTxGALFYhILnknVsOqwoPdE4Ocem5Hv0RmJI/eCOsh8EJAyzdrMUmoaPwlMKd0vhVS0uza3isspu7QjwrvU1dz5MKTGlRBMCCQSei+u46FLWG9MLDRmUDh4CFBrCby1EQGgRvFYN6FWAZ7ceTqACLyySq5BmG0JL4eQ2sDfn8UrruyLDFVZ6OFx+Fwg8BzM9gp2ZCo/ZAozEAAQ+XmkFv7KJmR7el1zTY71I08IrLtHwu1z5aFaawKvUfq8IPYYZ78crL9eZ2ws9Snr0PSRH3oVmNCqwhBC1kcUtkqtcLlMsFunu7q5b17Ks2nPNCPSDEIvFmJmZIZlM1q6FTz31FENDQw2E2vcj2tvbefzxxzFNk7//+78vAT8EXAA84NNKqT8FOKBGOMQbi0++1SdwiG2USiWGhob4xCc+Ufvh9eijj/KLv/iL/PRP//Tr0ohLpVK8+uqrBxq9vxXYWfvsp+RIGSYV30cIQd5zKflencJbEZJREU3HDnyUUkSkRlI3yLsued9FOg5+NUWtGeKazlarLCo1TASe41ARirhm1rkBuFVFPCKsKUKFl6qq6+v3H1SpMIHaTmGU1jaZRZXgkkY42hhUqhp8tkcdazYBOkgD1/PRRaW6T9k00EWogMArQeA0pD4KI1V//1U++CFRofwiQmoEbq6qEPPQIl145TUCJxc+3CyaZRF4DnokhRHrxCsusnWvVgii7SN1FgjKd4l1nwDAtxvv/ZoRwUwOoFsp9EgSLZJEj6QQQiL0BFbbEHq0HYRO4FRwckvIaAf5tdu4lSJ+EKDHu9CsJLbrUcquU86uUtq4EyYhKoXZfgQn32ipEEl1U8o0t0GIdw0jjTj5lduUc2sU1u/U6p54xwDFjeaq8p7xc4AitzxDbqXRlkHqBvauUUQz1kalsFFTeMU7h/DKOVTgY0RTdI0+wOrUC3XbBJ5D++Bx2vommhJcyb7jZO9sm/67dhGJR9vIOfKL1wh2Gc4jJGaiE1SAm18h3tlHJG6SGjyOJit0jj8IRoxU7zCdp/9X4oOP0Hb03bQfe5zE4IW7agBuGc/71e/2QdumUilu3bpVa0q2t7fmibuFdDrNjRs36q6F//7f/3t+/ud//jUFC91rOKzB3ngcKrn+GeD69et86EMf4tvf/vbrUhQ5jsPFixdf90TDndHao6Oj9PX1tXSBVUpx+fJljh49SlvbveHBNzs7i+M4TExMALCez/Evf+Pj3LizX2LMNizDoCfVxokjI2wEPoFSOK5Lxa4gpIamawRK4QUBQTVZ0fMD+trayWmSsl//1VQofM9H20VIRXSdsYEBlkqNKhpT0xjv6GKovR3iFu6Or3vKsOiORlkul3igt5/ggLRFgIlEG2YL3R0BdBtWyzdXXQg6NOPAEQkAvDxCueyVzlODu4kIDlbFKQTl7EJj1Pge+P/Ze/MgSc7y3Pf3ZWZl7Vvve/f0dPf0bNJIoxmBhISMEDbGBww4hLhhHwxhA7bPJcLnhG3hMBjiGCODr8+9OMLguAeOBXGNEdgOG2PLCIMwHJBmRqPRjKSZ6XV6m16rqmuvzMrM7/5R1Ut1VXdXa0YLpp+IipnO+jLry+zqL9983vd9HsuSdWtQAWRNDa9bx0wvU0zvvp/qDtfMbNaCROAO9WAkpnAF2+qalwTckX7M1UoxekUPomgKOFuug6LiDvdhpqbY7XbhDh/Ayl1HD/Vi5RaRTiUZ7G26lWDPm1Fc1Xofm7G1bTEWi7G6urrukrN17Pnz5zl27Bhebz3mBJV4+umnEUJw22234XK5+L3f+z3e/e53c//99+/5WK9VFItFdF0/CISBpJRyYrd99vGKYT8G+wnA5z//eUZGRvjUpz51U463srLC/Pw8x48fvynHu5mYnp6mWCzWXG83w3RsnliawahRrS6lJOzScQmFvGOTt62qL3qj7kEBEkWj6t4fdenETAOvouLaEvO6HQh7PEilMhZSEYS0Sodnt1DwK2pVtVhJnn7Lvc7OleIAoZWTT+XzktSu9BJuhFU7IWVZFjjVkgkOes0qLxQdy7LKbosbEHoDZrJaR1coOooeIR+rrCzXfJ0UM8uVep5CoaiGCUSaUYSGZeax8insQhIrn0DR3BRWl9C8DejBlhKZklnENjMoLh+KHiG38GLF56i+NtI1qrxUT4hMOlXVTqj4O8gsX6s+bU0nlzeq4g7dFyaXqk4+RrqPE5t6gab+21gcebrqfaFoNB04wfLkhXVtLE+4hUC0nZVrJSfKcNsAiesjVfsCRDqGWC2/J6XE2zJEbrFybPeRN+BYefKrCxTS1b9LTfcR7R7GLqRwMls6CoSCL9pJLl75HNE6fBfG8iiWUbtDxBPpJNDSjyguVVT8+Vr7iXYPgNqAFh4i0HpLzf33imKxiGVZaJq2a9tiJpPh4sWL9Pb2rrsV7gWxWIy5uTmOHz+OEIJ77rmHZ5555jXXzn0j2I/BXj7sC8+/CvjABz5AS0sLx44dW98Wj8d54IEHGBwc5IEHHiCRSNR9vOHhYe6++26+/OUv35T56bpOZ2dnXW469cBxHGZnZzlz5gyKonDnnXfS3t5eN8mx5pg2MjLymnEe6urqIh6PkyuTR43BEP/wB39EZ2MTXl2nt7mZW3oP8LqhYd4wfIS7Boe5vbefgeZWIm43Ri7HzMJ1vnP2x8jkKhcnx7k8O83k8hITi/OMzs0yfn2OqYV5ZpYWmV9ZYTkR59LkON58gXw+RyydWn/F02mS+RzxdLridT2RYGJmlqONzRxvaavQtzJtm8vLizwxcoVkbJVub2A9+ZkqGoynVnGkQy5fIFDHDeV6PlPX70dCTS2O7WBJSXKXNs21rJJQvaUqLTtXLjVXqSnwrtVX2SOQuPzbOxRWHbYOl0VFj6Dojdi2wDbyZK9fqovgArCNJO7w7hWbLn8bqhYgv3wFx8pjJCbRdjkPRQ+heFowVyeocls006h6tOL3q/nb0DwRzNS1qvE1525m0UMHKGZmKggu1dNI9PCvED749l0JLqh2+tnJmlpVVQYGBl6Sa9maG1BPTw9jYyVNjHpFT1+L+Ju/+Ru+//3vA7C8vMzKygqWZeFyuZBSTkgpn90Prvaxj73j13/91/nRj37E1av1t6vvhKamJmzb3lMc+Eqhq6uLlZWVXZ29dUXlUKCyckNKSdSlowrBillg3sghJGg1iLC4WSBRNPBtilmklEQ1NzGzRBDlHRtty/3dUGDJLKBJiU9R8SsqbqGAkEgpK+4DhnQolI1zxKaXg6hW91R9gCwnvDa9K9Tad78dtDy3c1dWttMfdUw0vYa2pJVF81W7fkvHxC6sILTK+6mVm0MqEksN44r0420exOUP4XMXcfLXsXLTqGoRK7eAtLOouo7qCRDuuxOXL0QhNkohPo5tliq7nGKuKrEKIIspAu3Vjop2IUW061jVdpdaW7bDsUz8jdVOemYuWSUUH+44THzmMtKxSMy8gKpXJ7WkY7E8fo6mvltQdS9tQ6+nmEutE1wAyYUxAo2VGscSCDb34G/opv3wPYQ7hlF1XxXBBWCYBmYuWZPg8jd144+2kJh6jtTiJJ7wxu9O9zcQ7jhcRXApmofk3FW8Tf1Vx3N5IzQdPI0/FMalmZXfOV8L3kgrqr+b8OC7bxrBBaW4anNV/U4IBALour5VYL1uNDY2omkaS0tLOI6DlHJXYu21iv0Y7JXHT+Y35Sccv/qrv8rjjz9ese2RRx7h/vvvZ3R0lPvvv59HHnlkT8f8+Mc/zl/+5V/etKCoq6uLWCy2ayCzE6SULC4ucubMmQpr7ZeyQPn9fqLRKHNzexD2fhmhKEqV5Xd3cwt/99FPotg2U9evc3FshKdeuMQPLz3Hj168xPmxq4xdn2U1U1n6/ezYCKfaOmp9TE1cmZuh3+NBq/M6zqeSzC4u8uzcDEXb4mhzK4ebW0rl+2WcmZ7ixekZ3Ai6fMH1c8pZFhdji2QKBi6h0OBy06x7CKrVFVs528Koo5USIG3X75wIID11toEIrSQIi0Q4uTLhlSlnXlXstQq47QLTGlC1PVQA1aj4Eoobxd0Eaggjlya3cpXcyosUswu4Rbbs6lQ/FE3f/j09iMvXQSE+SXGL245TNLe95nq4B5wicgftLWP1GnqgG6FouKP92IWVKi2tWhCKhjvSj2PEMVPTKGsBvlAJdL6RpuMfxB3q2/U4m6GVM/K2be9IckGpJNzr9bKwUH+FHUChUMDtdtPa2sr8/Dzf//73mZ+fvyH76lcTX/rSl/je974HwPve9z5aWlo4ePAgJ0+eRAjx90KI/yGE+D+FEG8VQpwUQrw2RBD3sY/XODRN4zOf+Qy///u/f9MScUNDQ4yOjr5mEntrUBSFwcHBugi9gUCYI8ES0aUCQc3FopGvSHIligVcNRJRklKCS1dUHMdBQxBy6cS2in9LiaeGA13WLlKQNnlpU6TksJhyLBRKFVxrn5lzbGwpK2QTJFBEL7Ulrr8AtVYSy4YajooCG7mNm7OQeYSrxrHsXClWqAUrA6KSUJKOgRBOqaVSqKieFjR/F5qvA9XTiCfSi7fpKK5AO7ibkK4Qmu4i0taD2xcoOTFuIeNsYxV/x3E8kb5Sgs+xMFMTqHptAs5IzuJtrCRgnGIO3V+7nUypESMZqQVCrdUkDkCooTo5JxSNQGMnLYfeQMOBO4j03EpyYbLU5ghYRo5Qcw+BpuqEYLDlAJ5AA5ruZWHkxzXF5IPNPai6l+b+22nqP4mm+0kuTjFz8TvYVpHE7GUso7o7ItI+SHz6ItlcDtVTef5Nfbdg55NkYxsklnQFCLQdItpzAscuVrQpriHacxwzG2f52nP4mgfW47+GvpN43CpG4hruUAR3oBSnKi4vwc7DtA7dhq034G1/003XV15zWwTqclv0eDwkk8n1ooC9YnBwkH/5l3/h6tWrRKPRl10v+uXCfgz2ymO/XfFVwrVr1/iFX/gFnn++VNJ76NAhnnzySdrb25mfn+e+++7bc1bwi1/8IufPn+ezn/3sTZljPB5nZmZmT44Ym/cdHx8nEAjQ399/U7QqLMvi7NmznDx5El3f/kH/lcRmEfo1/OOPf8h7H/nEno91y5FjXJrfwYVuC95w7FZ+vFCfgDrA/UeOMZbecHUJut0MNjaTLRYZi68ghODNw4dJq5I2XwCP5mKxUKqg8mkujrd3VIi8akIQ1HQ0ITAch5RloisKB0NRxDa2x5sRVl149lBy3KTpFcTctrCypbLtGpCAYUoUzYtGDiELgAPSrumgVNpHkF+dgXqcGwHTKInRSlxYhQTFzM6/U0tEkPWK21MStbVtB7G53UGo6MEuCrFx5NaWwk3wNh8qC8OvQcUd7cNIVLc8Y870IwAAIABJREFU1IIr2InmDWFl65uvK9CBtPIlW/IyNG8zqq+BcN/PVQnL7wW2bWOaJuPj43R2du6o0bDmlnjixIm6145EIsHKygqDg4NMTEzwS7/0SwQCAc6fP/8TGWSNjY0RiURoamrin/7pn7h06RKxWIyZmRkee+yxHwKtQBPgKb9eJ6U8sy96+qpi/7r/hEBKyXvf+14efPBBfu7nfu6mHHNsbAy32/2aJNYvXbpEe3s7TU07r+FSSv59ZZ60ZZKxa9+bgpqLnGUhKGstlkknIQQNLjerRaMkRr9NBXjU5causSS3efzILduXFxa4vrDAkQMHaW9sQgCaouBVqmMRFw4qayRQ+UBWhlIsUP5ZFkvtjnYesYUwksIF1mrN2MJRvDiFGq2Jqq9USSVLWlxASYhellwYLSNe1piSCEVHKFpplump8pgtn4MLy0iiiC1zEApIrUJzVAJ6oAfbjKPqUbLzl0pTckdQvQ2Y6Tj55UpXR1egBdUTopjLYBs5VN0P0qGQmMLIZrFy1Yl3X+dJMrFZconZdUflYNc2LYaam0DXCZLxFYLBQIkMuj6CZWRp7D/F/JVqMfg1NA+cIj5zmUj7IIqqkl66tl5hFW4fJDE/hmNXV9xFOg7hCTYz+8L3q94LNHaSiVUn292BKEKomGVR+NaDt5OcLT3ftR16HSsT5yqE7NfQOnQ32blnas5f9zfgWDmcslOp7osS7TmC1+1COjbZ68/ReORnKMRGaTtxL/lkBs3bgCfgB0UhpZ3CtJV1WZWbDdM0sW1715jq/Pnz9PX1MTU1VeWWWC++8pWv8I1vfIOurq6b1rX0SmM/BnvlsU9yvUrYSnJFIhFWV0tVEVJKotHo+s/1wrZt7rnnHj73uc9x5MiRmzLPixcv0tnZSWNjY13j0+k0Y2NjqKrKwYMH8ftrlFjfABYWFkgkEhw+XF0K/WrAMAzOnz9fpV/2B4/+v/yPv/vano51qLuXq+lU3TcAn9uNr7mNRKG+ajuvS2eop4dVs1oLotkfoDcSJWka9La3ki8HRD3BMDaSuFFgINxANBTcdn6qEAQ1F01uLxG3F8NxsHdYQhSgaQ/aXD5FJbhdOf9mSAmFa7sK0Nu2xEpvDtgUUN0oigsUF0LREEIFRcEysthmFnDKLQ+loFNKB8puQVJaJecgXOSXL9V1TgCar70qcNwNeqiXfKzUvquHuilmlrELu68XisuH4vIi7Ryat6FkL56rFnathsDTOIC5OonqDoEmYBvXJSg5KuqBdorpmS3bPYQP/Bze1pvj3moYBs8//zzDw8PbmlesYWVlhYWFhYo28Z0wPz+PZVnrD5if+cxn+OY3v8mFCxdueN6vFRiGgWEYhMPhNsAPBCnpQrQB/ySlfGmp133cLOzHYD9BmJ6e5h3veAff/e53/8Mm9tZQKBS4cOECp0+f3rU6P2ka/HvsOtkahIKUkpCmU5QOYZdO2iqua3RpQqzrdq2NqQVVCPyaa11bS5ZdG8OaC4/m4vEfPMnZi89x9uIF5pc3EmAtDY286e57yRYMNEWgCIX/9u6HONRVWvMVSm6LlRMGYSU2/ShKYvPSBjtZJrQ2OS0KV8X4tflJRS/HFGtjRYl4QsGx8tjmarUGJoArjJWtTJwJ1QOKh2J6uno8oPq7QdrYZhKBUv4cUXZsLlVeARj5FErZlEeoHgqJ+SrizBXqo5hJgOoCKSnExgCJO9rP6vhTm+ak42k4QDGfx0xdx0wv44l2o7oD5JZGyZlgFbJ4op24g42oLi9L4+cp5jfa2ly+KC5/FDOfIrVcTSz5G3tIzNfu7gq3HcQTiGDbNrGpi8gaFUfR3ltYnni2YluwpQ8zm0SoKpnEUk1iqrn/tsr9FAVPtBszUXn924dO43brrEzUJrFaBu9kZfwMTZ0DVW2KAA19t7M6XTm/9sE7sAurGKklWobvxilmUT0uGg4colgoovuDqJ5GAt1vRahuzp07x/DwMMFg7RbZG4HjOJimiaIoO2pknTlzhtOnT9d0S9wLHnjgAYaGhn5iSa5a2I/BXl7styu+BlGPa0UtqKrKZz/7WR5++OG6SkjrweDgIKOjo7seL5/P8/zzzzMyMkJ/fz+33HLLTSe4AFpbW8nlcqRS9bvYvZxwu9019cv+27sfIuTbXV9oM67OTHGqu369n5xhcGgPDiP5oklgmwqr5WyGc3MzjC4vMTu/RJNeIgym00mup1N0eP0sZDM45vZVQraUrBZNJrMpCraFEOARCgFFhVweXVYuOA5sG7TWnH/ZhWlXCAHq7jf0GrlVsPM4xRSOEcPOL2LlrmNlZpFOkULiKoXEKMbqGGZyAjN1jWJ6mmJ2Diu3gJ1fwTESCKe6/H0nWPnFKu2M3eBYOVR3Ay5vC4WV0boILihraLgjuCMHsI10XQSX6ongDnWua3XZRhJVC2/f+hjsQVX1KoLL03iUlpMfwdd28qYQXFLK9ZbqekRIm5qaEEKwvFwPqbfRrriGt73tbczPz/PUU0/tsNdrF2v6Gfl8nje+8Y1MT0/jdrsJhUJIKRfLmhDPSSn/Hfjb/eBqH/vYG3p6evjFX/xFPv/5z9+U42maRl9fH+Pj9VXavpLweDy0tbUxNTW1++B8gZaFRAVh4FYUwuXWq2Uzz2rRwHRsctaGBY4lJfly9ZZHUWsSDioQVF14hEpA0VDL7YWqaVEwTFKpNP/wnW/zj//27QqCC2ApHuOJZ87yzad+yN//6Af87f/+Pu/7vz5Fppw4dACbLfcWQUUbokAipFkarfqQZadFKVxIxQOoOMKDI7wlYXkHbNsCK4dQPchiqvxKIs0E0owhhFab4IKa26VdQFpphFpNrGr+LmRhESFtHGMV24iXpAYKy9j5RbCzFDNzFDOz6wTX2jF9rdXJcsdYpZCYpLAyQiE2ylokZabmcPmbNu1vkl++iqaXjHVc/gYKiRmyCyXdrGjnMFLa5OPTrE49S2zsR4RbevA2HSDSexvBtiEcBKmFUQrJRUJt1dVI2dg07YfuxBPc+Fx/YxctB28juzJJ7NqzrM5cpLG3toGD2PJ9Crb0YWRXMbIJCqkVGrtrFwssTzxLQ/dRAFyBJvwN1QQXlKrunW10q5oO3sHK+JnStbMkql4ZA/qbD7A6U5lQa+4/SXbhRQqr1/E19pJfGcVYnSHY3o9jW7gjnbiCBwj2vh1F8yCEYHh4mCtXrrwsbc9rbYu2bW/7jLj5c/v7+5mdncUwqhPt9eD+++/ne9/73mvm+W+v2I/BXnnsk1yvEazpvkCpgmBz+9tecNddd9He3s63vvWtmzIvr9dLS0sLMzO1XQNN0+Tq1atcunSJtrY2br/9dsLh+kS9XwrWROhfipD0y4WtIvQA0UCQ3/xP76r7GK3RBm7pHySk69x96Ah3HzrKXYeOcNehw7x+6DCvGxzmzsFhTg8McfLgICf7B7jtwEHyhRy3d3ZzrKOTI23tDLW2MtjcQnc4QmcoTFckSnsoTEsgSKM/wAtzM3T6autErOHK8iJLyzEGQtGyXTaMJxPE81mWM2mEvTMxZUvJqlEieiwkBemg+X04ikARAq+iEFQ0fAgS6QxF00DIkjLVTi8B5OsQugTqEpbXVAF6fX9nwslvsgXfGU4xheZtrmssANLBE+nafVwZmq8ZiYp0HIxk7cztdhCaF8e2sIvGjm2Nayi6msExKWYr22LN5BR6oLKFRnEFcId6sLJzJSv0te16kOjh99Jw+CFU/eZkE+PxOGfPniWdTnP77bcjpayL2B8cHGRycnJdtH4nFAqFiuqwubk53va2t/GRj3zkJQdprybWiMDZ2Vmeeuqp9WtWLBYRQriEEJoo4U3AE6/ubPexj59M/O7v/i5f+9rX9qwBuB3a2trIZrMvWbj55URPTw+Li4sUCrUTO7lcjosXLzI+Ps6RgUGGgw1oCKIunXSxyKKRw9jUgpizLCIuN1rNJIgk4tKJutzrr4DqoiglsaLBvJEjYxUp2Ba2Y2O6VHIKfOVf/pGp2drxazgQJJGpvK6jc7N87NH/ibQdVAQ2aolb2xxuKq6qJFmpgkst/99BSAPh5EDmSsSUlQI7C7LImlm1Ig2EVuOeaKdQ9Gj1dgA7h+ZtR3U3oXpaUD2tqJ5WcEUp0ECBRhRvR1mbqw2nrLPpmAlUT3VHhlPMoIdqJ1ft/Dx6qNIRzzZW8TQcqBorbQN3KIziqtQwtctSBcVspRuinV2gsfdExbbM4gi2mWVl4hyJucsY6Y2ElKYpBFqqHT1jk8/Q0DkIQtB26HVYuRjxqecqB0kb1eUm0jlMU98JWgZP0zp0J0KB9oE70H1hVJeH9NI1zOxGwlARgmjncM1rkzdMQp1HEXaefLw6Dgu1HmB54jyx6xO4vJXxaFP/SeLXNiq0UovjeBr71n/2Nx/Aym8hhYMNWKnS91goKt5QCNUTpPHIz6B5vEjHQg8dxN/5JsSm1ttgMEg0Gt32Ge5GsWYEtJ0I/ZqwOpRI+4MHDzIyUtu9cjckk0ne85738NGPfvQlz/fVxH4M9sqjvqe2fbzsePvb386jjz7Kww8/zKOPPso73vGOl3QcIQSPPPIIP//zP8+b3/xmvN49iGZvg97eXs6ePUtbW9t6ZYNt20xNTbG4uEhfXx9DQ0OvmE5NIBAgFAoxPz//kstebyY2i9Cv9ZvHYjHu7jpAwONdzwqGfD46GlsIBwO4VA3DKrKayTAbX2ExnWIxnYLpSQ4NDjOyvL0A+FbceyzMxaXFusdrUnJbZzd50+TKcu39fnxtkpDfR2soiGnbxI08tpRcTcRYzGW578AAUsBqsXbL2lw+Q8TtqdnGUJSSIjYI0HweXJqOW6lvKbIBaRVKN3FRdk6s9b0TGhKllkdSBUzHTV1NINJGD3ZjpupzHNUDLVj5+iqGAORWu/IqCFzBTqxCmtxSqbXR03Bw1702wxXqwcrFMBITqHoA1e1F2rVbXYXqxh3uQkle27ZfqhAfQw93YRdWcIcPYOUWKGYqWwp8bXcQ6nsLyl7E+3dANptldHQUIQRHjx7F7/evBwmWZe3a0qPr+rpb4vBw7eB1DYZhVJBcMzMznDhxgqNHj/LpT3+aT3ziEzfjlF4RFAoFxsfHaWlp4dlnnyUSidDR0YGiKCiKgpQbasBCiCPAgfL/VSll/Vao+9jHTzm8Xi8f+9jH+MM//EO+8IUv3HBctDmxVxYovkkzvXGsidCPjIxwyy0b7m2maTIxMUEqlWJgYICGhgYAbpUBvKrKj+O1CcBk0UATgoim4yAR5VY+w7GZK+uDhjT3tjIIqXI1WNTlRpcC27G5OjHOciJeNTYcDHKof5AzE2NV7z199TL/6zuP82s/+zZA4AgVVRZBrsUzslQtblcSZAJZqvKyM5u2Aa4A0nRAVsdLisuPbVUTmIrqqhm9CC0AOAjNi5XbaFsUQEAH4QpiZRdq7qsgwd2AbWy5HtJEqG6kXZ28cfkbMFOV93VFKeLyNVLMxSq220aaQMdRUlPn1rcVM0uEem4nNX2+Ymwxs4KiZVB1H7a5kRSLtB4gl6jWms0sTeBrqKFNJxSkdGg/9HpWJs7UbEvMJa4TbukmvXwN26o+x0DrAPHZK1XbE3OXaeytdCX0NvaiuoNgplElFAvZqr9JIRRU1YV0LPKpZfS2flzSwS4WaDpwGysTZ6s+Kzb9Aj6vG3/zAQqJ2YprAhBt7SO3eAVPqIFgx1HcoQi6P4KqF5G2Saj/XbjDtcX7Dxw4wNmzZ2lubr4pz4Rb4XK51vW5tlbVb62Ib2xsZHFxkaWlpT0Xc8zOzvLII4/w27/92/zgBz/gnnvuuSnzfyWwH4O9OtgnuV4FvPe97+XJJ59kZWWFrq4uPvnJT/Lwww/z4IMP8sUvfpHe3l4ee+yxl3z8zs5OHnroIf78z/+c3/3d373h+aqqSn9/P2NjYxw+fJi5uTlmZ2fp7OzkzjvvfFXsXPv7+zl37hzNzc3rWYJXE5FIBF3XmZ6eJhaLoWkar7vjFA8/9Ct89d+/y0IiTiydIjU/C7toxTd7POwlzzG7B4IL4LnZadosE0tKOsIROkMhJuIxYtlsxbinxse5dWgARVUYCEcZS5Z0JVaNAhOxFaLhEG5FJaS5MGyb1CZRWQks5bO0+Xev3EnZFm6h1hW8l9oHBJosbnIyFKUqq82klxDgaoBiDWHXTfD7PBhJFUXsfg9xuQNb1Tm2xXbk0Xaw88sIVwBZrHTeFKqOy9dGITlHdv75ivcKiWsoLj9OsfL3VgWh4Y70kl/Z+FbZZgZXoBXLylVdd1egA5wCZvLazseVDtJ20IM9VVogqqeRyOA7cIerM74vBWsC85lMhsHBQSKRDTertZJ50zRxHGfX9ai1tZXFxUUSiQTR6DbZ8vJnbl5bZmdnuffee9cTCMvLyzQ376Fi71XEzMwMH//4xzl79ixSSnK5HA8++CCdnZ20t7fzB3/wB+8Hpij96f4ssJYKf+08Ue9jHz8heNe73sUXvvAFzp07x6lTp274eMFgEL/fz+LiIm1tbTdhhjcPjY2NzM7OEo/HCYVCTE1Nsby8TF9fH4cOHaq4vwghGAxEeDEdJ7kpQeZRFLyKxmqxwFw+Q7vHjyoEScvE2rKm+1UNBKSsyhSPisCRNqmiiQBCrlKrVCqdpquzi2g4QjweY65s1tPZ2s74NrGTx+3mfz3xzzSGI7g9HtK5HNl8iga/n76WFjojYRoDIXQ0FGFVLpLChVQC4OQRlOIKIYvgCiPNGokvxwChl10ONxE0dhahh5HmhlmQojcgrXRJJ0vZxiinmEb1NGHXELWXjoHqaa4iuaSVxR3upRCvjDyl0JCOheppwCqsricNnWIOT0NvJcklVFRPBFX34g51YqQXyq7WYKamcIfbMZKVwa9jFYh0HCK2qaopM/8CHYfu5PrVShF6T6gZX7QdT0M3iaUFguEwUjqk5keITZY0r5r772Bp7EzFfro/iscfJrN8jca+21gaO8dWePzV7phrSC1do234brKpBNmVa+RjG+25iuoi0nmI5PXK69Y6dCdLoxvzTy5M0Hn4bhxjtSbBVboWJtG+e8kuXakguISi0TZ0iuz15wl1HqX16GHMnEDVXLiDPqRTJDL0EJqnYdtzUFWVoaEhrly58pKF33eCoihomrZWkVTx91rL/XpwcJBnn32WaDS6p+e3ubk5ent7+fznP8+HPvQh/vVf//WmncPLjf0Y7NXBvvD8f1AYhsGdd97J17/+dTo7O3ffYRc4jsPTTz+N4zi0trbS19eHpr26HOn169dJp9McOnToVZ0HlHqsR0ZGiMVinDhxYj1z+cL0NU7/9of3dKz2aCPzjrOnG9HgwUGmU8ndB5Zx3+GjTGY2+toFcKi5BQV4cXEBq5wNe/PwYfLuUmamOxAiaRikyk4v9/YeRLg2sjY+VcOvamQti1zZMWc4FEXXdr+JNWtu9BruRrWgAX6xG93kAiHBmN+1misRW8Kn1qFpJVSy8WtVltvbwXbUXZ0VN8OQEZRc2bVQC+DyRMitjCGt7TW+PI1DFGLbU6Iufxu2ZWDlapN9vtYjmwiqDXH5epZ+d8MAVnYBl7+1pCsmBKAQ6HoDwe77EOqNk8+2bTM9Pb1eMdra2rrt30WxWFzPJO5GdBUKBS5evMjJkydr6nlJKTl79iynT59e3/Zrv/ZrfPSjH+XEiRNYlvWqr397wdLSEt/4xje4cuUK//zP/8zq6iodHR3kcjkKhQJzc3PzlJ6wokAM+B0p5df2s4ivOvZjsFcIfX19BINBVFVF0zTOnTtHPB7nPe95D9euXaOvr4/HHntsR2J8My5dusSHP/xhHn/88bo0A3eDaZo888wzVSY3rwVks1nOnz+Pqqp0d3fT2dm54xqcs4pcTMW4korTqHtYKGSxK/S6VCSSJreP8VSCRq+PgmXhdbnAkbhUFRVBzrbwqRq6omJJh4RZQAiBlBKvqvHU+Wf50re+uX7PUBWFIw0NjLzwPIcHh3h26lrN+Z0ePoqqqpy7PrceC63hDYcPM720xJxTmq8iBL/+ulN85j/9fHlEEVDASpYE6VHLMYONNKsryvKmwOt1YxvZEimk6KUkXblH0nEcnGKmdIwtS7HpeFGsOFXOz4qOtIul/bZC9WEVVsvJQKUkRK+oCNWDlUuC4sKx8hTzcbBLsYfmbSe3dKVEYrl8CLVkzGNmkjhmFquQwi5sxJ+ehgGSU+fRQ22our80XvNSWF2kmE9iZpZRFBeq24/mjSBdAYqFLNKxsYwcxXwSw5SongC6J0DRSJO6Xqq00v1RFH87q9PPVZ1apPMw8ZkXSj8IQaCpF4EkGyu16gVbDpCOzWMZlYlBRXXR0Hcb16/8aH2b2x8l3D7E8tRF9EgPVmYJI10dRzX13Up8+iIIgTfciuaNkF+ZxNmU9BWqSrRtAH8oQqLGvBXVRVP/7cQnzxFuG8RIXFufr9fjxsqt0NB3C0I1CPcMYaTy+Jra0LzNBPt+HqWGFlstvPjii0SjUdrb2+savxc4joNlWTiOU0Fczc2VqgC3PocuLi4Sj8frNhGTUnLPPfdw4cIFhBD7Mdg+6sK+Jtd/ULjdbj75yU/ysY997Ia1q+LxOM888wx+v3+9p/q1sLi0t7eTSqXIZGrcyF8hrGmSrblQDgwMEI9vBDJHe/o43NWzp2POJ2Icad1btrbds7cS5K2aQhK4srzEi8tLhL0+TnX30hWO8PTkxLrs6kwmhenY9IdKQf6Li/MV362cbbFsFsg5FiFNp0n3MJNKbturvxkpu1j399QC7K2+4FUolgNLiUSUrLy3gS+4swX6Osoti/XC5a3vYWh9Hh4Fzd+G4m7GWJ0ju/D8jgQXQDE9X1MrTEpwNwxipOe3JbgA8ssjqJ6Gsrh8x7q4/E5QXH7c0X6K6RmkU8RMz+IK9uDyd9B84sOE+h64YYJLSsn169c5c+YMiqJw+vRp2tradiR+VbVUDVjP983j8dDR0VFlGLGGzToSa5idnaWvrw9g2/Xv61//OkePHkVRFM6d28gYnzlzhhMnTnDixAluvfVW/v7v/379vccff5xDhw4xMDDAI488sr59cnKSO++8k4GBAd7znvdgmvXWEVajpaWF3/zN3+Rzn/scv/zLv8xHP/pRvvvd7/LNb36Tv/qrvwL4PeD/AT4F/KKU8msA+8HVPn6a8L3vfY8LFy6s/+0+8sgj3H///YyOjnL//fdX/H3uhuPHj3PixAm++tWv3pS56bpOV1fXtmvWqwEpJQsLC1y6dAmv10t7ezvd3d27Jhl8movbwk1EXW4WtxBcXkXFqyjoKMSyWRTbQRYtXJaDTyjEsxk8QikJvjsOVxMrxAtZJjKrSOngSIesVWS+kOXM+GjFPcN2HC6trNB85CjuSBTfNo68NhA3jSqCCyBRLNK7KT5zpOTL586znFmFdQEBBxQvQhYRsoDAAhSE3lrSC1V9JQJMC+F1C4Q0UfRQqTrdzoKVKf1r51AUtVTtVWMp1pU8mq+1+gQcE9Vdjj+EC8XdhNAbQPXiFNOo7jBOMYNjprDNVexCDCs7BwKM1fFScs7eiD2s/AKK7scuJDDTcxir1yjEx1B1D8bqTAXBBSUpA3ekA2N1ltzSVbLzz5OZO08hMUMhPoVj5rAKSYzkdbILL1KMT5CYOs/q9AUyiyMYqUVCLZ1YuRixybPrBBeAmU2gFGsLj6cWx+k48kaa+k6ge/zkYtPrBBdAemkSf7ipQrMKwLGLxKcuoOle3MFGWgZOY+QzLIz8GNvIkl+8XNL9qoFCOkawuRfdE8RILZFdHKkguADaBk6RWhhjceIiwdZKXTF/Yzehlh7ik6U1J7kwiuJtovXwG3GrDv7mXlwuBU9DO96GFpAOgfZBPI3HCPX/Yt0EF5QqqK5du3ZDscR2WKvmAipisFqVXFCKSYrFYsXz0k7I5/N4PJ71v+f9GGwf9WC/kus/MKSUvPWtb+V3fud3eP3rX7/n/dPpNKOjo+vElt/v5+rVqwSDwdeEFhZAKpVidHSU22+//RXVqlirMFlYWKC3t5f29vb1DOLZs2fX9YIAHvn6/8d//5uv7On4dx87wY9m6xcV72tu4XrZNrse+HWdtrY2rF2Ipf5oI4fa24hvaefrC0ZYymW4pbUDt692oAiAlPT5grT7QxSlg7mDm2KT5sZdZzWXDnh3reYCpIUolB4KpOIFxQeyWFH/KxEUEiMoYvflTqp+csvP7zoOStoZ+fjurliKHkaoPozkPLZpYGXr1/IC8DQOll2OSlA9UYSiY6brqyLztR1HFhM45u7Cxnq4F8dM4lib2jGFSvjAAwR77kWIG68wiMfjjI2NEQ6H6e/v31M5+5qldT3VXFJKnn32WQYGBghtcSlNp9PMzMxw5MiR9bGbs4jb4fLlyyiKwoc+9CH+9E//lDvuuAMoCTDruo6maczPz3Prrbdy/fp1hBAMDQ3xxBNP0NXVxalTp/jqV7/KkSNHePDBB3nXu97FQw89xIc//GFuvfVWfuM3fqPua1Hr2jiOs11wuF8S/9rEfgz2CqGvr49z587R1LSR9Dh06BBPPvkk7e3tzM/Pc99993H16tW6jxmLxbjvvvt44oknqtaYlwLHcTh79izHjx/Ht0f35puNtXU6FApx4MABNE3j7Nmz3HbbbTUfamvBkZK5fIZ/mb9GQNUwbYvZXAYViCg6I6sxnHKM4lIUDoQipEwDSxGYVhGtfG9o8/rxai4EsGDmkeVY6LN/8QVy+WrpgP7ePiZWYmiKQm8wgJXLMjMzjV02JPE2NVMoVldsuzUNwqXWtpZgkHg2S6G8z3+77y4+/paf2TRaAekgtuh2SaFBsVLLCkCiIdFwCtX3/6LjQVjV+5QuTBjbTINQSxpmZQdticA2Utj5JaqWEaHi2DaOVVnRpLobya2MVY8HNF8HucUrRsuZAAAgAElEQVQXKzcqGlbOqOnu7G4YIDm5pXUw1EFy9nLN44tAF+nFSn20YOcxFseeqXna3uYhEjMvonkCaG4foeZe0ksTGJkYTf2nqtoWNyPSdZyVaxeQUuIONOANNaN5gmjeEDOXvodjVceWbn8UI5vCKXcpBBq7CDZ3sTT+DNK2aB04xfJE9VxbD54kdm3DJTHY1E0xs4Dm8tLQdwuJ6QtIe+O7pmhu2gduJT13ibbhN2AsvUDz8bdgpibxtw+gutyE+9+BO1Jbf2s3LC4usry8zLFjx17S/rtha0X95cuX6erqIhisli4xDIPnnntu24r6zRgZGeGP//iP+du//dsdx+3HYPvYjFe/HGcfLxuEEPzZn/0Z73//+/nOd75Td4l7Pp9nbGwM0zQZGBiocEt8rWlhhUIhvF7vK6ZVsVZhMj09TUdHR1XrwNqCefXqVW677TaEELz77jfWTXK1RKIMdXRxou8Adxw5ji0dLNvGcmxsx8G2nfX/W07pPaf8/1MNDSQNozSuvK20v0Mmm0HTdSSlbKYtJUPRRl6M76xZNZGIkbeK3H/LMSaSifWM67X0KgGXzko2Q6dXXw+sqiAEM4UsIY8PVVHQhYpLCApGAVtVYBMRkbSLNAulLqLOBNxS7E5MCQ1Z9kgSTh6cfMmZUAuDlKXtSBRPGxi7iKUBwilQKoDd3clPWhk0b3NNAXqpeNHcUcz0Mvn4BkHljh7cM8llJGcQmg+nmMXTMEAhcQ1p704AKpobT7QPI3YVd7gXKdM1NfyhJEQvfO1Yucpr5Ap00HD4QfTAjZe/ZzIZRkdHUVWVY8eOvaSHOEVRUFV1vZR9J6JrTdD58uXL3H777RVjtzorrgVtu303tyu933wuhUJh/ThnzpxhYGCA/v5SwPrQQw/xD//wDxw+fJjvfve7/PVf/zUA73vf+/jEJz5xQwHWmsDpGuLxODMzM+i6zpEjR7qBQvmVl7LOntx97OM/CIQQvOUtb0EIwYc+9CE++MEPsri4uN7a09bWxuLi3vQvGxsb+Y3f+A0+85nP8Ed/9Ec3PMfNQu8nTpzYfYeXAZuTn1vX6YMHDzI6Olr3A7QiBN2+IO8/cJTr+QxXkzGiLg8XFue5YpSqdTQhaHH7uRJbJpc3afR6MR2HJp8Pn6ZjOg4Zw2C1kKfZFyCZyeJSFCgYNQkugHyZmLIch/FkuSqotQO/kBRTKaxtwopoOMLwwUGKSKZSSU62tnHh2iRZ0+R/PnWe//rGuwm416rqbRCiLJ+/6YBSUiuGEFgILBwtCFuE6F1KAcsVhWIChAvhCoCUJe3PYhJF82HlqsX8FVdDbZF+aaP5mjFTlSSXbcTwNA5UJM3W4dRwFHYsfC0HSU9Xkzt2YRXNG8HKbxBgZuo64b7bSV6rHu8LNVaRXJn5y+i+KGYuUTl9KfEGwhjNByjEJjHNDCvpDbOmre2Im+GNdKD7I7h8DZjZBIV0nEK6VE2kh9vxNvSQXao2IzCyCdoP3wPSYfX6CLnEHLnEhiB/fPZFGrqPEZu+tH5/DzT1Vul1pVdm6L31fjKLV9art9bnFm4hFG0ks3CFjsN3k198nsYjD5BdHqFh4DTe5sMEO++5oUr5lpYW5ufnicViNDZWu23eKFRVxXEcbNtGUZRtK7mg1HHU2dnJxMQEg4O1K+XWMDMzQ0/P7l0x+zHYPjZjn+T6D44jR45w11138eUvf5n3v//9O441TZPJyUmSySQHDx6koaGhWpTa5aKnp4fJyUmGhoZezqnXjYGBAZ555hmamppetjZKKSUrKytMTEzQ0NDAHXfcsS3JF4lEcLvdLC8v09LSwmBHF7ceOMhzk6WqHo+uM9DeyVBHF4OdXQx2dDHU2cVAexfhcvXXzUYikeDatWsVopO24/Ds/ByPj1zh38ZHSBq1W+Pm0ylimSwdwRDJQoGUWQp2MkWTsWSciMdLazhMxqm9JttSkijkaPIFsJElokzXUaTEhUBTFCzpYJRfnjqrgQxceOuRgtdbwdzsQmSDFUcCUg2BcOHyhCnWQXIhbVzBriqh9W0/2r9BctnShaMEUe08RnyKksZkJazc3gguKLlNehoPYRdzFeLyO84r3AWOibFaqnIzklN4moawMtU2065gB0gLu7Dp+giFUO/PEOp9E6JOZ8ztYBgGExMTNUXlXwpUVcW27fUgayf4/X6ampqYnp5eb0Vcm9Nmkuv69es3rGPx9NNP84EPfICpqSm+8pWvoGkac3NzdHdvtMB2dXXx9NNPE4vFiEQi6+tZV1fXurbFjWJpaYn3vve9nDt3bnNi4G+ACWAOyAghPr1fJr+Pnyb88Ic/pLOzk6WlJR544IEq91UhxEuqFv/gBz/I3Xffzejo6K4PcvWgoaGB2dlZVlZWKqrOXm7k83nGx8cxDKMq+bmGpqYmZmdnWV1d3dM67lIUev0hev2larc3tfUwlUnyz5NjzGdSXM6U7os5q0guXWoFk1IStm1Mx2a1aOA4DleuL7CaSZPJ5IjFa1c++fx+FpI12t2EoKCouFvaEKqKV0qEY+NYNrpbp7+jk/lslrHUBmkzkVzlWG8fV2am6YxE+LsXxvnPJ28tv6sCCmg6krKzolRAFkDb1JoodKRwkcnm8Pn8KG4NR7gw8mkEFqrLjaZ50ISKjY0sppBmJekj7BxCCyCtSukOaSVR9DCOWa3Xut03Wdmmmt4qxNDDXZjJ2S3bVzZpiG3AsQwCHUcxVhdQNDeOZZBPTKNskyA0Uwu0Dv8MhVyy1HZaNJCOheIJszJ5HtssEZZSShoPnGJl/AyRnlspxKrbd1fnXqRl8DRLoxvVXBJo6r+DlckLZFcXaOq7jYWRSnF7MzlPtOsotSiyQGMX6cVxgs295FM1qu0KWVamLhLpGMIqpAk29xKbPF91XTRPkNj0JaKdQxiZje9oY88xZGEZ7ALtB4/hCYA3ei+2mSLQcZTooXeiB268g0YIwfDwMBcuXCASidx0fb+tRkBbDXy2oqOjgwsXLpBMJmuuKWuol+TaCfsx2E8f9kmunwL84R/+Iffccw/vfOc7awYetm0zNTXF0tISvb29DA0N7RjMdXR0cO7cObLZ7HpL3quJzVoVNyOI3IpkMsno6Cher5dbb7214uF3OwwODnL+/HkaGxtRVZX/+4P/hVQux1BHF11Nza+4I2U0GmVubm6deIOSCOsdnd3c0dnN7937Jp6emeLx0Ss8OTlGrlipKZBOZcgrEp+q0ekPMpfdyDQ+szjH3apKYyCApmnEzEJV7nA2nyHi8aIplVVvFmCVWxhdQqHg2PgVFUVs5D7XjyVLCluyvK3uai7Vj6Q6qBMAdjnYFTqO8JLOZAkEgqiqKGlgOGvaXhtwuUMUd+3sEwhFx3YcCk4IaeYhPw9yjp3SM7aRwh3pxVitJsBqQXEF0Pwt5JZH0Hzbu+tsnpevZRgjUa29VVgZwdM0iJUpB7FCxdMwgJmaqhhbFEHaT/wK3khvXXPcDpvXnQMHDjA8PHxTWo7XgqxisViX22JPTw/nz5+nubl5fT0rFArr5hEA09PT64HQm9/8ZhYWqrPmn/rUp3jHO96x7efceeedvPDCC1y+fJn3ve99vPWtb30pp3dDSKVSfPCDHySRSPDII4/wW7/1W7z//e9nZGSkAPwflP4sFqWUN152so99/ARhTRi5paWFd77znZw5c4bW1lbm5+fX2xX3ankPJe2YP/mTP+H3f//3eeyxx27KGjc0NMSFCxdoaGh42WOJteTn6uoqBw8epLGxcdtzWKtkf+GFFzh16tRLPteg7uZYQwsBVefTZ39Q+aaUdPiDaIqCY9mEXTpLsQRPvvgCZnGDACuka+s22aqGtGsTLR6PB8pkpiMEKAoefwCf10vMNNFUFXuLTtd0KsnJg4NowLdHJ3nn8eMEdAUB/OvkJD97oJdUUSPkKber2uWKKGmASCCcPAKLkF+npCVaRHUpOI4XlTxgg5NFAKo7iLWNHpXmiVLcqk8rbTRPI2YNkss2kwjVW+UEbZsJhKIjncoEoo0HtyeElQsinSJCaGieCMLlRtUbyS1ewTazCCFwN/RTSMyQnX8OKV2YqdL9UtE8CEUj2H0bhdXrmKlFhKIS6DhOau55HEcSm68mrRp6TlLIpXC5fTiOs+5QmJ6/iuYNY+Wrz29l/CyhtgFSC2MEWvqREhY3kVork8+i6D6cTU6GAMn5ETSXB6u4kfQNtfZTzKcwMnFsy0BxeXCK1UlhVffib+hgdeoisRqtiwCN3cPEJp9h/soKLQN3kJq7RMfwXWAXMPKScFMbisvB09BCam4WPdhO++kPo7lvvN15DR6Ph87OTsbHx1+WYoXNFfXAjuvAWkX9Cy+8wMmTJ7ddz2ZmZnjd614H7Mdg+6gf+yTXTwEikQgf+chH+PSnP82f/MmfrG93HIe5uTlmZ2fp7Ozk9OnTdQVMtVryXm10dXVx9uzZm0q8ZbNZxsbGcByH4eFhAoFA3fvquk5nZyeTk5MMDAxweqg+B5GXE1uJt81wqSpv6OvnDX395ItFfjg1weOjV/jfU5OYts3F63PcHh4kZ1vkbYvBSAOjq6USbwlcz6Tx+H0Iq0hQ0/GpGvFiAbMcEEpgOZ+l3b/9jdoBCtIh59j41BpLkwBRUp1Yh4mGRxqldkkp2bbfTmsGa/sqKSFNFEXDI1ew0iuVRJRQES4/qupFqDpIiaI3IqWDdGyktHBsE2mZOLaBtPJIZ4MkVEwHewfx961QXLuTqIoeQPO1UIiNUcyVfg9CKDtfAm8DmjeIkdheJ8xYncHlDaNobhSXjpm6tuldQbD7XlLaMHMrRQZeYsGVlJL5+Xmmpqb2tO7sBWvVXJZloev6jmMVRalazwqFQkWJ/ezsLL29JVLvO9/5zg3N7fDhwwQCAZ5//nk6OzuZmdmonltbixsbG1ldXV1vu1zb/lKxplFz5coVLly4wDe+8Q2EEASDQf7sz/6ML33pS78E/BfgXuA/39AJ7mMfP2HIZrM4jkMwGCSbzfLtb3+bj3/847z97W/n0Ucf5eGHH+bRRx/d8QFqJ7zxjW/kL/7iL3jiiSd4y1vecsPz9Xg8tLS0VFWg3kxs1h3t6+vbNfm5Br/fv15ttrlC4qWgLxzhj+++n7+8+AwjqzFaPT5ypsmVlSWQkhbdw11dffx4dZWA20PKtilaFvlsBss0ae7opKmllXhshdVEAmlZOAi0cvyjqSqNgSBJ08CjqviDQZKZDEXLIhwMcLj/AIMtbbwwP0/A7aY32sBSLE4ql2VkcWFdL8zn95Gzing1nb88f4kPvf4eTNvmv//gx/g8Uf743/6Vf/rVDwEgNA1wELYCug52vpRss/Pr926Jiu5rBRykk8exCmClEXYW1deOnatRdW7XllLYSmKtb3ds9EAHxVwMFKVUkS1UkBKPu6kke2AZSKeIHurFWZ2hmJ7BHd7Q5rKNErkkFBeObZVF7gPkFjeE4j0N3eskl2MVyM4/jyvYSj4xj6LpCM3L6tR5AIzUAp5wC4XkUsVcbSNFcm6LHhhgFwuE2oeJz2ZqCvN7Qs1onjBL42erK83sIsGOQyRnL1Ue0zJp6j+JZRq4PD4cu8jq3FWKhRKBaGQSeMMteEMtrExdXN+v5eBJsrFZ5l/8d3RvCFX3rlefraG5/zZikxvk19LYOXpu+Rlyq4ukFsfpO3YXmZVp2m89jRACX1ML7Xf+15tKcK2hq6uLZ555hlQqdVP0ArdCVVVM06wrtvP5fLS0tDA1NcWBAwdqjtls/LMfg+2jXuyTXD8l+MAHPsAb3vAGLl++zKFDh/jWt75Fc3Mzzc3NnDp1as9tfuFwuKIl79XGzSTeNrdPDQwM1G0ZvhUvB/F2I3C73XR1de3a/+51uXhg4BAPDBwibRg8OTnG46NX8DuCrFoirGZzGbo9fuaNPJZ0mEgl6Ik24HLrFBybgmOjIGhz+8jZFinLZKGQo8ntw7XLdy1lW3jK1Vy7wULBQaLIctZRKpTaBASwifFxRZDW8o7Kji5vBKM6IQjSRpopLDYyqHbRwUhe23V+AN5oJ5k9kFxGchrVE6kp5qroQTRfE4XYOMVspSuNmZrD23yIQqxaT8LbNEQxM19yY9wB0jLQ/K1Y+UWc/Mb5at5GGoYfxB3pI1w2V0in0zXFRHdCLBZjfHycSCSyY8vvzYCmaevaELuV5IdCIUKhEHNzc3R1dVVpcs3MzFS1L+0Fk5OTdHd3o2kaU1NTXLny/7P33lGSXOXd/+dWVecwM909Ocfd2ZwlgiREMGCCQLKFXhBgjAH7YI5MOBYGjCUbEFiA7dcWwSCOwa9lLCEL2f4ZHLAxybC72iC0YfLs5Bx6OlZX1f390dOz09s9Oz27M7uL6M85c6Stqbp1u6a7+qnvfZ7vc56mpiZKS0vp6elhYGCA2tpavvnNb/LYY48hhOD222/nW9/6Fvfcc89VPWDDxQDrzJkz2O12Dh06xDe/+U1CoRBSSqSU80KI/wvsB34b+CMhhJBX25q3SJFfACYnJ3njG98IpDurvvnNb+ZVr3oVhw8f5u677+bRRx+lsbGRxx9//IrGF0Lw8MMP84Y3vIHbbrutYGP2y9HY2MixY8eorq7elPEyWJbF2NgYw8PD1NTUcNNNN214EaK5uXklE269RYb1CLrcvGfPQb4/PMDAwjw/mZuhyu0hHIlyz869vKS5jbftO8jTXWf47f/3NwD8/q/8KrXlFficLkqdTr514jj/dubn/OC+D+LTHBw/fnzl++eL//sDPvv97/G9d78Pn8fD3/Q/x+D0zEqGeVQa7KyuQSJxOOyUhwIM9M7TUlWN324jHE+wqOvMJeJMEqNrfpazs9N4NBvhpSU+8p1/YnZpiWgyicfhQKZzspCqG2GlEJqKVD3pBTLLIB6Zw+d1IDKdGhU3qt0FqopEAyuFmYlvViMEDn8zqeRiesELwEphpZbQPDUY0YuWDYq9FGnpGLFRFHvgksWsNJqjBD0xDCjoCwMrsZOUuRYR0krhqeokfOEYVio7M0qaCRSbCyt1UfBJLU3irWwjMtmbY/LuL2/OEbkiU704fSESS7lxVHj8PMGmvenSwMylUDTKGvexON4LipYjcGWw21RKardjd/kBiaknWZoZYqbvOBXtNzHR/dO8x8UXp/CU1aBoDipa9pGMzDMzcHLl93o8TPW2FzC9yvze4Q0QncnO0A/UdzLT978gJaGGXUTGngMEmt0Oaim1L3oXqn1rnh2EEHR2dnLmzBkOHTq06QuNS0tLdHd3EwwGC8qor6+v58SJE1RUVOR9XhoeHl5ZaLwSijHYLydFkeuXBFVVefjhh7n//vuZnJxkx44dPPLII1fVoaetrY2TJ0/mzQy6HpSWlmK3269YeDMMgwsXLjA9Pb0p5VM3csZbJBIpKDPN53Dwuu07ed32nfRPTfJ3fWeYX14xm7FSVHt9hJMJFvUkPbPTdFbXrLxOC5hLpVPzQ3YnAhiPLVHvK73stTCRJNbK5sqDLpw4ZcZFwSJrJVNqy34RArQAGGu3KxbCANVxsZzgMtjcgYJFLmnG1t8p+wDsvkriq0QuS3GhOAMYS8Okomt0WQIScwNo7hDGsqimaE6cZY0r3luXQ3UF0Jw+ErPncZS1YMTGEULgrX0hJa2vRlHTDyuZ4Oj8+fMcOnSooPf1alP53bt343K51j3malntDSGEWDfIam5uXvH2sywr6542PDzMK1/5ynXP+dRTT/G+972P6elpXvOa17Bv3z7+7d/+jR/96Ed8+tOfxmazoSgKX/jCF1b8dP7qr/6KV77ylZimyW/+5m+yc+dOAD7zmc9wzz338LGPfYz9+/fzzne+84qvRSZOmp+fX/G1mJmZIRAIkLpYmhwBkkDG+EIBip4QRZ73tLS0cPr06ZztwWCQ733ve5tyjsbGRu644w6+9KUvcd999131eKqq0tLSQm9v78o942qQUjI9PU1/fz+hUOiqFiFWzy3TofZqCLrc3NWxk4VEnDdt20VU11lIxLm5/uJD7x3bdvLNtg7Gw4u8/yWvyD7e7qC1LEjAlY55mpqa6OvrY/v27fz2zbcQ01M0BdP342ZfKYqEnukpJFDmdFFe4icZ15nV4wi7itvlYCGeoL6iHF8pxGNx5hLxldd+fmaag/WNbGtuQloWHTU1nJmZ5EhttqdQNK7jc6XLI1UtvaiiOQUpM4nNthz/CHXZOsGGIlMowkAtqcdImcvCWBKhOiG1CMYCquYkFc32DhJConlqsYwYIDATs2TiJGUNT03N4Vl2PM3ODDPjM3hqdhMdy86AslL5VgjTZvO+ut05nRad/hCRydwFOTM6hcMXIrla0JKS0upWpmKLWGYq5xghTTR3GZo7gMdXxtLMMFM96dJEX0X+zCBIy4Sa5mAyj5gVnsidWwbV5kS1O6loPcBk90/zxkCTfc8QatyH3eEgkYiRWBhFj12M6TSnB02k2Pcrr+Xcj/8Hh2ahqypVe15B2fa34A5tW/P8m8VanqRXQ6ZxWSqVorOzE7vdXnBG/bZt2zh//jwHDhzIuqZSShYXF7MsJNaiGIMVWY1YRyQsKojPE06dOsUf/MEf0NPTw3333XdVH9bVDA4OYlnWSmeK600ymeTEiRM5XQ8vx+qyzbq6Omprazd1VePMmTOEQiEqKys3bcyrYXFxkd7e3pwvkrWIxWL09vZimiaNLS38+/QIR6curgq6VA23qjESCfPS5nZU29rilENRafWW4NA09MvcexSg0uYsKJsLwC1jKOt8F0gEJCZAJlDWGDaZSJKcL6BFvGInMtVFobdI07ChhzdmWqk6AlipGKorRHy2F9Yw9r8Um6cCIxnG4atCWvnbe1+KK9SBvjSaVWbpqdqHv/E2nIG2vMf09PTgcDguawaaTCbp6+sjFovR3t5+WWPRrSKZTCKlLOiBbX5+nqGhIZLJJEeOHFnZfuedd/I3f/M3V5Wufj3JrCL+wz/8A0888QQPP/wwQ0NDvPWtb+Xee+/loYceagBuA/4YeERK+TkhhFo0Pr3uFGOw5xGxWIwXvOAFPPXUU5vSDVpKycmTJ2ltbb2qe+vCwgI9PT14PB5aWloK8h0tZG4nTpxY06R+K5iNRvnsf/0bD73uznXn9swzz9DR0ZG3VCucSvKFsycIx+O47HbKS/xIyyKSSDIVjTC/FAEBtSWlaJZgPh7DSBlIy0IAgwvztJWXE9aTLOk6lpQkdZ0Kt5fXtW/n3Mw0ZU4X79x3CFUmEZcISfF4ApctmZN5LiFtVm9GQDjQ8zXAUd0Yegwzmb0YptgDJBfzWBUIFcURJLnQm7M9FYshjfwNiVRHOfGZ7C6M9pKmHDErM5YRi2HqFz3DNHcZhqURnR7M2d1buzdvFpW3eiczg6eytpXW7yYZDZOIhokv5u9+qnkrV0omM5RUtxOeGsIyDTSHl2Q0dwE01LKf+dEeUom0CasEKtsOszDet5JVFmray9zQz3OOLW89SGR2BJJhZJ7YrWb7zTjtMWq37WBpPsJi30kC226jrOkgFXt+Le/r2Aosy+LYsWPs3r37qpIeUqkUg4ODzM3N0dbWttK5MWM+n/HpWo++vj7sdntWqbNhGNx+++2cOnXqMkfe2BRjsOvDtXW/LnLNGRgY4N577+X+++/nE5/4BN///vd59NFHSSTyf3FtlIaGBqampjZtvKvF4XBQU1PD4ODguvtKKZmYmODo0aPous7hw4epr6/f9LTd9vZ2+vv7Mc0b415VUlKC2+3Oa9y4Gl3XOX/+PM899xx1dXXs37+fQEkJ97Tt5O7WHWgifZ3ipsFcMkFHaZCR+bUzpQCSlsl4PJ115RIKXkXFnkfIsoCEVfj10sX6QblApldFLRMLB5bM/TvbnAX6rlk6qreu4Pk5fOUF7yulRPNUYnOH0KPzxKfPFyxwAZj6Ep7KXaSik+sKXIrDjzPQQnJxMEvg8tW/mNDut6wpcEE6+2FsbCzvZ980Tfr7+zl58iShUIiDBw9eF4ELWBG3Cvn8lZWVoapqjvg7PT29KQ+l14vM63nNa17D7/3e7+H3+7ntttt47Wtfy5e//GWA/wD+mnTLz/9v+bD87sxFihS5ItxuNx/96Ed58MEH2YwqlIxpc3d39xWNF4lEOHXqFBcuXGDHjh3s2LFjUwSuzZjblRD0ePiNIy9cd7/M3Lq6uvLOzW9zcFNVHa9r7ySwXDolFAWfy0XQ5abM56XM601bMcgUPq+bbbXV7GpqYFtdLbd0bsPudREKlNJcWc7+ujpagyEWDZ1vnn+OicgST55/jmhKB8WRVk8y85DgcjmJJ3Nvv+m7uAKKExQHtpIOFGcVwuZH2MsQjgBCUdE0G5o7uxuwZSwh1DxlrdLESkxh9zXkbHeUlKM6s+06hOYhpaazphxlLWiuix0+9cUL2H15vielibMiO5YwYvO4fPmNPS8VpDKomYUqISit340r2MRU30kWJ/rQbI41DUnLKrPL3DyBeqLzk5ipBNIyKK3Nb74+03+SYEM6q8fpL6estpOJ7p9llU2ql3ioqjYnFW2Hme57hvjCJKX1e3LGreq4iYWhk/jLK5ASrOg0JY0HaPvVP6Z8911557JVZDKozp07d0WfU8uyGBoa4vjx47jdbo4cObIicGXGt9lsmKaJZa0fUjQ1NTExMUE8frG8dWJi4hc6/oJiDHa9KGZyPc/54he/SFtbG694xcX07U9+8pOYpsmHPvShTTnH7Owso6Oj7NmTezO/HmRWJvbs2bNmWdTc3Bx9fX34fD6am5s31dMiH8PDwyutt28EUqlUli/FajKGs5OTkzQ1NVFZWZk342s4EubrXaeZS14UOercPhoDQYR2+RWbHf4AjlXeXCoCPR5HahrW8rEqgkqbo+AyT7eMrtmeOoOUQLR7+V8CNB9goqyymo8vzZKKrJ91FSxGoMwAACAASURBVNNtWOECsr4AoTqJz0+tuSoKIGxu7O5KUrE5jFjaJN/ub0hncRVyDs2BO9hKMjyCNBI4Qx0k59Y2mXcG20lFJ5CryjM1V5DQ7v+DK1hYqvzs7CzDw8Ps3bsXIQRSSsbGxhgaGqK2tpa6urpr3kk0H4ZhkEql0DRt3fnMzMxw7tw5brrpJux2O5Zlceutt3Lq1KkbouT4asisJmb+Ozk5yXe+8x3e8Y53fBGYBr4opby8+l3kWlKMwZ5nWJbFy1/+ch588EEOHjy4KWN2d3fj8XgKzjRNJBL09fURj8dpa2vL23V7s+jq6sLr9d6QWbCXm1vmHtkbWeC/p4YxV0QoSTgWZzoWXdl3W6gC45Iuzw4UZqNRZhKxlfHKFDt9szNoikK528ORympe296Z7uJsrTYp15gOL1KRx45JooIZRVhxEAKJgmXqWPolC4zChhQOjGQEaSXT5vXSQKJixGdQHWVps3mZ7j0tBUjLwtLjIJb7UQsAFT0ygzQTmHoUaV6MYYTmQg/PYxlJNGcJis2DonlIzI8jpYk0DaxUDCOxiOrwYaZAWbah0CNTpKKz6CkwEhFUhwe7txzF5kKoGrohlkUoE2mZIC1Uu5tkyiI8OZA3a8tfs52FkVyDepvLh+b04/QGQVGZHzmPkbz49/ME63F4A1m+WhncZdXYfSHCkwPosdySTHdpFanEEqpmJ1DXydzoefRo9uJi9bYjzA+eBEUl1LyP2Phz1GzbQUlFDfGpYRRF0P6GP8cVvH4VMefPn8fn8xX8OV1d3lxeXk5TU9NlM7V0XceyrIIy6hcWFhgcHFyJK3/605/yxBNP8Nd//dcFv54blWIMdm0pily/hCQSCW6++WaefPJJqqur1z+gAE6dOkVDQ0NBNdPXgrm5OYaGhti3b1/W9qWlJXp7e1FVldbW1mtmCC+Xzbp37tx5Q5jQA4yNjbG0tMS2bWlBY7VAUVNTU1BWWzSV4rGe5zi3cHFla2+wkqZgiFl9bUGn3O6kzpu/o4sCaEJBlxYeRSvYm8uGhUNG191PxifBvCRYUb0gBAo6FjYi4/nbP2chVGIL41ipyPr7Aqqrmshotu+LlGD314GE5MIgyGyRTnWWkYrOZWVZ5U7DjivURmpp7BLTV4GjtAE9PJK1v2LzYvdXoYezSx38jbdR1vE6FG1jgm+mHFfTNHp7ewkEAjQ1NW2pqfxGsSwLwzAKCrImJiaYm5tDSsnOnTuZnZ3l7W9/O//zP/9zjWa7dTzyyCP8xm/8Rr570C+2evf8pRiDPQ85ffo0733ve/nud7+7KYsAhmFw7NixdX20VpcUtbS0EAqFtly4L3Ru14NC5zabjPPDmTESpkHSMkmaBkbKYGBhnnKXB793jTIvKbFLhb6FWUwpcWs2PJoNS4BDUcAwORCq5sU1dWBGkFKyZBj4bTaeGhriNq+XUr/r4ntEWiB1EBoYSyA0hJUAITAtiZXM7SBtSRtG7OIzs9C8JMNDrHlr0fykItkxg+qsJDbTlROfAGiuGqLjq8r1FA3VHmBpJNfjzhlsZb7nf1f+bfMEsZW1MnvhWZKR7PLKkob9jHflliz6a/cw1Zc/PnNUbCc+cYnIJQSh5oOYpmS671je4wBKa7YxN3Ju5d92dwklNR0kIvMIxcbc8HNrHlvdeSuzF05iJNaOPw+96vXMTSywMHSaqrZ2AtUNREe70bzVhDpfRvWh69tQzzAMjh8/zv79+9dd9F9cXKSnpwe3201ra2tBSQIbLVvs6urC7/dTXV3N448/zvj4OB/72McKfj03KsUY7NpSFLl+SXnqqad44okn+MpXvrIpQU4sFuPnP/85hw8fviEyNwCeffZZampqCIVCxONx+vr6VrKprkfp1MLCAv39/TeMCf1qX4pMR8krESgsKfnPkQH+bbgPCbg1Gy9uasGppL/I5vT8Ru67SoLY1vmysyEIrcrmWl5fTN+Y5OobVPr/HAVlc0mI9uT/peICxUYyMkFqaTj/PqvRSolMnFh/P9JiVGJ+FsuIozpK0JwB9KWJlTbca+EItK207M4aT9FwhdpJRaew9KX859ScaM6SFSN6R6AVMz6DZVxcNdbc5ZTvfvNlSxMvx9zcHKdOnSIYDNLR0XFNTOWvhEKDrMHBQVwuF1NTU1RXVzM8PMxXv/pVvvGNb1zD2W4+kUgEv99PVVUV73vf+3jPe96zsighhLADUkpZeF1skWtBMQZ7nvI7v/M7HDx4kDe/+c2bMt6li1arMU2T4eFhxsfHaWhooKam5prGIOPj4ywuLl5Vh9qtYnx8nIWFBTo7Owva/7uTQ6hAf2SBkM1FWE+QFPk/pqoQKAg8ikZET+K12Ulg4dFseDUbHs3O48+dptLv466GesYSCU7Pz2MYBtOJBA/tP4Bd5AonEgWx3ERHCjsodrBMjHieDHTFiR6dAWtVHKY4sCyJEcvttqzYSkglFzD1SNriIfNanFXEpnPjEEVzY6YE+tLYiq2C0JzEZyay4oz0L1SS4cWsxThHWQORpSjR2Vx/MWd5R16/K+EI5M3kcvrLiYenkZaFojnwV7Whx2MsjPcghILd7SMVzx8rAQSaDiAUlURkjoXxnnQG2TKhpr3MDOYKd2V1O5gZPk9V825m88wVoOPQzTRsa+P0f/83pVW1BKsqWBw8h9PjZ8e9X8fhDeU97lozPT3N+Pj4mlU5q03l29vbN9xdeyMZ9YZhcOLECfbt28df/uVf0tbWxr333ruh891oFGOwa8+NoUYUuebccccdzM7O8rOf/WxTxnO73QSDQUZHN2auvZV0dHTQ09NDV1cXzz77LJWVlRw4cOC6eQOVlpbicDiYmppaf+drgBCC2tpannnmGSYnJ9m7dy/t7e0bXm1VhOBX6lt4944DeDQbMSNFMqmTlBZJaRF0OCiz5a70zF8m0ytDCokuLRQhUIRIB43L/1UVgbbyo6ApClI40qn/6Zmt+bpR13gPWHEwwthcAdB86NJDOKERSWoIzYsUWrZvgbmEYl/7i16ioGheVEcAxVaGu3InmruSVGSa+EzXugIXQHK+H3vJRRNOhILpqkNzlZKc71tT4AKQRgIrFUfzVKYFsaXhVYGnwN90O7Uv/vAVCVzJZJIzZ87Q39+/IozeqAIXsCJuWZZ1WW+IZDKJ0+mko6ODz3zmM/T09FzWXP8XBa/Xy4kTJ3jJS17CAw88wMGDB/noRz9KT08PUspUMbgqUuTa8Sd/8if8xV/8BUtLa9+/N0J1dTXhcJhI5GJmcSY7++jRtBn4kSNHqK2tveaLbFVVVUQikU17rZtJVVUVsViMcDhc0P63hqo5UFrBoUAVB8qraC4N0u4vo97tw6Nlx05VTg81Hi9+p5MSlxunw0HQ5cFps+Oy2fHa7VR6vUwshfnCuXN8e2CAgYUFhiMREobBVErHMHMFNIGVFrcAIXWEGQGZRHFWg+q5KI0LOwg7dncFaN60b5fmQZoJVC23252UaYFKtbmRlomw+VHdVaiuSiwzCiI7prKkilRLUFQwkwlUZwhHWStIibduV+7FkybemmwxMTk/REmoJu+1tjvyxxMlVU15tyfC01TtuB27v5pELMZU/2kWxnuWX5tFSXV+7y1FsxFoOoAExrt+wvzo+SyBC0C15V4vb6iRhYlBpGmg2PLPtbSmjYraKoQQNO/ZTXlNNQsDz2Gz26i6+d03jMAFUF6e9o6dns7OCEylUvT09PDss89SXV3NgQMHNixwAWiahqqqBfmjappGbW0tH/vYxxgZGaGxsXHdY250ijHYtacocv2SoigKn//85/nIRz6yaYbozc3NjIyMoOv6pox3NZimycTEBLquk0gkOHLkCOXl5dc9g+pGMaGPxWI8++yzTExMUF5eTllZ2VUbzm4rDfL+PTdR7/UzGr4o3iQsCx2LkN1J6apAYTIRw8qT/n4pEbPw+76l2JCYYCXAiqX/K03S+V+rbnfOisuOo8gECd1ENWfxaBFcagQjMYOVimBZZjrA1HwImx9XoAPVVYPqrEKxhxCqHykdmCkTMxFFj0yTXBwluTBIcrEPS1+/pDILaYGVQLW5cYW24fBVYDemsPRCgnKB3VeNYnOgRy+KqzZPBdU3/x7BzjtR1Mu3dr4UwzDo6+vj5MmTlJeXc/DgQZqbm0kmk8zNXb7xwPUmYyp/uc9fIpHA6XTicDhobGzksccee16IXAD79u3jscce4/jx47ziFa/g0Ucf5Q1veANCiA8KIVqFEIXVBhcpUuSqCIVCvOc97+Hhhx/elPGEEHR0dNDd3Y1lWUxPT3P06FEikQiHDh1a1zNnK1nP6P16stG5uVUbQYeTg2UVNLt8HC4tx6nacNsd1Hr81Lv9K5GGpigYEkzAabOhLmevKAj8dgeWovCS1nZs5P+7/MfkJFJZIy5Ts8udBBYKOhix5ZR3J5gJSC2kSyEtHTM+hZlcQJL28rL5WlAcIRR7AMVehmL3Y0THkKkINlcIMzFDammIVGQYMzGLK9iKzV2NzVOPzVuPQMFcuoCZmMfuqyS5OEx04jmkCZrdQ74KLGlE0NzBrG364kjOfgCJhfyL5vFYfouIYPNBhk7957J3Vu7fMjIznGMU7/SFcJbVMdb1U+bHegg07M5/zsVs4cdVWomeiJFKpOcy0fsMwaa92fNp2IkVm8Pp9abfW5ZkYeAsTpcb3dlAxe7X5j3X9WT79u309vau2DsMDw9nmcqHQlcnymnLXryFPAPV1tbS19fH8PDw80LkgmIMdq0pliv+knPfffexfft23v72t2/KeBMTE8zPzxec+r3ZSCkZHx/nwoULVFdXr2Qq7du3b9O6Bl0tw8PDJBIJ2tvbr/m5dV1nYGCAxcVFWltbCQaDK74UBw8exG7fmOCRD8Oy+KfBbtxuF0LN1dHdikrStAgbOm3eEnz29ev5K2wO7EphAbpiJVAv9dxaQYBwIIWCjI8j1twPTGkjMnG8kDMSD89hxGbX3xWweetyvLkuh1DsOAPNIFTiecoF1sJR2oiUqZVSRZu3GiOxQEnjrZS2v3rD4paUktHRUYaHh6mrq6O2tjYr5Twej3P69GkOHz583R6mCiFTtqiqat6U+YxHixACy7K4+eab+a3f+i0++MEPXofZbh1zc3OcPHmSp556ikceeaQbWAD+SEr53es9tyIrFGOw5zGGYfDCF76Qr371q5vWlObkyZMkEgn8fj8tLS03VHbt+fPnKSkp2TQv2M2ku7sbt9tNXV3hXZMTiQT9/f3E9CR6Qw1JmX5wtyyL6XgUn92R1zyhzObAuSrW+p++HgbC8zn7NQQClHs8VLk8CGlS5nBSomnUaxKPYoIZQ8jsjHjTUrESuaV8ptRIRcdytxsGVio3w05xlJNY6M7ZLi0XejhXfNI8tSyNZBu3OwLbMGILJOYGkKaOo7QBaVkYpiAZmcfh8pKcH8KIL2DYy4lMD+aM6wi1M3+JH5Zq9xCPRbGMi16lZfW7meg9iZQWoeZ9zAycyhkLoLLjBUz3pitYfBUtLC3OkAhf9JSt6jjCVG9+765gwy5SyRh2TxmTfSeRlyzAekrLUTVIhOep2f4C5i+cora1mWBNDZHxQYRlotqcCLuf4Cs/R1I36OjIn112PRkdHWVychJd1ykvL6exsXFFnNoMUqkUpmmuGYOtZmxsjFtuuYWzZ89etcB2o1GMwbaeYibXLzkPPPAAX/jCF1hcXL9sqhAqKyuJRqPXPC090+nj0pVLm81GW1sbPT1reDBdB+rq6pifnyca3WBGz1VgmiYDAwM888wz+P1+Dh8+vNLmV9M0mpub6e0trIvfemiKwp0t29lbGsKbxzQ+ZpkYWFQ4XCyl9IJWTzeWzeW4zJOhBJlAWDGEvQSTdAeifHNQRQqbu5C2xRbuUOFdcVKRERxlzevup7mCuELbEKpGYq6XxGxXQWWFmjuIM9RGKjq+InABqA4/1TffR2D7HRsWuGZmZjh69CjxeJxDhw7lbUrgcrmorq5mYGBgQ2NfazJli5mVykvJdL3J7FtXV8fXvvY1Eon1y2t/kQgEAtxyyy2ZzrvDwGHgVgAhxI2rUhYp8jxB0zQ+/elP89GPfvSqM5yi0SinT59eKcfevn37DSVwAbS2tjI4OIhh3HhVOS0tLQwPDxdUiWAYBr29vZw6dYpQKMTBvfvo8JWu+JC6NBs7yypo8pXR6Cmh3u2j1uWjzuWlxe0naM9ecN1eUYmW52HftCwWUjpj8SgLqRSzyRS9sQQ/jqToMtxE1bKcY5Q1GvUoWv5FXs25hnBg5f++09z5O3FaZq73qhGbJHzhOEYihuoMEBk7Q3TiHDI5T3jsHNN9xwjPT6OVtuCvaEa15xr4uzwluGv2opa14Qg04q/Zgb9mO+VtNxNoPkSw5RCBpv1MXXgOuVwZoKhrW24sTvSkO13W72RhaihL4AKYH+vNGz8G6ndiSYXwzDgT3cdyBC6AUFUZ+2+9hdY9N1FeanDwZS/BoUFsrA9FmpimhXCUsPc3/56GxmYWFxdvuBLecDjM+Pg4kUiElpYWWltbN1XggsIy6jNUV1fjdDr5gz/4g02dw41AMQbbeoqZXEX48pe/zNmzZ3nooYc2ZbylpSW6uro4ePDgNSkPXFxcpLe3F4fDQWtra97A7kbr/ri4uEhfX9+Wm9Cvzmy7XMdEKSUnT56kpaVlU9uJSymZSMbojYWJ5QkKpJRs95Th0DSS0rzsDafa7kIt8Fqp5hKKFbvsPhIwIiMIK0pCF9gcXmyKwepTWNhZGl+7I89qkrFkTifDNefnLCM2NbBcSpmNo7QJhEBfzDViVWwupBR5fbgUzYkj0ExyYSCrC5LdX09g+x24Qp0bfq8tLS3R09OD3W5f87O1mkwX0R07duD1ejd0rmuJZVmkUimklFkedIZh8Oyzz3LgwIGVbS996Uu5++67WVxc5BOf+MT1mO6msLi4iGEYnDlzhn/+53/mJz/5CUNDQ5kMvZ8DBnC/lPI/hBCqlHnenEWuNcUY7HmOlJJf//Vf561vfWvmYWdDZJrGRCIRWltbCQQCXLhwAdM0aWkpfPHlWjEyMkIsFrshM1gynXV37NiR9/eWZTEyMsLo6Cj19fXU1NRkxVPD8QjTegK7oqAui002IXArKjErHd/4lgUY0zJZMlNElr2forrOqbFheuYvZoTX+EtwubPFqUa3D0uA3+ak3O7CQZKdWvqYhCVwKGDE5/LGFinDxExkZ5wLWwl6eDDv61UcIRIL2QvEir2U+FT/GvuXE504k7XNNDQSs5cufAkikThWKltIc1bsAqGQmLuA6vCQMhUikz0opc2ER/OY3jtLc0QqAEWzE6jfyczAyZzfAVR13sLwmR/mFaoASqpaWJoaQKgaocY9JCJLzI6cB6C64wiTa2R6HXrZ7ZQFA6jLWXpz/V0kI4toWvo9IjzV7H/H/1spmYxEIpw9e5bDhw9fdyuVTHMuXddpb29HVdUtbSa2XkZ9hvn5ed7ylrdQUlLCBz/4QW6//fZNn8u1ohiDXXuKtZ9F+K3f+i1e9KIX0dXVlbczz0bx+Xx4PB4mJia2NC09FovR29uLaZp0dHRc1ghx27ZtPPvsszdM98eSkhKcTidTU1NUVlZu+vhSSmZnZ+nr6yMQCKzbIjvjS3HmzBkOHTq0addICEG100Olw814MkZfdJH4KkNPIQRhU6fCZsOBiirAkJJkHq+uuGng1QozxbcU97oilwBihg+PEsVplyCXsCwbQvMiZAqBiYKOzVuX01I7H67SyoJFLjMxj7d6F5GxdNmiUJ04yhowYnPo4bW7OlqpOHZ/LclUdEXIkghspc2gz5Gc71vZV3OXE9j2OjzVBxBiY3/PRCJBX1/fSlmt3+8v6DghBJ2dnZw7d26l5O9GRFEUNE0jlUphWdbK+z3jx5VBSomu67z//e/n9ttv5/Tp0+zdu3etYW9IMq/vve99L08++STJZJLGxkYOHDjAq171KlpaWrj33nv/j5Ry5emkGFwVKXJtEELw2c9+ljvvvJPbbrutYMsAwzAYHBxkZmaG5uZmtm/fvnK/ra+v5+jRo1RXV99w2Vy1tbUcO3aMaDSKx+NZ/4BrSGVlJaOjoywuLmY1KJJSMjU1xcDAAOXl5Rw+fDhvdku9y0uNw81sKknIls4oz/hwLRopbAhSSOKWiaqolCoqJVIikWB3sX97gOFImG/3nWc8FsGWx9PqQmyJkMOFIlJEjBQuVUO3StitLdJruNhlj6G4KrFiuaWJNoc/R+SSqTCKvQRLz1fNkfs1YOkL2Evq8vpoWXniDFegNo/IJSmp3sb8ULZtg2ZTGTv3U4SiIeVk2gkf8JeUkadCkpLK5rwil2Xo6LFcz1JfRRPC5iURi64pcAF4yqpx+iuYHe5i9Hy2oLXWqkNdaytejxOhCCwjxdLYMHo0vCJwqSVN7Ln3q1meYF6vl0AgwPDw8HXz/TQMg4GBAebm5mhtbc0qCaysrGRgYIDW1tZNP+/qjPrLdVvMXJuHHnqIN7zhDfzoRz/C7c7N+LuRKcZg149iJlcRAH7wgx/wqU99iieffHJTHkx1XeeZZ55ZMxi42rH7+/tZWlpaWbkshL6+Pmw22w1jIp25RkeOHNlUD6NwOExPT89lM9vWore3F7vdvmXXyJKS0USU/liYxLLYpQlBpy+Isup9J6QEAbplkglFNCGotDkLfn9qqWlEXkeMi0jAXBoAeWmJgkBo/pWsrvBYYdlcKV2QmCuw7FNoIDwoqo3k4hDSLLxhgyPQRmKmC0dpI5alY8YvBq6qw09p26vxN7wIoWzss7f6wSkT8FzJ/aCnpwen00l9ff36O19HdF3HNM2VB8uZmZkVvzpIr26+9rWv5ejRozz33HN873vf47777rueU75ifv3Xf52DBw/y+te/ntLSUlwuF2VlK+UuorhyeENSjMF+Sfj4xz+Ox+Phfe9732X3W51NlM8bMcPs7Cyjo6Ps2bNnq6Z8xVyrTPYr4dLsmvn5eXp7e/F6vbS0tOBwrO8huh6mZRGxTJKWuRKh2AR4Mlle0uInY0OcmJ7A5nGiCkG9y4MiYCAWpcRmp9SR/aDvUVU0AR1uO3ZVw6XPIJYdaRzJUTKW94nIRM58hL0MfTFfdpZATyzmZIVprmqi42dy9jZxoM9nL9LZvJUs9OV6m9pLapkZzB7DUVLL9HBu/OQsqWZmLHd+weYDjHcdzTPv5XO7fKTiS3iC9eg4WRo/tyKcBes7mR89n/e4YPNBEtFFFsdyPcnsbj+WoWMZF8sza1va6Dy4G0VVkZbJ0sQI8xPjeL0upARv0610vvFTed/rpmly7Ngx9u7de00FacuyGB0dZWRkJG9WYmaf48ePb1lm/loZ9av5l3/5F5599lkeeughvva1r9HZ2ckLXvCCTZ/LtaAYg117iiLXLyDDw8O87W1vY3JyEiEE7373u7nvvvuYm5vjTW96E4ODgzQ1NfH444+v/gBdFiklb3nLW7jzzjv51V/91U2bZzKZ3DRDVcMwuHDhAtPT0zQ1NVFZWbmhACnzZbJ///5NCVQ2g800oY/H4/T29pJKpWhvb7+iFr/X6hpZUjKSiNAfC5O0LFrdJXjztGiGtDGqruuoTgdBm71gA3rViqOY63cgtEwdK3oZHynVjZ6IY8RnkNJEmjpmKoEizJz3n7D5CQ9np8grdi+qzYdQHSAEWCnMVAwzuYjNXUF04lxBr2c1Nn8tNneI+PRFQ1ap2Am0vZKS5tvX9N9YC8uyGBsbW9NUfqNk3kc3UsOHfGRS5jOriiMjIwghqK2tBdJi3Sc/+UmefPLJ6zzTrWN5lfHGetIskqEYg/2SEIvFuPnmm/mnf/onKipyu/9KKZmcnGRwcJCKigoaGhrWXUA8ffo09fX1N4xNw2rOnDlDKBTakkz2q6W7uxtVVQmHwyiKQltb25ZknaVMg6RlkZASj6ogRFqW0pAIYCqZZCq+RJvXi0uzk7RM/uZCL1JKKty58Z1TUSl3e/CqNlyKiipE2vvISlEZPYtmxYknE1hCQ9XnSNnKsKXmAYVUYj7L5iCDFE5SsfHsjYpGcm6afLeneCQOlxjZS1zEJnMFI1xVhMeyhaYkPpLhqZxdHYEWZoezRTHN4SGVskgl8vtaVXfeSmxpjsm+3LLFyvbDTOcR3yrab2bozE+o7XwBk13/m3fc+s5D+DwW50+cpKqxnZ2HdgCS+Nw08flppGliKQ5UYVBx5J003/LOvONkmJub48KFC+zbt2/LRV8pJTMzM/T19RVkKh8Oh+nu7t4y+xnTNEmlUmuWLX7pS1/C5/Px27/925t+7huFYgy2dVz/uq0iG0bTND73uc9x9uxZfvrTn/LII49w9uxZPv3pT/Oyl72Mnp4eXvayl/HpT3+64DGFEPzpn/4pn/jEJ0gmcw0kr4S6ujrm5uaIxS5fNrYemTa2x44dw263c+TIEaqqqjZ8w1VVlZaWlk0zWN8MNsOEPpVK0dXVxc9//nNqamo4cODAFQlckL5Gra2tdHfnCUg2EUUIGlw+bglUs81TSuQyWUyqpuFyu9Op/paFSvpHQa78CFj5yWCJwkQ6oa4jwpgx7A4HRmwcMz6FpS8gZALLTCFRQXUhbH4UewCh2PHUHMTur0dzBRGKDUuPkIqOo4cH0RcH0JdGMBNpz4xUdBxX+faC5olQcARacZQ2YManScyewxnsQCgq/qaXshD8NdTKF21I4Mo0bDh27BiJRILDhw+v6du2EVRVpb29nfPnz99wLeNXoygKNpsN0zSxLItkMpklyg0NDa2bjfbEE0+wc+dOFEXh+PHcoHloaAiv18tnP/vZlW3f/e532bZtG21tbVn36YGBAW666Sba2tp405veVJAJcqFIKZmbm+Ppp5/m4Ycf5itf+QqxWAxFURBC3Fh1Q0WK/JLhdrv5yEc+gPLmPAAAIABJREFUwoMPPphzz5ydneXYsWMsLCxw4MABWlpaCsqQ7+jooKenJ2+DjetNe3s7AwMDBZlPX0sSiQS6rnPhwgXq6urYu3fvlpVVakLBo2qUajacioYDBbuioSg2hGKj3OFiV2klLpsXEDgEvDhQQcIy836vJi0TS0qWzBQJy0BfFq1mDYNJzy7iznqMkp3EgjeTdDeQ8LVjKg7AQnUuC6FCRdh8KI5yhD0AwoniqER1ViK05Uwjy8DmKc86t6K50TzVpNQyuMQz21mav4GP05srvgZr8/vIqSK3vNBIRgnW53q7KZqdUMthokvhvAIXQHR+PGdboH4nw+d+CkA8PJf3OICGplIq6uoob+igc38HVkpnrvcMsZkJpGkiJQhp0Pq6P11X4IK0AbndbmdqKlfc20zC4TAnTpxgamqKffv2FWQq7/f78fv9jIwUZsWxUTLi1lr3geHhYRobGy87RjEGK7IWRZHrF5Dq6uoVY2Sfz0dnZyejo6M8/fTTvP3tbwfg7W9/O9/+9rc3NG5dXR133XUXjzzyyKbMUwhBe3v7FQsmmZXLo0ePouv6pjyAl5eXk0wmWVhYuOIxNpOMF1ZXV9eGxQDTNBkcHOT48eM5HROvhvLyckzTZG5u7S/5zUIVCk1uH7u8Afyqhu0ywqVQFJJSImRa4MqIXSoWGubKj235RxOSREInEolhmAD5M8AEFsKxzmqylcBRkt0RUQiQZhxLX8RMzGDEJzBiYwhriVRkEiM+i7RSawx4EWmEUWxrewwomgNX+XZsngCppWFS0Ynl66Fh91VTd9sDhHb+Gtt27NuQqLS0tMSJEyeYnJxk7969tLW1bWppcTAYRNO0LQ/crhZVVVFVFdM0czy5Cgmwdu3axT/+4z9y66235v39Bz7wAV796lev/Ns0Td773vfyne98h7Nnz/L3f//3nD2bNtW9//77ef/7309vby9lZWU8+uijm/AK03z729+mvb2du+66i/vvv5+vfOUrWJbF5OQkwLuEEDeeS3WRIr9E3H333QwODnLyZPrB/MSJEzzzzDOMjY2xa9cutm/fXrBnF6Q73gaDwS17QL0a7HY7tbW1N0w33tUdEysqKtixYwfT09Nbek6hKAhFQUs/5CIuiW0VRU1vEwIUO6hudvj8lNvsaKtipRanhkOk86rEcnbVkmlgmjqGaWJIiS4lS/YqYrYSEsBC6R5ijnJ0xYNhpAAbejxCcmmKxPwA8dlzJOa6MZPzREZOsTR8iuh4P6pvJ6ZhR9h8IHyYho1kJM7SaBeL/UdxiBSRmRns/iaUZVHMSkXyvv7o0nzOtsjoaRx5xK/Y7BCeslyP38XxbpRVXq3eUAOat5KhMz9moucole035T13ZGbk4nmEINi4h7nJEeSyIDw31o3NmVuiZ3c68ZaWIrDYfXAbUkoWhnpXjgOQqos973iMUMeL8547H+3t7fT395NKrR8zbpREIsFzzz1Hb28vHR0d7Ny5c0MZ9q2trYyOjm5Zh+lM3JlP6BoZGSnGYEWumKLI9QtOJiC66aabmJycXDF6r6qqynxwNsSHPvQhnnjiCSYmcuv2r4SysjJUVWVmJtcc8nLMz89z/Phx5ubm2L9//6a1sc2ISt3d3TdMhslqE/pCkFIyNjbG0aNpL4IjR45QXV29qanEmWt0rVaANUXBrWoEbQ5CNjseRc17c5JAcgN/NsXmx+dWsYkkwoqBlVmV0Vjdd0Oxr1/Wa/cUJiBKM46namfBc7SMGK5QU8521VWGq3w7QrORXOjHTKZLL4Vqx9/80mVx603Y3Ol5rTYxvRyZgKe7u5v29nZ27dq1ZSWFHR0dWxa4bSaZe0sikcgq0y0kwOrs7FyzYce3v/1tmpub2bnz4vvh6NGjtLW10dLSgt1u55577uHpp59GSsl//dd/8Wu/9mvAlS1UXErm8/u9732PD33oQ7z0pS8lHA7zh3/4h0QiEbxeb0bwv2P5B7HRLgVFihTZFBRF4fOf/zwf+9jHuPPOO/nABz5AdXU1u3fvvmKz5ebmZkZHRzc1I2GzqK2t3ZRs/6vBsiyGhoY4duwYTqeTI0eOUFFRQWVlJYlEgsXFfIbs1w+huXlZdTOmnkQFbg5Usi/QwEvK6/EqCsqyoboEYpZJSqb/bZMp4ggSQrtoEC8EZtWLMatuxbJSeHb+JpqvEWEvRat7GQBmchZ/04sAcJS14yhrJTl/ATMRITpxlsRsP0b0YnxvxachFWPu/PdZGu1PZ4CpjotZYKuwy/wVDCXV+Z/1SyqbcraZepyy2m2U1e+ktH4P06MDLIxfbMCTSq793iqt7qC8/WYUV5DRnlMkIqtENykJNu7O2t9m19hxYBdmMoEiJFKazPT3ZpnYGzgoqdmGJ09MdznsdjtNTU2bWmmSEW5Pnz5NVVUV+/fvv6Iqj63OzM9k1FuWlfPMUYzBilwNxQv5C0wkEuGuu+7iz//8z3O6n4nlWvyN4nK5+PjHP87HP/7xTbuZtbe309vbW5BgEolEOHnyJENDQ+zYsYPOzs5N94byeDyUlZUxOpqnXct1oq2tjf7+fgxj7Y4vcLFsIRKJcOjQIZqamjbVtD6Dy+WisrKSCxcubPrY66EJBZ9mo9zmoFSz4bjkfh81Cxfe7A5vlmuEQCKsJMKKIqwoWEmQMt19UPVzube8sBLYfYUZ8ktjHs1VuA9KKjKGpzodUNn9dThD7UgjRnKhf8WQXtFclLa+ivqX/DHB7W9Ec5bkjNPc3MzY2FjeFbfVAU9lZSUHDhwouGvilZIJ3Hp6etbf+TqiKArGsvdbVkv4AjK51iISifCZz3yGP/qjP8ranmk/n6Guro7R0VFmZ2cpLS1dEdwy26+GzD33X//1X2loaOBzn/scbrebWCy2siBSU1MDsABkjICK3hBFilwHZmZm+PrXv053dzd79uzh+9//fubzecWoqkpzc/MNZdOQQVEU2tvb6erquubnXl0pkEqlOHz4MHV1dSv3/6vJst9qqpweOvxBAqMzVC536/NoNm4LVWJXFOxCoU412alFccq0yJNaw89aWiksVxVG3auQNh+arw6t+XWI0otlgGWtr6LipvsIHXwXmsOHv/4IqfhM3sY2lh7FW5MWFKSZZLH/Z8yd/xHSHmJhLszCQpSlJZ1oTLIUjuCpOYi3dj/uinYULR3rX7qgrdrdlNTtRrW7CTQfIthyhGDzIYIth/HX7UZ1ljLafZKx7uNIK/t1Lk705zU2LKluZ2K4l8jCHLGF/AvM0ekuSqrbcLmd7D28l1te/QpCVVXL181ibqAbZVW2fiKpQ9lOWl/zR3nHW4+qqiri8Tjz87kZbhthtcWL0+nk8OHDV9xEKEMwGMRms21ZZn6+skUpJclk8ooF/mIMVqQocv2CkkqluOuuu1bM4iHd7nV8PF1nPj4+nte8tBDe+MY3Mjk5ybFjhXWUWw+n00llZSVDQ0Nr7pNIJDhz5gznz5+nubl5S30QIC0GDA8P3zCrm3a7nfr6+jVT9zO19OPj4+zevZuOjo41u5FsFo2NjUxOThKPx7f0PGshhMCpqJTZ7FTYHPhUDQwTXUqsQoNOAShrv48EIKSOsGKo9lKkqSNxIhVP2m/rEhy+Aj9T0sQVLEwQE6odm68GxWbDU3sAIz6FvniBjKmrYvNS1vE66l/yIGUdr0G1r/168q245Qt4ysvLr1lXq6qqKhKJxFUHbluFYRj09PRw5swZ2tvbs4KskZERmpqaePnLX86uXbtyfp5++uk1x33ggQd4//vfvyVdiQol8zfu7++nsbFxpT34c889R0tL1mp5PTCbM0CRIkW2nGg0yic/+Ule8YpXcNNNN3Hq1Cm+853vEInkL/PaKJWVlcTjccLh9RuxXGvKysqw2WxbXhq4mkIrBTILojdiueeuQAVNVdVZMaNDc7HTX8Febxk1nkpstgC1cokyYazZwcJcjnOkaidhmailLUh3BVIoeBpfRemud2OoPmylLaA60DxVBDvvpP7F78dTtz/vmDZXbrZQyjTTXRpNHVOPkoovoEdmkUaUsXM/YaL3WRbmw0hHJYYp8NbuxlWxDc1fS3hxkdGuo4ye/SFTAz9n+MwPGT77Y4bP/IiJnuPMDf0cT1l+369UIkJJ5UWrCaGoBFsOMXahh8j8JA5v6ZrXuLmjhZbmcl7wK7dTXl+PsmpBOTI1SiIWI7MmZqIxH1U48taHcZZcmTAthGD79u1XXEWR8Vg9evQoyWQyR7i9WraypBJyyxaTySQ2mw0hRDEGK3JFbJ4BS5FrhpSSd77znXR2dvKBD3xgZfvrX/96vv71r/PhD3+Yr3/969xxxx1XNL6iKPzZn/0Z73rXu/j3f//3TckUamho4NixY1RXV2dlZqVSKQYHB5mbm6OlpeWqVxsKRdM0mpub6evro7Ozc8vPVwi1tbUcO3aMaDS6IvDF43H6+vrQdZ22trYtz7xZTWaVtbu7m717916z8+adixB4VA2XU+FMVxfehnp8bhe6rhONRtA0Gx6Pe/nL/JJwTvWCtb6xv1A1kCBTF/3apOZDqHaQOkKmEFYCR1k7yfn1M5Os5Cyu8m3Ep7NXqVVnGaqzBCEUTH0JMzGPEZvMTAJ7SR364giqo4SS5pfhq3/hygpnIQSDQcbHx5mamkJRFPr6+giFQhw+fHhTPbcKRQhBZ2cnp0+f5siRI5sWcF0tl7bQPnLkCFJKdF3HNE0URWFxcZFAIMB//ud/bnj8n/3sZ3zrW9/i93//91lYWEBRFJxOJwcPHswqKR0ZGaG2tpZgMMjCwgKGYaBp2sr2qyFzrZuamjhx4gTT09M0NjZy4cKFlTbcy9maFUCmzeeNlbZQpMjznG984xv4/X5+9rOfrXhuvetd7+Lhhx/mwQcfvOrxhRB0dHTQ1dW1ZV3Srob29nZOnjxJIBDYksz0DJFIhJ6eHhRFYceOHQUtpLa0tHD06FEqKys35Id2Lairq8uJGdXV36+2ErBMGpNjXBBpo3ghLfxC4lJV4hJiFmCmUBWJzUqC3YMUKorqwFFxMOeccjmzXrX7qD3yDiYVWBg8RmnrrSz0/SC9j3WxeZUzUEtibhSHuUAUwaVfL5GJ89jcflKxMEiLpelBlqYHsZU2Mj+a3XnRMnTKG/Yw2pW9AJ+IzFHRdoTofH6bFZe/ArsngJSCyaHzDJ07uvI7Q8/faKuhsYaSEm/aDu2Sjt7xhVn0SBhLpq9FyhQ0vPxDuEa6sOfx8doIbrebyspKBgcHLxVBLks4HKanpwen07llXa3tdjvNzc309PSwY8eOTR9fURQ0TcMwDIQQjI6OUldXB1CMwYpcEUWR6xeQH//4x/zt3/4tu3fvZt++fQB86lOf4sMf/jB33303jz76KI2NjTz++ONXfI5du3Zx6NAh/u7v/o63ve1tVz3nTNe+np4edu3atZJdMjY2RkNDA21tbdc88KqsrGR0dJRwOHxNxaO1WJ0ev2vXLgYHB1lYWKClpYVgMHhdAtNgMMjo6CjT09OUl5evf8AWoygKrY2NnD59Go/Hg2EYtLe3X95nQFGR9lqQKZA6WMv/lamsnGCBheqtx4ysKtE0lli2tECqboTmwu7ykJzPHKmAULCkRKAgVDVd+igEoGBT7Zj+eoRqR1o6RnwOKxXGSq21oi5w+Kvx1b0YX+0RhHpl2XrV1dWcPn2a8vLyLQt4NoLL5aKqqoqBgQFaW1uv61xWt9C+VPwTQqyY0Espr7jsG+CHP/zhyv8/8MADeL1efvd3f3clc2xgYIDa2lq++c1v8thjjyGE4Pbbb+db3/oW99xzz1UtVGTIzP2tb30r3/jGN/jkJz/JF77wBWZmZti3bx/xeJwHHngAoB84BSBlnj7yRYoU2TJ+53d+J++2F77whfT392/oYXctfD4fXq+XiYmJlTKZGwWHw0FVVRUXLlzYlNd6KYlEgv7+fmKxGG1tbZSWrp29cymZrtw9PT1Zvj43ApmY8fz58xw4cCDvd5XiCKAmx6n9/9k77/C26rP930eyvLflLduSJY8sxxvSluGEUMoIhV9JoC2Bl7YUCKVQKGUkJCFlJCT0ZV0UKLQBSngDZb0ljBJIIXlJbMd2EsdDw0O2JNuyhrXnOb8/XAk7XpJ8JB3C+VwXf6Acfc/Xx7LOc55x36QedopAOmEHx+sCuNlI5HCRQbpAeLXgkpMP4xQAChy4iDliBoILipy8N3Ji4pFTuxFpovMRn1XqT3K5LFrEpeYiuaAMGSW1kH/yNLxOM5JzRLCM9Z2xIIW0PDHG+6a7IMbHz17YM2mk4MTEgvRMn8Iwj81tYEBRgNPlxYiibca/6dRyxCamw2X7pri5ZHkZisrEIAgCbvt0TS+Pww7r+AhIkkJiTjnya36M4nM2gCJJ5K+gZ+qhuLgYra2tyM3NXTAR63A4IJfL4XQ6UV5eHrKzeqD4Job0ej0yMwOX5AiUmJgYeL1eeL1eKJVKFBcHNhExG2wMxkIsMGvOZhO/w+j1elxwwQX417/+RUsSiKIotLe3IzU1FVqtFnl5eSguLg5r5W4hzGYzenp6UF9fz4jqptfrRWtrK1wuF8RiMe2C8qHgcDjQ0dGBhoaGqP6ugMnOv/7+foyMjIDP5y+umkRRMxJfFOWBW39qwbd6yRjYDT0LHgcAnDg+LJqTc/wrAV5KIeLTxYhLFyMutSTkxBYwvfMvPT0dLpcLlZWVIa9HJyRJorW1FUuXLo1a+7jZbIZUKkVcXBwkEsmsyT+SJOF2u6FSqXDPPffg448/nnfNd999F7/5zW+g1WqRnp6O6upqfPLJJ9OO8QVY99xzD4BJfYY777wTXq8XN910Ex588EEAky3t1157rX+M5vXXX6dNk/Dpp5/GPffcg9jYWNhsNixbtgwEQcBoNGJoaOgiiqIO0nIiFjphY7DvMJ999hmefvpp7Nu3j5Y4wO12o7W1NWpdvfNBkiSam5uxcuVKJCTMFCkPBY/Hg4GBAYyPj6O0tDTkMX1f7FpaWhpUgixSdHd3Iz09fd7kJWlsBTix4MamgvQ4QIEDEjzAa5shfUGBgIGbiwROHDhcHkBR4FBOkEQsQHAQQ9rAOeM6kh47jNIDcBhUyJSsQnxGMQACHocB5uF2DB17H5zEAozJjs7YW4qgBpqer6e/SHDg9HLgtptnHJ+QLYFOeXrG67EpubDo1NNeyypeBlV/FwoqzoGq++sZ7wGAhgsuhNOsRfeJLlTXL0du8Td6TW6HHbGJk/GK1+PGxHA/nE4PbAQfP7r/w1nXo4OJiQnI5fI5k5dTP9tisThiUzDAZJx54sSJsD0TkCQJl8uFN954A06nE3ffffe8x7MxGMtcsEkulnl5/vnnIZVK8cgjjyxqHYqioNPpIJPJ4HK5cO6559IuKB8qPT09SE1NXbTA62KgKAojIyMYGBhATk4OxsbGGBWIDg4OwuPxRK0LZ2rnX0lJCXJzc9HS0kJrQOyDIl2gXEaQLgMotxGUawLAGYUVggeLTj6ZJAsEbips2smkWExizmRSK0OCuDQRODGL77KaOvYrFouRlTXpuNjW1gaJRIK0tJki9dHAZDKht7c34kllh8MBhUIBh8OBsrKyBZP2JEni8OHD2L9/P1555ZUI7TL8yOVyvP766xgYGMDExASKioqwYcMGfP/7349+hp9lNtgY7DsMRVH4yU9+ghtvvBFr1qyhZc3h4WHY7XaUlZXRsh6d6HQ6DA8PL1oegSRJDA8P+wWmCwoKFj0mb7Vacfr0aTQ0NES98HgmvuRlfX39nFqtpFMHeMwA5YLHqgQ3LhOkywhuXBa4vJkxlIcEHJwU8EgHYB0EBS5ikgQgYtMBggMOwQEFwG8ER3lBePQzz+uxgyAtIL1uDB8/CPmRf8w4JrlgOUakrTNeT8ipgLa/Y8brWaUNGDp9eMbr2aUNUPV8k0RLTM+D1W6Hw2IEv3gJxpXdM96TmJyIpksuwKhqBCkpiUg6I1byupyIiU+E226DSaPEhNkJg8mDi+54Fdml1TPWo5Oenh6kpKRMG5kjSRJqtRpDQ0O0fbZDQalUwul0hu17xO12Y8eOHWhsbMT69evDco5owMZgkYVNcrHMi8fjwfe//3288MILKC8vX/gNs+CbFY+Li4NYLMbQ0BCSkpIWPetMF4EECOFEp9NBoVAgLS0NIpEIsbGxjAtESZJES0sLli9fHlZDgDPxOSD19/fP6PzT6/VQKpX+kd3w7YEE5TaBchlAuo2gXAaAdMFLxMM+3jnzeHDAjUkEwUsEJ2byPyImCRTiEJ8hBjeWvnbyqbpSxcXFKCgomBaA+wLz+vp6xmhhSaVSJCQkTHO3CRderxcDAwPQarUBV/J9VftNmzahpqYGf/vb38K+z0jidrthNpvB5XL9yU+CILhsizwjYWOw7zh9fX245pprcPDgQVo0oSiKQktLC5YtWxbRe3mgnDhxAgKBwF+oCQaKojA2Nob+/n5kZ2ejpKSE1kKhXC5HXFxcRO5dwaJWq2EymQLq3PaYZSDtKhCUGxQFxCRkg8ub7mDnMKkBwj3NnYz0UiCpeCRklcJjN4LHI0ERPCBu8noQnvEZ5yK9ThDeSXkGiiKhOPIulC0fIzErHzbdpFFWXEoOdJqZxlTpJXUY7pqZzEorXAKNfGbyK0fSCJfbOylDQRAwGcahV0+6ivLiEpGekYzMjCQUFheAFxsHkqLAjYlBDI8Ht90GXsJMFz+KJGGdMMKhH0VK2Y/Q9tHLWHHpb1B39X2zXls68Xg8aGlpQV1dHXg8HsbHx9HX14esrCwIhcKoFsEpikJraysqKytpH5EkSRJvvfUW7r//frz44otYt24dretHGzYGixxskotlQQ4dOoSdO3fi7bffDqqCZbPZoFAo4Ha7p+km+b64o5VUmg2VSgWLxYKKioqIndNsNkMmk4HH40EikUzrSPLdQJYsWRJVZ5CpGI1G9PX1oaamJiKVTIPBALlcjpSUFJSWls4a4J86dQp5eXkR1QujKArw2kG6DHBZR8HhJfuTWS4vBydOdaOhoTGso50+F52+vr4Fg/m+vj5wOBwIhcKw7ScYvF4vWlpawqoVRlEU1Go1lEolCgsLA3YYGh4exrZt2zA2NoYnnngC1dXVjKvaB8KBAwewfft2nHPOOSguLoZQKERxcTHy8/PB5/Nn63789v2Q3w3YGIwFmzdvRlpaGjZt2kTLegaDAQMDA6ipmd0dL5rY7XacPHkSDQ0NQRVmfPFCcnIySktLwzIp4PV60dzcjLq6OsaJ0FMUhePHj6O8vDwgeRHSNQGPoRUESJAUwOEmgJfIB0FwQJEkHBN94MbMHp+7bDa43SRS+IWIiY0HxcsCYtIB19iM+yVFugHPFDMfioJW1oK0wjIcefFe/+tEYj7MY/1IzODDZphMlqXklUOjODHj/LFJ6UgqqILDbACHwwVFUTCOKWEaVyGv4lwMdc0cS1xWVQ5BiQAxsTwQnJmxmdthAy9+MslFer2wG8bhcTrgdTlhMFqQW30V6tbvwFhfG/glK6c5LYYTrVaLoaEhUBQ1r8xCNLBYLOjq6qKtu5GiKBw9ehQPPfQQqqqqsH37duTkBOhozjDYGIwZsEkulgWhKArXXXcd1q9fj0suuWTB410uF/r6+mAymaaNTk1FrVbDbDZHNKk0H5FMKvl0k3ytvnMFJAvN5EeD06dPg8/nIzc3N2znsFgskMvlIAgCEolk3mqz0+lEW1sbGhvDm1QKhnCPdk5MTEAmkyExMRGlpaULBjy+LryqqiraRztDha6xlLnWVigUSE9Ph0gkCiiRbjabsWfPHhw8eBBbt27F5ZdfzpjOt1B47733sH37dpAkiZGREUxMTMDlcoEgCCQnJ4PP56OwsNCv5bF79+7vURQ1u2AJSzRhYzAWWK1WrFq1Ch988AFtD33RKBAFSn9/PwiCCKgwM9UxcaF4gQ7Gxsag1WoZJ0IPBK8xS5IkvIY2UG49CIIASZIAuADBBYfwzPk+l80El20MFEWBIgnEphQhpeh8wK2bmeSiSMCtm7GG22GG9Iu/Y7S3HZnF5ciuOAek1w3T2DAUX38KAOBweSCS8zChlvrfl8wvhsVsQWxKNtSy9hnrZotWQn1Gl9eK6iUQVkgAAB6nHVzezASlx+lATFw8KNILk1oJj9MBAPB6SWjtCbhqx1cRj8N9Mgu+TvTFiLCHC7lcDh6Ph5KSkkWto1AosHXrVrhcLuzcuZORf1/BwMZgzIBNcrEEhFKpxJVXXonPP/98zgqZ1+vF4OAgxsbGIBQKkZubO+dNgYmdSuFOKs2mm7TQebq6upCZmYm8vDza9xMKLpcLx48fD4temNPphEKhgNVqRVlZWcACr0NDQ36tJSbgE1inexzEbrdDLpfP6IwMBIPBgP7+/oh14QVCZ2cncnJyaHto8z3scLlclJWVBZTQ83g8ePXVV/HCCy/g17/+NX79618zprt0MVitVphMJrhcLpjNZkxMTECn00Gr1UKtVkOlUkGlUmFsbAzt7e1wu92/oyjqT/9pmfdGe/8sftgYjAUA8MYbb+DgwYN45plnaFnPZyjT2NjIuIS+T4R+vm7fxTgmLgami9BLpVIkJiZCIBAE/B6PVQXH2NegPHZwYxPhsIwjMV0Abszs3WoW7SBATNcj5aVWIjFD4O/+8rkTAwDpnNnhRXpccJhHQFHkN7peACz6YYwqetF19GvYJ7TIKqmGzaRDWnYhABIqaRvcDhtiE9Pg8TjhsH3jaMjhEIiJTwHgQQY/C8P9SixdUQbx0m9GOD0OK7ixMz9TXrcLBDcGZs0QPI5JR0WTxYFhjQlXb/8EmYLIGficKSqfkpLCGPOnM1msYYTBYMCuXbtw9OhR7NixA2vXrmVMjLoY2BiMGbBJLpaA2b59O3g8Hu68885pr08VQgxmNIiJnUrhSCqdKZoizALiAAAgAElEQVQejGNiOJNKoTI8PAybzRayRtuZTNVNEolEyMnJCerzcLYnTKcmRyUSSUhaJcDkZzsjI4Mx9vG+z/Zix5ZdLhcUCgUsFkvAyVGKovDZZ59hx44daGpqwoMPPsjIB5ZQmPpwMRsejwculwtOpxM2mw16vR5VVVWZFEUZIrhNlsBgYzAWAJNxRFNTEx599FHaxgyD6ZiKNFqtFiMjI1ixYsW01+lyTFwMTNS69DFVxymYkUqSJGEdPgiX8T/i7BwektKLwOVNL2pTJAnzmAycmDOSLQQHiTl14HJ58NjGQBAecOPSEJOQA8plAHHGZaJIL+wTqhn7cFr1ID12uJ12tH/6ISwTdug0g9OOySkswIrzzgOXQ0CnUWF0WA2H1YGCwmzo9SaIV6yAdliJuFge0rOnuw667VbExM3iquz1Qjs4AA7pgtvtgdHihcHkQtPNT0NUe3HA13ExTH2WEggEKCws9H++hoaGYLfbaYu76SSUIqrL5cJf/vIX7N27F7/97W9x4403MuYZZ7GwMRhzYJNcLAFjt9tx7rnn4t1330VeXt40XSA+nx+SEGIkxt+Cgc6kks8xcXBwELm5udNE04OBaSL0dCWVKIqCSqUKOjk6GyaTCVKpFHV1dYxJmC7WtXOqQ9RsovLBEm2DhdlQq9WYmJjAkiVLgn6v1+uFUqnE6OhoUMnR06dPY/PmzcjIyMCjjz6K0tLSULbOaH71q1/htttuQ01NjT/gcjgc8422MuOPhuVM2BiMxU9bWxvuvPNOHDhwgJbkSiAdU9Gkvb0dQqEQGRkZYXFMXAxyuRyxsbGMHCEbHR3F+Ph4SCNfpNuKCflbIN0mUAQXiakF4MV/E+e5HVY4LZoZ76MoCjbdGEi3FRRFghefjoySFeDw4sHhJYL0OAHSDYITg9jEDACAVT84457tsk3A67b41xztk2JCp0d8YgKGezohXLoEGfnfxEJOsxGcWTS2XFbTtA4xHx6HDdzY6Yk7w5gW8q4emIwWUAQXy9fehNOfvoTS+ktxyV17g7yCweNzn1coFHOKyodT6J0Ouru7kZ6evmARlSRJfPjhh3j88cdxxRVX4N5772VMcZpO2BiMGTCrBMHCaBISErB582Zs27YNn376Kc477zz09PSguroaEokkpKRQWVkZ+vr64PUyozszNjYWAoEA/f39i1pHr9ejpaUFJpMJtbW1EIlEIbcZFxYWwmg0wmKxLGpPdEEQBCoqKtDT04MFkuSz4kuONjc3w263o76+HsXFxYsKWFNTU5GcnAyNZmbwFS3EYjEGBwfhcrmCep/PIaq5uRkejweNjY0oLCxcdPKOx+NBJBJBJpMtah06yc/Ph91uh8EQeAGLoihoNBo0NzeDIAg0NjbOOxrtY3R0FHfccQfuvvtubN26Ffv27TsrE1wA8PLLL8NonBT79V2XK664Ap2d091AtVotXnzxRRAEwSwVZRaWbyk33XQTcnJysHz5cv9rer0ea9euRVlZGdauXev/vqMoCnfccQckEgmqqqrQ1tY279q1tbVYsmQJ3nrrLVr2yuFwIBaLIZfLaVmPbioqKiCVSjEyMoLm5ma43W40NDQsqiBGFyKRCCqVCk6nM6r7mI2cnBw4nU7/PSAYvIjFOPd7MLvTAcoLm1EJq04JivSCoki4bLPfq902C0i3BcDkA73HOYGx3sPQD3bB65yAcbgHNsMgrDoFnBYtAICY5RGUmPJ7JQgCeeIKVDSuQr5IhNqLLkJmwZmx0Fz3/dlfnyo6bxjXoeP/jqHj2HEYDGZQ4OB7v3gW/PqfobThUixdc8O814oOzGYz2tvbMTIygpUrV875LEUQBJYsWYLu7u6Q4u5wI5FIMDAwMGe8S1EU2tracMUVV+DAgQP43//9Xzz88MNnZYILYGMwpsB2crEExenTp/HDH/4QxcXF2L17N6qrqxe9ZriFuoNlMRbbZrMZcrkcXC4XEokEiYkzLYlDgYmjnaF0KplMJshkMsTFxUEsFtMqhB5qm344GRkZgU6nC7iiOlVUXiwW0+4QRVEUOjo6/NVxJmCz2XDy5MmAtGECcdycbf1nn30W7733Hu6//35cc801UX9ACifj4+PIz8/HqVOn/HbyRqMRmZmZ6OzsxNKlS/3HHjp0CKtXrwZJksz4UmE5EzYG+5bx5ZdfIjk5GRs3bvQ/0Nx7773IzMzEfffdh8cffxwGgwE7d+7EgQMH8Mwzz+DAgQM4duwYfvvb3+LYsWPzrq/VarFmzRp89tlntDwg+u4JIpGIcSPbBoMBp06dQkJCAqqqqsLimLgYxsbGMDY2Ni2hyRRsNhtOnToVsEvlbJ1ydm077COHAYoCQCA2MdPfZXUmdqMOHvvsSTW3ywvSQ4KiPCA4HKTkiZBVshIepxkUOb3A7XHZ4LbPTKS57WZ4nNYZr7ssplljYrfd+p99n7kXJ0aHVBjuH/Trebk9JEBwcOndf4ewek1EYiSfqLxPTzYQR0yA2R2EY2NjGB0dnTFirFKpsG3bNoyOjmLXrl2M0oYNB2wMxhzO3kifhVbUajVuvvlmbNq0CY899hgIgpjxRRYqRUVFGB8fh91uX/jgCEAQBMrLyyGVSgOumDgcDpw+fRpSqRSlpaWoqqqiLcEFAGlpaUhISMDo6Chtay4WX6eS2+1e8Fi73Y5Tp05BLpejvLwcy5cvp93pLyYmBiKRiFFV6dzcXLhcrgU7lXy26X19faisrMTSpUvDEtATBIHKykr09vb+x0kp+iQmJiIvL2/e7kmbzYYTJ05gcHAQS5cuRWVl5YIJLq/Xi3379mH16tVITEzE0aNHsWHDhrM6wQVM6uzExcUhLS3N/5pSqUR8fPyMoH18fByZmZkAAOJsjjpZWCLE+eef7/+b8vH+++/jhhsmu0JuuOEGvPfee/7XN27cCIIgcO6558JoNC7YjZydnY2bbroJe/bsoWW/ocQ74cZisaC9vR1KpRLV1dXweDyMfCjOzs6G2+0OqhM5UiQmJoLP52NoaGje4yiKwujo6LTOcV+nXFJuHTi8LCQKLgY4PJg0clBzxA2ke+6OdbdtAk69Ei6DGk7dMLSdX0J66A04bWZYjaMwjylgVHXC47KBw5l9IoQgZp+EIOa4n0/t2CK9XoxrRnC6uRX/98kXkJ/ugd1qR3H9OgBAw4/vwqbXRyGquSjsMZLH44FCoUBHRwdycnJQW1sbcIILmOwgVKvVjHlemkpOTg5eeeUVvP322wAmi/7bt2/Hhg0bsGHDBnz66aeMKtSHCzYGYw5nd7TPQgtvvfUW1q1bh8suuwxffPEFrr/+elRXV2Pfvn20rO+zfpZKpQsfHCHS09PB4/Gg1WrnPc7tdkMmk+HEiRP+G9bULzY6kUgk6O/vh8czt7VzJOHxeBAKhVAoFHMe43a7IZVKcfLkSeTn56OmpiasegK5ublwOBwhtemHA99o51wBk+/6nDp1CoWFhaipqQl7+3ZCQsKCSaVIU1xcDJ1ON2Mk1+12o7e3F52dnSgqKkJ1dfWC3ZUUReHw4cO4+OKL0d7ejoMHD+Lee+9lpOZMOJBKpeDz+UhOTvY/tMpkMmRkZMz421MoFODz+b7/ZeMBFpYwMDo66teqycvL8xerfF0zPgQCAVSqmWLcZ7Jp0yYcPHiQtu/wpKQkZGRkBHTucOJwONDV1YWenh6IRCKsXLkSqampKC0tZdSYvQ/f/V0qlTKmaDQVoVAIjUYDh8Mx678bjUa0trZCr9ejpqYGpaWlM2Q1Mpf+HIn8pYhJKkbh+dsQkzx94oL0emE3jIL02Obch/eMBBhBEKBsevR+8jcMHPkApjElSI8TppFeuJ0WUNTkfdxtM8Fu/M9o4xxyHxzu9KSY2+Wa1ECK4cHldEIpk6P1iy8h7TgJ47gOHpIERQFLL/41LvnNS6j/f/ej4Sf3T1sjISEBubm5GBgYmPNnChaKojA8PIyWlhbExsaisbExJNMELpeL8vLykOVCws2dd96Jhx9+GH/+85+xdu1aCAQCHDt2DOvWrTvrC4w+2BiMOZwdVgYsYeWiiy7CVVddNW1O/OGHH8aFF16IdevWBVWFmIusrCwMDw9Dr9fPqIJGi7KyMrS1tSErK2vGjf9MUXCJRBL26kRsbCyKiorQ19fHGIeVvLw8qNVqmEymaZ+DqY6SxcXFKCsri0j1xleFC6ZNP9wkJiYiNzcXg4ODEIlEAKZ/fkpKSiJ2fXwUFxejtbUVeXl5QY/khgMOh4PKykr09PSgrq4OFEVNcyQtLy8P6PrIZDI89NBDAIBXXnklJEH7bztyuRxFRUXTgim5XI78/PwZib6BgQGUlJT4/petIrKwhBmCIGjRV3z00UfxwAMP4I033qDl3iESidDS0oLc3NyIG5Ms5JiYk5MDlUqFiYmJsBURQ8XXMTU8PMy4ETKfbIZUKkVVVZX/davVCrlcDoqiAjYQSileDYIgkCq6BPrTfwPltYITXwCbXgarqg/xGVngzJKIctssIJ22GTcXigK8DhsIDgV9Vws4yZngxieC0y8HLykNOqUcLrMR8LqRJayAoHoVSJKcEdNxYuNgnTBCr1FDOzQEp802mSAjCVBeNyiSgofiIIYASIIHDjyoXPNLnPfzhwEAtevuxGyUlJSgpaUFeXl5i57KGB8fh0KhQGZmJi3GP5mZmdBoNBgbG2OMaRcwmciTyWRIS0vDm2++iX//+9+MkcWIJGwMxhzYJBfLgsz2JZWVlYVbbrkFTzzxBHbs2EHLecrLy3Hy5EnGJCfi4uJQUFCAgYEBv16Yr7V7YGAAubm5aGxsDFlQPhQKCwvR2toKi8XCCMFGXyWzu7sb9fX1AOC/Pjk5ORG/PsD0Nv0pN4+o4guYcnJyYLFY0N/fH7XrA0wmlXzmAUxpH09NTUVKSgq6u7thMpmCuj46nQ47d+7E8ePH8cgjj6CpqYkRP1M0GBwcxJEjR7Bx40YIBALU1dXhs88+Q3p6OgwGA1JTU/2jsCqVChKJJMo7ZmE5u8nNzYVGo0F+fj40Gg1ycnIATN7Pp46TDQ8Po7CwMKA1165di+effx6HDh1CU1PTovcYExODkpISKBQKv45MuDlTB2ouXUZfnHH69Gk0NDQw7rtdKBT6E4RM0w3j8/lQqVTQ6XRISUlBX18fTCYTJBJJUAVlLu+bRE/6kutBEBwQBIGMcoD0uKA9+TYc+k4QmN615XE5QMwiLei0WhDDmXydIAhQVgM81smxT53NA8r7zTq6gV64HHakFxTDaTPDohuHbcIIr8sFq8UK13/E/0mSAsHhwO2lQHpcoDgxWH7p7dCNqmAaOIr0PAm+d+NuJGfkLfjzTo2RQtWQMpvNkMlkiI2NRVVVFa0SHWVlZTh+/DgyMzMZ4Zbd1dWFzZs3+xNct9xyC+RyORoaGqK9tYjDxmDMgRWeZwkZj8eD733ve3jppZdQVlZGy5oKhQI8Ho8xFTGSJNHS0oKqqirY7XbI5XJ/+3y0xM2ZKEIvk8lAURQmJiaQnJwMsVgcVfF3JlqjDw0NQS6XIzc3Nyyi8qHQ09ODlJSUgB+swsnExASkUimsVmvAOhVOpxMvvvgi/v73v+N3v/sdrr/++qgkDZnEyy+/jHfeeQfj4+N+4wMulwur1YrY2Fi/zXdRURE++OADPPPMM9i0aRNBEASXoihm2Nyy+GBjsG8hAwMDuPzyy/3C87///e+RlZXlF57X6/XYtWsXPvzwQzz77LN+4fk77rgDzc3NAZ9HoVBgw4YNOHjwIC0PuhRFobW1NeDunsWcZ2xsDP39/cjOzkZJSUlA7twymQwJCQkQCARh21uoaLVajI6OMlKE3mq14vjx437d0ry8vLDEjh6nGT37f4kkfj64vMnfp82g9yevpu1JPw6CnKnn6iUBq9UBYPr4p8XqAIf4JgFKUZMujharAxwOAXBikVN1GbILRUjJESE+vQBZgiXgxSXMeE8wdHd3+++ZgRKqqHywaDQaGAyGaULmkWZ0dBSPPPIIpFIpHnvsMXzve98DQRCQSqXYuHEjvvrqK0Yk4SIJG4MxBzbJxbIoDh06hCeeeAL79++n5abp9XrR3NzMKIc8lUoFuVyOjIwMWh0TF0N3dzcyMjKQl7dwRSrcWK1WSKVSGI1G1NTUMMahSafTYXh4GCtXrozqPmw2G+RyObxeLzgcDnJzcxnxewOY4UjpSx673W6Ul5fD4XBApVLN+3sjSRIffPABdu3ahauuugr33HMPI8YumYDNZoPdbofD4YDFYoHJZML4+DgMBgM0Gg3UajVUKhW0Wi2USiXefPNN1NXVEQRBcCiKYp6wzHcbNgb7lnHdddfh0KFDGB8fR25uLrZv344f//jHWL9+PZRKJUpKSrB//35kZmaCoijcfvvt+Pjjj5GYmIi//vWv/o7oQHnggQeQlZWFW2+9lZb9+xyQw1VE8znkJicno7S0NKhij+9+RcfIVzhob29nlHMxRVHQaDQYHBxEfHw8UlNTw+5irjr6V+ilB8GNIRATnwCnadzfyUVRFDxeHijKC/eEetbZLJN5uraXlwQo0gunc9J8gAIHFrsbpbU/hE7xNRwuIC6Fj4vveQtxifQnk9xuN1pbWwP6zHk8HgwODkKr1c46dks3FEWhvb0dIpEo4p85n2v1u+++i/vvvx/r16+f0YX56KOPgiAI3H///XOscnbCxmDMgU1ysSwKiqKwYcMG/PSnP8XFF19My5qjo6PQ6XRRrU4A31Rj7HY7CIJASUnJVIHAqOK78TY0NARUAQ0HTqcTfX19sFgskEgkcLlcGB8fx7Jly6Kyn9k4deoU8vLykJ2dHfFzu91u9PX1YWJiwj8aEEzAFCmiZYPu8XjQ398PvV4PiUSCrKws/7+dOnUKubm5/rEeHxRF4fjx49iyZQvEYjH++Mc/oqCgIKL7/jZDkiTcbjecTifsdrv/2vN4PGa0hLKcCRuDscyLxWLBqlWr8M9//pO2+1xXVxeysrJo1fuxWCyQyWR+o6FQixIajQZGo5GReos2m40xeqA6nQ4KhQLp6ekQiUTgcrloaWnBihUrwl6otYz0QH3srxCs+i8M/vtpeCxqgOAh/3ubYNUNoaB6HXrefwjWoWPgTLnzUBQFk8kGgkOAoigkFtah8ke/BwEK0i9ehttuQuP1ezCu7EThkh/APNaPlBxRWH8WABgZGYFer5/zmYSiKKjVaiiVSggEAhQWFkbs9x/pz5zX68X+/fvx1FNP4Wc/+xl++9vfzjkt4Xa7ceDAAVx55ZVh39e3BTYGiyxskotl0QwODuKqq67C559/Tks3CEVRaGtrC2ub73y43W4MDg5Cp9OhtLQUfD4fTqcTHR0dc2pGRAOVSgWr1RpxEXqv14vBwUGMjY1BJBIhJydnssJGUejo6IBIJGJMN5fT6UR7ezsaGhoiNsY2VXRfKBTOGA1Qq9WYmJhgVJB+4sQJCASCaYmmcEGSJFQqFYaHh1FUVITCwsIZ1U6Xy4Xnn38e119/vV83RKlUYuvWrTAYDHjiiSei3qF3lsEGWMyEjcFYFuT111/HoUOH8PTTT9OynsvlwvHjx2nRjHQ4HOjr64PNZoNEIll0bOArdJSXl0clPlwIhULh1zeLBj4dKB6PB4lEMk0HymAwYGBgANXV1WGXunBb9eAlTd67rWMyJGbPNGcivV6QXg8OPNSAgiXfh9lBwj3SDngdEK+9G8JVPwU3JvrFwPk6pqaKyguFwqgULwcGBkCSJEpLS8N2DoqicOTIEWzduhW1tbXYtm1bVIrHZzFsDBYG2CQXCy1s3boVCQkJuOOOO2hZz2w2o6enB/X19RHTnTpTBLWgoGBaQqu/vx8EQUAoFEZkPwsRKf2MqefzVasKCwshEAhmJPyYVMn0oVQq4XK5wi7ueKYpQXFx8awPCL4krkQiYYxTlMPhQEdHR1iTgRRF+QNCPp8PoVA4bxfic889h5aWFjz99NPYs2cPvvjiC2zbtg2XXXYZY7ToziLYC8pM2BiMZUFIkkRTUxMef/xx2pL/SqUSbrc75PG2hRwTF0M04sNA8Xq9aGlpQU1NTUR1N6fqQM0XW3R2diInJ2dGl3Q00Q20I12wHGPacRiNRkiEheAlMCuBeWZsO18yMdKQJInW1lYsW7YsLLINcrkcDz30EEiSxM6dOxlVoD2LYNYX2VkCm+RioQWbzYZzzz0X77//Pm0t7j09PUhLSwtK8DEUpoqg5uTkoKSkZNYHfSaKmYdbPwOYvD6+1vdAqlXRrmSeCUVRaGlpwdKlS8OWDDQajZDJZEhJSQnIlMBqteL06dOor69nVDLQ6XTSZiIxlakuQxKJJKC/H6fTiQsuuABerxebNm3Cr371K8aMeJ6FsAEWM2FjMJaAOH78OH73u9/hww8/pOWeMtV0J5gH+IWKhXTR09OD1NRURo6ra7VajIyMYMWKFWE/19RkolgsBp/PnzcWpLNLj26YWACcSn9/PzweD9xuN2w2G8rKyhizz3AYUun1ejz++ONobW3FI488gtWrVzMuqXwWwV7YMMCMpyuWbz2JiYl48MEHsW3bNiyQOA0YsViMgYEBeDweWtabDYPBgNbWVhgMBtTW1qK0tHTOG79PS0Imk4VtP8GSmpqKxMREjI6OhmV9k8mEtrY2jIyMoKqqCmVlZQsmGoRCITQaDRwOR1j2FCw++/He3l7aPps+bDYbTpw4gYGBASxduhSVlZUBjewmJSUhKysLSqWS1v0shqKiIhiNRlgsFtrWdDgcOH36NKRSKSQSCZYvX75ggouiKHzyySdYu3YtVq1ahfj4ePzyl79kE1wsLCwsc1BXV4eysjL84x//oGU9DoeDsrIySKXSgI73dTI3NzfD7XajoaFh1m5vuhCLxRgcHAxrfBgq2dnZ8Hq90Ov1YTuHTxahpaUF8fHxaGxsDKhbLjY2FgKBAH19fWHbW6gQBIHKykr09vaCJJmlv+31euH1eqFUKpGSkoK6ujrGJLgAIC0tDcnJyVCr1Ytey+l04tlnn8Wll16Kuro6HDlyBGvWrGETXCzfOtgkFwttXHPNNRgaGkJbWxst6/F4PBQVFaG/v5+W9aZisVjQ3t4OpVIZVHKCz+eHPXgJFolE4q8w0YXdbkdnZydkMhnKysqwfPnygKu5XC4XEokk4OA4EqSlpSEpKQkajYaW9VwuF3p7e9HZ2YmioiJUV1cH3SYuEokwMjICu91Oy54Wiy/A7O7uXnQy0Ov1QqFQoKOjA9nZ2aitrQ1IP+XUqVO46qqr8NZbb+Ef//gHXnjhBWzYsAE7d+5c1H5YWFhYznYeffRR7NmzB1arlZb1fHqIOp1u3uN8xUK9Xo+amhqIxeKwG+LweDwUFxdDoVCE9TyhUlFRAZlMRnuyxjd50NzcDJfLFVIyUSAQwGAw0FrQooukpCRkZmZieHg42lsBMHm9VSoVmpubERsbi5qaGmi12mhva1bEYrFfniMUSJLE+++/j6amJthsNnz99df4r//6L8Z1/LGwBAo7rvgdxuFw4Pzzz4fT6YTH48FPfvITbN++Hf39/bj22muh0+lQV1eH1157LWBB+RMnTmDTpk34+OOPaang+UbN6Jo1p0MElYm6U3SJ0LvdbgwMDECv10MsFiMrKyvk6k0kxcwDgQ5nQ6/Xi6GhIWg0mllF5YNFr9dDqVRi5cqVjKmSSaVSJCQkoKioKOj3BqLbNhsjIyPYsWMH+vr6sGvXLjQ2Nvqvh9vtxnnnnYe//e1vqKysDHpPLAHBjA8fy5mwMRhLUDz55JPQarXYsmULLevZ7XacPHly1niHLsfEUIm0Lmmw9PX1gcvl0ibdMDExAZlMhsTERJSWli5KNiMSUheh4tM1i7Y0iE6ng1wunyHT0dXVhYyMjLBLqYRCKKOyPjOHhx56CKWlpaxrdXRg1h/hWQKb5PoOQ1EUrFYrkpOT4Xa78YMf/ABPPfUUnnzySVx99dW49tprccstt2DlypW49dZbA1731ltvRV1dHX7605/Ssk+j0Yj+/n7U1NSEvAbdIqgKhcJfSWQCiw32pupoFBcXo6CgYNGBj0/MnEmOlBqNBgaDYU4r6LmYKiqfl5eHoqIi2qpbTBOCDVU490zL8kASiVarFc888ww++OADbN68GVdfffWsn5XW1la89tpreOqpp4L6WVgChg2wmAkbg7EEhcvlwqpVq/D666/Tllw5M96h2zFxMYRDi4gu6ErW2Gw2yOVyeL1elJWV0ZbQi5TubSiMj49DrVajqqoq4ue2WCyQSqVzisrTUTANJydOnEBhYSH4fP6Cxw4NDWHr1q3Q6XR44oknUF1dHYEdsswCs768zhLYJBcLgMmb6A9+8AM8//zzuOyyyzAyMoKYmBh8/fXX2LZtGz755JOA1xofH0dTUxM+++wzpKSk0LK/zs5O5ObmBm1ZS5IkVCoVhoeHaRVB9Xq9aG5uRm1tbUQddOYjlMpcoKL7oRIJa+Ng8FlBl5aWBhyYGwwGyOXygEXlg8UnBNvQ0BD2EY9ACSbA9FXzuVwuysrKAhpr9Xq92LdvH5599lnccMMNuP322xf8O6IoinEPMWcR7IVlJmwMxhI0H3/8MV544QW8/vrrtHxn+uKdlStXQq1Wh8UxcTF0dXUhKyuLNtMjOhkfH4dGowlJhN7lcqG/vx8TExOQSCT+8VG6YHqy5uTJkygoKAgoWUMHTqcTCoUiIFH5UAumkcDpdKK9vX1et2yTyYQ9e/bg888/x/bt21nX6ujDXvwwwIz2Cpao4fV6UV1djZycHKxduxZisRjp6en+h22BQACVShXUmnw+HzfffDN2795N2z4lEgkUCkXA+gZTRVBD1S2YDy6XC7FYDLlcTst6dOAToR8ZGQnoeKPROE1HYz7R/VApLi6GVqtllO5URUUFpFLpgp8lq9WKEydOBK3bFiyxsUO9oQEAACAASURBVLGM0xbxBZXzaU+4XC50d3eju7sbIpEoIBcuiqLw5Zdf4qKLLkJnZyc+//xz3H333QElitkAjIWFhWVhfvjDH4IkSfz73/+mZT2CIJCamopjx475Rc5zcnIY850skUjQ19fHSBH6UHRcvV4vBgYGcPz4caSmpqKhoYH2BBcwqWtWUlLCqNhjKuXl5f4OtnDi9XrR19eH9vZ28Pn8gETl8/Ly4HA4YDQaw7q3UIiLi0NhYeGsv1e3242//OUvWLt2LYRCIY4ePYrLL7+cMX/LLCx0wia5vuNwuVx0dHRgeHgYzc3N6OnpoWXdW2+9FYcOHaItCRQfH4/c3FwMDg4ueGykRFCzs7PhdDoxMTFB+9qhIpFIFnSk9CVvfI6AS5YsCVs3ms+hqbe3Nyzrh4LP2XBoaGjWf3e5XOjp6UFXVxeKi4uxcuXKsGuNFBQUwGw2w2QyhfU8wVBRUQGFQjHjs+T1etHf34+2tjZkZmaivr4+oK643t5eXHvttXjhhRfw6quv4umnn45YhZaFhYXluwJBENizZw+2bNkCt9sd8jpTi4VxcXFISUlBSkoKY+QHfPgcA8NhUkQHgYrQUxQFjUaD5uZmAEBjYyPy8/PDmoDIz8+HxWJhVOzhIz4+HgUFBWH7vU4VlY+JiQkqectkJ0hgskHhzTffxJEjRwBM/qyffvop1qxZA5VKha+++gqbNm1iZAcfCwtdMOtOxRI10tPT0dTUhK+//hpGo9H/YDs8PIzCwsKg14uJicHjjz+OBx98cNFObT5KSkowOjoKh8Mx679brVZ0dHT4O2/CmbwBvukK6u3tpe1nXCzzOQ75Om98yZtQHAFDITMzEzExMRgbGwv7uQJFKBRCo9FM+yz5kjfHjx9Heno66uvrkZGREZH9+AKmnp4exnyW4uLiIBAI/J+lqQE4QRBobGxEbm7uggHh+Pg47r77bmzatAl333033nnnHVRUVETiR2BhYWH5TiKRSLB27Vq8/PLLIb3/zGKhRCJBZWUlpFIpY+5RU/E5BtLlLEknCQkJyM7OhlKpnPMYvV6PlpYWmEwm1NXVQSgURsTVbmqyhqm/V71eT7sTpE6nQ3NzM6xWK+rr61FcXBx08jYxMRE5OTkBFd8jDUEQuPTSS3HXXXeho6MDV111Ff7nf/4Hb7/9Nnbu3BlVHT0WlkjBJrm+w2i1Wn+rrd1ux7/+9S8sWbIETU1NePvttwEAe/fuxZVXXhnS+qtXr0ZcXBwOHjxIy345HA7EYjFkMtm0151OJ7q6utDV1QWhUBiRzhsfSUlJyMjICHqkM5wUFBTAZDL5gwJfK/bx48eRkZER0eSNj7KyMigUirC3nQeKTz/KF9hNTd6cc845i3ZNDIXk5GRkZGTM2WEWDQoLC2E2mzE8PIzW1lZMTEz4A/CFAkKHw4GnnnoKl19+OVatWoWvvvoKF154IdsWz8LCwhIBNm/ejL/+9a8YHx8P+D0WiwXt7e2zFguTk5ORmpoKjUYTri2HDEEQKC8vZ2wSTigUYmRkZEaR1ne9h4eHsXz5clRUVIRFFmE+kpOTkZ6ezqg41geHw6G1mOy73iqVClVVVSgvL19UN1NJSQnGxsZgs9kWvTe6EQgE4PP5+NWvfoWHH34Yb7zxBoRCYbS3xcISMdgk13cYjUaDpqYmVFVVoaGhAWvXrsXll1+OnTt34sknn4REIoFOp8MvfvGLkNYnCAK7d+/G9u3b4XK5aNlzdnY2PB6Pv9tMoVD45+gDHZuiG5FIhKGhoUWNBdCJr8Osp6fHP4bK5XKjlrwBvtEIYNI4QVZWFtxuN/7v//5vWvU0mqMYpaWlUKvVc3YrRhq73Q6CICCTybBkyZKAdMlIksQ//vEPNDU1wePx4OjRo9i4cWNEqtIsLCwsLJMkJyfj3nvvxY4dOxY81uFwoKurCz09PRCJRHMWC0tLSzE4OMhI/av09HTweLx5tSSjBYfDgUQigVQqBfBNcdZ3vauqqpCYmBi1/fniWLpidTpJS0tDUlLSopKrU693aWlpQBqigcDhcFBeXs6oLnybzYbHH38cV199NX75y1/6xz4jGfv39vaiurra/19qair++7//G3q9HmvXrkVZWRnWrl0Lg8EQsT2xfPdg3RVZws6WLVuQkpKC22+/nZb1fJUYLpeLoqIiFBYWRl0jQqPRwGg0YsmSJVHdh4/x8XF0dnYiLS0Ny5cvZ8TcPUVRaGlpwbJlyyLWaTcXVqvVr5HhcDhwzjnnMCYJMz4+DpVKhZUrV0ZtD263G319fX5XJ71e7xepnQuKotDc3IyHHnoIlZWV2LFjB/Ly8iK4a5ZFwLbXMRM2BmNZFCRJ4sILL8SuXbtmdcv1eDwYGBgIyjFxeHgYNpsN5eXl4dp2yATiLBdNOjo6EBMTA4vFwiiHSgAYGxuDVqvFsmXLor2VGYTqBOn1ejE4OIixsTGIRKKwGSZ0dXUhMzMzqjHPVNfqjRs34je/+Q3i4uJw6NAh7NmzBx988EFUPmterxeFhYU4duwYnnvuOWRmZuK+++7D448/DoPBgJ07d0Z8TwyEGV8CZxlsJxdL2Lnvvvvw+uuvL1qTiaIojI2NobOz068XVFRUFPUEFzDptGK1WqMu3mk2m9HW1gaNRoO6ujo4HA7GBFBM0DCbqksmFApRW1vLOMFaPp8PgiCiUo0mSRKDg4NobW1FSkqK39VJJBJBo9HM6ZI5ODiIG2+8ETt37sRzzz2Hl156iU1wsbCwsEQZDoeDJ598Evfff/80gWySJKFUKtHS0hK0Y2JhYSGMRiMj9a/i4uKQn5+PgYGBaG9lGiRJYnh4GFarFQaDAfX19YxyqAQmJyVcLhcju2t4PB5EItEMuZK5oCgKarV6mqh8IBqioVJWVob+/v6oTHT4XKvXrl3rd62+5557/GPGF154IXJycvwyNJHm4MGDEIvFKCkpwfvvv48bbrgBAHDDDTfgvffei8qeWL4bRD87wHLWk5SUhAceeADbt28POblhNBrR2toKnU6Hmpoa1NbWQqVSMW5EMFoJHIfDgc7OTkilUkgkEqxYsQIpKSlzitBHi7S0NMTHx2N0dDSi553PEdAnbMqkgL2ioiIi1tk+prpoeb1eNDY2Tmtv53K5KC8vx0cffTTtQcloNGLz5s3YuHEjbrrpJnz00UezdguwsLCwsESHuro6iEQivPvuuyBJEq+99hoOHz4Mj8eDxsZGCASCoIqFTNe/Kioqwvj4+JxFmUhCURS0Wi1aWlr8XeMCgQDDw8PR3toMfHGsVCplpGNgbm4uHA6HX0t4LnQ6HVpaWmCxWEIWlQ8WHo8HoVBIm6N8oEilUlx77bX485//jL17987pWv3EE09EzeX8zTffxHXXXQcAGB0dRX5+PoDJ5oBIPwuwfLdgxxVZIgJJklizZg127NiB2tragN9ntVohl8tBURTKysqmjblpNBpMTEygsrIyHFsOiZ6eHqSmpqKgoCAi53O73RgYGIBOp4NYLPZ3AfmgKAqtra1YsmQJkpOTI7KnhXC5XDh+/DgaGhoQExMT1nP5ROUHBwdRUFAwZ+ffxMQE5HI5amtrGVNZHRoagsPhQFlZWVjPMzExAZlMhsTERIjF4nkdSdevX48f/ehH+PnPf45XXnkFr7zyCm6//Xb84he/CPvvkiWsMONDz3ImbAzGQgujo6P44Q9/CK/Xi8rKSuzevRvZ2dmLWrOzsxO5ubmLXicc6PV6DA0NRXXs32QyQSaTIT4+HmKxGPHx8QAm4+Hm5mZUV1f7X2MSfX194HA4jBQpt1qt6OzsRENDw4xYzmKxQCaTgcvlQiKRRFzjjKIotLe3o7S0NOz6wDqdDo899hja29vx2GOP4YILLmBM7DoVl8uFgoICnD59Grm5uUhPT5+WpMzIyGBk52AUYN4v7yyA7eRiiQhztczPhdPp9I+VFRcXo7q6eoaOU15eHsxmM8xmc7i2HTRisTgioqy+UYPW1lYkJCSgsbFxVm2HqSL0TKm4xsbGRqTDzGfJbTabUV9fj5KSkjmreWlpaUhMTMTIyEhY9xQMAoEARqMxbJ9vu92OU6dOQaFQoLKyEkuXLp03wQUAf/rTn7B7925ceOGF0Gq1OHLkCH7961+zCS4WFhYWhtLd3Y2bb74ZJEmiqakJe/fupSUxxTTX5KlkZmaCIIignCXpwndvlcvlKC8vx7Jly6Ylszgcjt/dmYmUlJTM6gTJBJKSksDn86FUKv2v+UTlu7u7oyriTxAEKisr0dvbG7ZOOKfTiaeffhqXXXYZzjnnHBw+fJjRrtUfffQRamtrkZubC2CyG89nIKDRaJCTkxPN7bGc5bBJLpaIUVNTg2XLlmH//v1zHjPVMdE3VpaRkTHrsVNbq5mSwOHxeGFN4EwdK3O73WhoaFhw1CA1NRXJycmMSuAUFBTAZDKFJYEzmyV3IEKlEokEAwMDjBqBrayspD1B6fF4IJPJcPLkSRQUFKC2tnbBLj+KonDixAnccsstEAgEWL58OR599FGkpqbSti8WFhYWFvpQq9W4+eabceutt+IPf/gDWltbcfjw4WkJgsUQFxeHvLw82tajm/Lycsjl8oiN3rndbkil0mn31pSUlFmPzcrKAjDZkcM0uFwuo5NwQqEQIyMjsFqt6OvrQ3t7O7KysqLmsD6VxMREZGdnY3BwkNZ1SZLEO++8g6amJrjdbhw9ehQ33HADI80VprJv3z7/qCIArFu3Dnv37gUA7N27F1deeWW0tsbyHYAdV2SJKFqtFqtXr8bBgwenPViTJAm1Wo2hoSEIBIKgHBO7urqQlZXlrxREm3CNCBqNRshkMiQlJS04VnYmPmeaSIwIBorZbEZPTw/q6+tpqUI5nU709fXBYrGgrKwspGCHaS6ZwKQVc1JSEgQCwaLWIUkSKpUKw8PDflfSQK67RqPB9u3bMTQ0hF27dqGurg6XXHIJtm/fjlWrVi1qT2dy00034Z///CdycnLQ2dkJANiwYYM/2DYajUhPT0dHR8eM9wqFQqSkpIDL5SImJgatra207u0sh5llYBY2BmMJmbfffhs8Hg/r1q3zf9d/9NFH+Mtf/oJXX32Vlvsu00fvfKYyIpEobOcgSRJDQ0NQq9UoKSlBfn5+QNfW4XCgo6MDjY2NjDBQOhNfsm42jadoQlEUZDIZhoeHIRaLGWNA5cP3N7Fy5UokJCQsai2fK/mWLVtQUVGBHTt2+DWtmI7VakVxcTH6+vqQlpYGYDKpu379eiiVSpSUlGD//v3IzMyM8k4ZARuDhQE2ycUScZ566ikMDw9j+/bt/rG7kZER8Pl8CIXCoJMwPo2nxsZGxlQ16NR4mqpLJpFIQk6cqdVqmM1mVFRULGo/dNLb24vk5GQUFhaGvAadFtEURaGtrQ0SicR/U442Ho8HLS0tqK2tDSqx6YOiKIyPj0OhUAT1N2axWPDUU0/hwIED2LJlC3784x/7A0mZTIaf/exnOHLkSFB23gvx5ZdfIjk5GRs3bvQnuaZy9913Iy0tDQ899NCMfxMKhWhtbWVcQP4tgQ2wmAkbg7HQCkVRuPLKK3Hbbbfh/PPPp2XN8fFxaDQarFixgpb16CScSThfZ/3AwAByc3NRXFwcdAwaiSRcqPiScA0NDYyJrfV6PeRyOdLS0uBwOFBQUMBITTiDwYCBgQFUV1eHHI8ODg5i69atMJlM2LVrF2vqc3bDxmBhgDmpb5bvDLfddhu++OILvPnmmzjvvPOwb98+VFdXQyKRhNRlFBsbi8LCQkZZRqelpSEhIQFjY2Mhr+FyudDT04PTp0+jqKgI1dXVi+oMy8/PD9uIYKiIxWIolcqQRgTDYREdCU2FYImJiYFYLIZUKg36vWazGe3t7RgdHQ34b8zr9eLVV1/FmjVrwOfzcezYMVx99dXTKqVlZWW48sor8fzzzwe9p/k4//zz56zqURSF/fv3T2t9Z2FhYWEJHIIgsHv3bmzevJm20Xw+nw+Px8NIAWkOhwOJRBLS/XM+DAYDWltbYTQaUVtbC5FIFFIiqKSkBKOjo4xwgjyT+Ph45OfnMyK2nk2GorKyMqIu1MGQkZGB2NjYkNwDJyYmsGXLFlx//fW48cYbWddqFpYQYZNcLBGnv78fCQkJeOKJJ/DUU0/h/vvvX3SFTSAQMMYy2odEIkFfX1/QN2Cv14v+/n4cP34caWlpaGhooKWdd2oChykaZjExMRCJRJDJZEG9L5wW0UlJScjMzGSUxXdOTg68Xm/A+h0OhwOnT5+GVCqFRCLB8uXLF/wboygKhw4dwpo1a9Db24t///vfuOuuuxAbGzvr8b///e/xi1/8IuifJVS++uor5Obmzuk2SRAELr74YtTV1eHFF1+M2L5YWFhYvk2Ul5dj9erVeOWVV2hbs6KiAjKZjDGxxVT4fD5IkoRer1/0WlarFR0dHVAqlVi6dCkqKyvnvEcGgk+Enu4kHF0UFRVhfHwcNpstKud3uVzo7u6eVVQ+Li4OAoEAfX19UdnbQpSXl6O/vz/gZLLb7caLL76Iiy++GGVlZTh69CguvfRSxorKs7AwHTbJxRIxRkdHsWnTJtx000344x//iCVLlsBkMtGyNhMDhdjYWAgEAn87+kJM7UzicDg455xzAtZ2CJSUlBTGidDn5ubCbrdjYmJiwWN91TyVSoUVK1agvLyc1nE5HyKRCGq1mlHuQpWVlZDJZPMmTb1eLxQKBTo6OpCdnY3a2tqAxOF7enqwfv16vPzyy/j73/+OP/3pTwsmVmNjY2c4noaTMwVMz+Tw4cNoa2vDRx99hOeeew5ffvllxPbGwsLC8m1iy5YteOWVV2gTPk9MTERGRgajikNT8SXhQu3Q9iVburq6UFJSgpUrV9J2/8vKyoqaE+RCcDgcVFRURLw46iv2trW1+U2oZtNZ9blQWyyWiO0tUHg8HkpKSiCXy+c9jiRJfPTRR1i9ejVGR0dx+PBh3HLLLYzRz2Vh+bbCJrlYIsLY2Bh+9KMf4cILL8RXX32FCy64ALt378bWrVtpa5n3WUYzya1GIBBAr9fDarXOe9yZnUklJSVhE9IUi8WMdBGcL4jyWUT39PSgtLQUVVVVixb0nA8ulxuWEYfF4BsdmC1pSlEUVCoVmpubwePx0NjYGJA2mVarxV133YXbb78df/jDH/D222/P2SkVTTweD9555x1s2LBhzmN8um45OTm46qqr0NzcHKntsbCwsHyrSElJwT333IM//vGPtK0pEokwPDzMmNhiKgkJCeDz+UEn4bxeL/r6+qYlW+Zy/F4MkXaCDIb09HTExsYuSn4jUM4s9i4kQ+FzWafbhZou8vPzYbPZYDQaZ/wbRVE4efIkrrzySrz77rt499138dhjjzFGD5aF5dsOm+RiiQg5OTloaWnBNddc479ZlZaW4pJLLsFLL71E23nKysoWVa2jG4IgUF5eDqlUOusN2Gw2o62tDWq1OqydSVPxVZeY1OI914igrzOpvb0dfD4fdXV1EQsA+Hw+KIpiVNK0qKhoRtLUlyC1Wq0Bj246HA48+eSTuOKKK3Deeefh8OHDOP/88xnbFv/ZZ5+hsrJyTodJq9Xq15qzWq349NNPsXz58khukYWFheVbxfXXX4+urq5ZTT5CISYmBkKhEAqFgpb16EYoFEKtVsPpdC547NTCEV2an/MRHx+PvLw8RuhfzUZZWRn6+vrg8XjCdg69Xo+WlhaYzeagir2pqalISUmBWq0O295CxVfE3bdv37TPnUajwW233Yb77rsPjz32GF577TUUFxdHcacsLGcfbJKLJWLMJsr5wAMPYO/evbRViBISEpCdnY2hoSFa1qOD9PR08Hi8aa3oUzWTxGIxVqxYEdbOpDPJz8+H2WxmlAi9SCSCSqWC0+kMuTOJbnwjDkwRNvWNDvT09PhF5YMZ3SRJEm+99RaamprA4XBw7Ngx/PznP2eM/fZ1112HVatWobe3FwKBAC+//DIA4M0335wxqqhWq3HppZcCmByF/sEPfoCVK1eisbERl112GS655JKI75+FhYXl2wKHw8GePXtw33330VYYzMvLg8ViYVRs4YPL5aK0tHTe8TGfG3FzczNsNhvtmp/zUVxcDK1WyyhtWR+xsbEoKioKS3HUarWivb0dQ0NDflH5YIu9PhMjl8tF+/4WS1JSEvr7+/HII4/AYrHgkUcewU9+8hOsW7cOn3/+ORobGxlbYGRh+TZDLNDeybzeT5azjjfeeAMHDx7EM888Q8t6Xq8XLS0tqKmpQVxcHC1rLhan04n29nbU1NRgaGgI4+PjEIvF4PP5Ubu5mc1m9Pb2oq6ujjE32LGxMQwNDcHj8SAzMxNCoTDsnW0LMTg4CI/HA7FYHNV9+HC5XGhtbQVFUVi2bNmsOhVnQlHU/2fvzsOiLvf/8T9n2JFVZJ0BBmaGRWUV0DpaCuGSpFmW2qJp22n5amqnLPfKfWkxT+bJysrTZsclMzNFM0sdRllEBGbYdxCYYWdg5v37w9/MBxRkBt4zjPp6XNe5riMz73te0AA39/u+ny+cP38eq1atQlhYGNauXQtPT08TVEtuE+bxA4DciOZgxKgYhsH8+fORmJiIGTNmsDKmOc4ttBiGQWpqKgIDA2/63dnY2AiZTAZra2sIhUKT3njUqqurQ3FxMSIjI03+2n1hGAZSqRShoaED6vStpVKpkJeXh6amJojFYr3mMrdSVVWFa9euYcSIEQOujW1NTU245557YGtri2effRavvPKK2fx9QsyCef2gvEOYx+17clebPXu2LiybDfrcrTM1Kysr2NnZ4fz587C1tUVcXBzc3d0HdQKoDaGvqKgYtBq6amxsRGlpKZqbm+Hv7w+xWDzoC1zA9SOCtbW1feaqGVvXIFaBQAAul6vrMnQrBQUFmDt3LrZv345PP/0Uu3btogUuQggh4HA42LhxIzZt2sRaBz1HR0cMGTIEVVVVrIzHJm2GU9cIiba2NmRmZnbrRjwYC1zA9WxZCwsL1NTUDMrr3wpb+Vdd5zKurq69hsobysPDAyqVCvX19QMeiy3artVJSUmIjo4Gj8fD4sWLTb7ApVAoMHPmTISEhCA0NBTnzp1DXV0dEhMTIRaLkZiYaFZfN0LYQItcZNBxuVxs374db731Fmtb5t3d3dHe3q5Xxz5jYhgG1dXVkEgkGDJkCGxsbODm5mY2x8OEQiGKiooGNShWe3QzJycHQqEQsbGxKCoqMptctcHqLqTFMAwqKiogkUjA4XAQFxcHHx8fBAYGQiaT9XpdfX093n77bcyfPx8vvPACjhw5QjlVhBByG1qwYAE8PDy6/Qxfs2YNeDweIiMjERkZiaNHj+oe27BhA0QiEYKDg/Hbb7/dcmwvLy88/fTT+OCDD1irVygUoqCgwGyO+nc1ZMgQuLq6oqioCDKZDOnp6fD09NS7G7GxicVi5OXlmeXXzsnJqd83R7vOZbSh8l5eXqzd7O26gGkO80dt1+rPPvsM33zzDX788Ufw+Xz89NNPJq9l0aJFmDx5MrKzs5Geno7Q0FBs3LgRCQkJkMlkSEhIwMaNG01eFyHGRMcVidl44YUXcO+99+Lxxx9nZbympiZcvXoVMTExg7JjSqFQQC6Xw97eHkKhEDY2NqitrUVpaSkiIiJMXk9vysvL0djYiODgYJO+bmdnJ4qKilBTU4PAwMBuO9vy8/PB5XIhEAhMWtOtZGVlwdXVFd7e3iZ7zfr6esjlcjg6OiIwMBDW1ta6xxiGQVpaGvh8Ptzd3XUfV6lU2LNnD7788kssXLgQ8+fPp1bUpC+0Vd480RyMAADOnDkDBwcHzJ07VxcUv2bNGjg4OOD111/v9tysrCzMmTMHEokE5eXleOCBB5Cbm9tjLqqWSqXCmDFj8O2338LX15eVmktKStDe3g6RSMTKeGzRaDQoLi5GXl4ehEKhyTK3DFFUVAS1Wo3AwMDBLuUmHR0dkEqliImJ0Xu3fV1dHeRyOZydnREQENBtLsM2bQfqgIAAo73GrdTU1GDDhg3IyMjAhg0bujX1qa2tRXx8PM6cOWOyJkpKpRKRkZHIz8/v9rdQcHAwTp8+DW9vb1RUVGD8+PHIyckxSU3kJjQHMwLz+qlO7mrvvfcetm3bhqamJlbGc3BwgJOTk8mP47W0tCA9PR0FBQUICQnB8OHDdVuT3dzcwOFwzKpjn6lD6BmGQWlpKVJSUmBtbd1jqLxAIEBlZSXa2tpMUpM+xGIxCgsLTbLrTfseKioqwvDhwxESEnLTpJDD4YDP52Pq1Kloa2uDRqPBkSNHEB8fj/r6epw7dw7PP/88LXARQsht7r777sPQoUP1eu6hQ4cwe/Zs2NjYICAgACKRCBKJ5JbXWFtb491338Xy5ctZ27HM4/FQW1vL2jHIgWIYBlVVVZBIJNBoNAgNDUVLS4vZLXAB12MSzDWE3srKCgKBQK9IkObmZqSlpXULlTfmAhcA+Pv7o6qqyuRfu7a2Nrz//vt46KGHMHbsWJw9exb3339/t7mtm5sbli5dio8++shkdRUUFMDd3R3z589HVFQUnnvuOTQ3N6Oqqkp309bLy8ssjxcTMhDm95Od3LU8PDzw7LPPYtu2bayNGRgYqAsONzaVSoXs7GxkZmbC19cXUVFRPYZzisViyGQys9hODfzfFm9jH8fr2rWotbUVMTEx8PX17XGCyeVyIRaLzequkpWVFfz9/Y3aHr2jowM5OTm691BkZCSGDBnS6/Pd3d0xceJELF26FNOmTcORI0dw6NAhvPfee6wEwxJCCDFfH3/8McLDw7FgwQJdpk5ZWVm33Vh8Ph9lZWV9jvXggw+ira0NZ8+eZaU27e/x3NxcVsYbCIVCAalUirq6OkRFRSEwMBDe3t5obW1FQ0PDYJd3E3OcA3Xl5eWFlpaWXiNBtPPhrKwsCAQCRERE/q+ovAAAIABJREFU6JUhygYul4ugoKABZ4fpS6PRYP/+/ZgwYQIA9Nm1+umnn8ayZcuMXpdWZ2cnLl26hJdeegmpqakYMmTITUcTORyO2TWJIGSgaJGLmJVXXnkFJ0+eRGFhISvjWVlZwc/Pzyhtj7W0IZoXL16Es7MzYmNjb3nH1c7ODp6eniguLjZaTYYydgh9Y2MjUlNTUVlZifDwcL1C5bW73q5du2aUmvrD29sbzc3NrGe9aTQaFBUVQSqVwsnJqc/3kFZZWRmqqqrw888/Y9GiRdi7dy9rR00IIYSYr5deeknXtMfb2xtLly4d0HgcDgfbtm3DihUrWLsxOHToUHC53EHbvd7c3Iz09HQUFhYiNDQUoaGhup31prrB11+3Qwj9jV+7rqHyLi4urIXKG2ro0KGwsrIy6tdO27V68uTJ+Pvvv3H8+HEsX768z6YFHA7HpE2V+Hw++Hw+Ro8eDQCYOXMmLl26BE9PT92cv6KiAh4eHiariRBToEUuYlasrKywfv16vP3226xNOnx8fKBQKFjvjscwDMrLy7sFgnt7e+t1N8Tf39/sjuMZI4ReGyrf365FQUFBkMvlZhPAyvak+MbjE/q+hxobG/HOO+9g1qxZeOyxx3Dw4EHs2rVrwPX0xNDA466OHTuG4OBgiEQiCjUlhBAWeXp6wsLCAlwuF88//7zuSCKPx0NJSYnueaWlpeDxeHqNGRwcjPHjx+OLL75grc6goCCT717X7iS6cuWKbld0T7ubByvWQl9BQUFmG0Lv4OAAV1dXlJaW9tggh81Q+f7Qfu2McZKjsLAQc+fOxdatW7Fr1y6z7lrt5eUFX19f3a7AkydPYvjw4Zg2bRr27t0LANi7dy+mT58+mGUSwjpa5CKsUKvViIqKQlJSEoDrZ8BHjx4NkUiEWbNmQaVS6T1WYmIiAOCPP/5gpTYOh4OgoCBW79bV1tYiJSUFjY2NiImJgUAguGWo6424XC6EQuEtu+OZGpvH8To7OyGXy5GWlgYPD49+dy2ytbWFt7c3azv72ODg4IChQ4eitLR0QOMolUpcvHgRtbW1iIqKQkBAQJ/voc7OTnz55Zd44IEH4OPjg/Pnz+Phhx/G2LFj4evri++//35ANfXkmWeewbFjx276+OLFi5GWloa0tDQ8+OCDNz2uVqvxyiuv4Ndff0VWVha+/fZbZGVlsV4fIYTcjbouzBw4cEB3I2LatGn47rvv0N7ejoKCAshkMsTFxek97ooVK/DZZ5+hrq6OlTptbW3h4eHRbeHNWG7cSaTPrmhtrMVgdpnujY2NjdnNgbrSfu0uXLiAhoYGjBo1CgKBwCxyzqytreHr68tqxIRCocDy5cvxzDPP4IUXXsAvv/xyW3St3rFjB5588kmEh4cjLS0Nb7/9NpYtW4bff/8dYrEYJ06cMOkRSkJMYfB/CpE7wocffojQ0FDdv998800sXrwYcrkcrq6u2LNnj95jcTgcbN26FatWrWJt0uHi4gJra+sBb11ubGzEpUuXUFZWhrCwMAQHB/d727G7uzs6Ozt1ORrmwNvbG01NTf0OoddoNLpQeRsbG8TFxXXrmtgfvr6+uHbtmtmE1wLXu/aUlZWhvb3d4GtbW1tx+fJl5OXl3dSYoDcMw+DkyZOIj49HXl4ezpw5g4ULF3YLcN24cSM2bNgAhUJhcE23YkjgcVcSiQQikUjXFXL27Nk4dOgQq7URQsjdYM6cObjnnnuQk5MDPp+PPXv24I033kBYWBjCw8Nx6tQpvP/++wCAESNG4PHHH8fw4cMxefJk7Ny506CbcM7OzliyZAnWrVvHWv3+/v6oqKjo1+9MfXTdWc/lcg3aSaS9wWfMWIuBMMc5EHD9KOjly5dhbW0NW1tbk4TKG4rH46GhoWHAjZVUKhU++eQTTJo0CaGhofj7778xadKk2ybHKjIyElKpFBkZGTh48CBcXV3h5uaGkydPQiaT4cSJE/2a5xFizmiRiwxYaWkpfvnlFzz33HMArk82kpOTMXPmTADAvHnzcPDgQYPGFIlESExMxGeffcZanWKxGPn5+f3a9q09dpeTkwOhUIjw8HCDjt31Jjg4GLm5ubd9CD3DMKipqUFKSgra2toQGxvba6i8obQhouaUm2FhYQGhUGhQoG5nZydkMhkyMjLg4+OD6OhovcLhs7KyMHPmTHz99df4/vvvsW3bNri6ut70vKFDh2Lnzp0mO9bQU+BxV/0NPyaEENLdt99+i4qKCnR0dKC0tBTPPvssvv76a1y+fBkZGRk4fPiwrlMaACxfvhx5eXnIycnBlClTDH69uXPnIiMjA1euXGGlfgsLCwQGBurVkc9Q2p31TU1NiImJgb+/v8FzD29vb1YWQ4yh6xzIHHQNlff390dcXBwYhmFt5x+bOBwOQkJC+h1Cr9Fo8Msvv+i6Vv/999944YUXqGs1IbcBWuQiA/baa69h8+bNuklFbW0tXFxcdL8E+vvH7fLly/Hll1+yFjxuY2MDLy8vgwLfux67c3d3x6hRo+Ds7MxKPQBgb28PNze3AR99Y5OjoyMcHR31zqhoaGjApUuXUFVVhYiICIhEItYnAK6urqzsxGOTu7s7NBpNn4G6Go0GJSUlSElJgZ2dHeLi4uDm5tbn+FVVVVi4cCFee+01LF++HN9//z2EQuEtrxk7dqxeYw8U24HHhBBCzIeFhQW2bduGN998k7WbcO7u7mhvb2etcYt2Z315eTnCwsIQFBTU75312ht8ubm5ZnMzrStXV1ejB6n3Ra1Wo7CwEBcvXtSFymtvuIWEhJhV1/CuHB0d4ezsbNDfIQzDIC0tDdOmTcPhw4d1XasdHR2NWCkhhE20yEUG5MiRI/Dw8MCoUaNYH9vBwQFvvvkm3nnnHdbG9PPzQ1VVVZ+B710XJrTH7jw8PIyyNVl79M2Q3DJj0yejoq2tDZmZmZDJZAgKCsLIkSNha2trtJrEYrHZBbAGBwdDJpP1WJN2d5tEIkF7eztiY2PB5/P7fA+1trZi69atePjhh5GQkIAzZ85g7NixZrUtvrfA464GEn5MCCFkcI0ePRp8Ph+HDx9mZTxtPupAF5K6NrQRCoUICwtjZWe9k5MT7O3tUVVVNeCxjGGw5kBdQ+UB9HgU1M7ODh4eHmbVNbyrwMBAlJSU6DXPLisrw4svvogVK1Zg8+bN+Oqrr6hrNSG3IVrkIgPy119/4fDhwxAIBJg9ezaSk5OxaNEiKBQKXUeTgfxx+8QTT0AmkyE9PZ2VerlcLkQiUa/HzBiGQXV1NSQSCVQqFavH7nqj3cZ/u4TQa3e3paenw8vLC9HR0Sa5u2VtbQ0+n29WuRm9BeM3NjYiNTUVVVVViIyM1Gt3m0ajwXfffYcJEybA2toaFy5cwJw5c8wiwPVGvQUedxUbGwuZTIaCggKoVCp89913mDZtminLJIQQ0k8cDgebNm3Cpk2b0NraysqY2m6G5eXlBl/bU0MbNnfWA9ejMgoKCozSkW+gbGxs4OPjY9IQ+vr6eqSkpECpVOpC5XvLd9N2DWfrvcImS0vLPiMmmpqa8O677+Lxxx/Ho48+ihMnTiAmJsasbjASQvRnfn89kdvKhg0bUFpaisLCQnz33XeIj4/Hvn37MGHCBOzfvx/AwFrTcrlcbNu2DW+99RZr26CHDRsGjUZzU46QttvdtWvXEBkZCaFQaLJz9x4eHqxu42fDjSH0XXe32draIjY2FsOGDTPpBIDP56O+vh5NTU0me82+dA2F7XqHWSQS6bW7jWEY/PXXX5g4cSKkUqmuy40xd8UZwpDA4/Lycl2nRUtLS3z88ce6oNbHH38cI0aMGMxPhRBCiAG8vb3xxBNP4KOPPmJtzMDAQBQXF+vdWOjGuQcbDW16Y2VlBV9fXxQUFLA+Nhv4fL5JQuibm5uRnp6O4uJijBgxAiEhIX2GynO5XIjFYoOySk3J3d0dHR0dNx351HatTkhIgJeXFy5cuIAZM2aY5Q1GQoj+OH1sGTa/g+nEbJ0+fRpbt27FkSNHkJ+fj9mzZ6Ourg5RUVH45ptv+uwgdyvPPvss7rvvPjz22GOs1NrS0oLLly8jNjYWbW1tkMvlUKvVEIvFeoWBG0NzczOuXLmC2NhYs7lz1NjYiOzsbF3nIXd3d/j7+w9q6KZSqYRcLkd0dLTZfJ3q6uqQlZUFCwsLiEQivRf/5HI5Vq9ejc7OTmzatAnDhw83QbWE3MQ8vpHIjWgORgZde3s7xowZg++//x58Pp+VMcvKytDU1ITg4OBen6M98m/quQfDMEhJScGIESMwZMgQo7+eoerr61FYWIjIyEjW50AqlQr5+floaGiAWCzusclNXy5fvgwvLy+4u7uzWhsbamtrMXXqVJw6dQq2trY4deoU1q5di3HjxmHFihXUYZAMFpqDGQEtcpHbQlVVFR544AEkJyezNunIyclBY2MjNBoNRCKRWfxyy83Nhb29PWsTyYFqaGhAWloa7OzsEBYWZja7i65evQoXF5du3aQGg7ZteXFxMSwsLODr66tXTXV1ddi0aRMkEgnee+89PPDAA2azYEfuSvTmM080ByNm4eeff8bXX3+NL774gpXfVQzDQCqVIjQ0tMcbi0qlEjKZDPb29ggMDDT53EOpVCIvLw9RUVFm+bs5MzMTHh4e8PDwYGU8tVqNkpISVFRUQCAQ3JS5ZYj29nakpqYiNja216ONg2nVqlWor69HeXk5HBwcsGHDBohEosEui9zdzO+HzB2A9mKS24Knpyfmz5+P7du3D3gsbYeY2tpatLa2IiIiwiwWuID/C8fUdxu/sbS2tiIzMxNyuRxhYWHo7Ow0q8mKSCRCYWHhoH6damtrIZFI0NLSgpiYGERFRaGwsPCWWR4qlQo7d+7ElClTEBUVhb///huJiYlmOYkmhBBCACApKQlNTU3466+/WBmvtxD6lpYWZGRkID8/HyEhIRg+fPig3FxzdnaGjY2NWXV07oqtEHqGYVBZWdktVN7b23tAc5LByA7TV3V1NRQKBQ4ePIh58+bhhx9+MPkCl0AgQFhYGCIjIxETEwPg+o3PxMREiMViJCYm3hSnQggxHC1ykdvGq6++iuPHj6OoqKhf19/YIWb06NEQi8VmFWRuaWkJgUAAuVw+KK/f0dEBmUyGjIwMXai8q6srBAJBjyH0g+VWwfjG1tTUhNTUVJSVlSE8PBxisRhWVla6mnr6b6fRaHD48GFMmDABTU1NOH/+PBYsWGBWC4eEEEJITzgcDrZv344VK1awFsredSFJpVIhJycHmZmZ4PP5iIqKGrToCC1z7OisZWNjAx6PN6DssPr6ekilUigUij5D5Q3F5/NRW1uL5uZmVsYbqNbWVmzbtg3Tp09HfHw8Dhw4gK+++mrQ6jl16hTS0tIglUoBABs3bkRCQgJkMhkSEhKwcePGQauNkDsFLXKR24a1tTXWrVuH5cuXG9x+uq6uDikpKWhoaOj2y9zT0xPNzc26cHVz4OXlZfKatMGuUqkU9vb2iIuLw7Bhw7rV1DWE3hxog/EbGhpM8noqlQpXr17F1atXERAQgPDw8Jvalnt7e+OXX37B2bNnAVxfWL148SKSkpJw7NgxHDlyBGvXrjXLnA9CCCGkNyEhIRg3bhz27t3L2piBgYG4evUqpFIpnJycEBsbazY7662trcHj8cxyRxJwfSGprq7O4IUkbah8UVERhg8frleovKG4XC6CgoKQk5Nj8HydTRqNBt9//z3i4+NhZWWFCxcu4IknnsB9990HHx8fXYOswXbo0CHMmzcPADBv3jwcPHhwkCsi5PZHmVzktsIwDKZPn46XXnoJ999/f5/Pb2pqgkwm0wWC29vb3/ScxsZG5OTkYNSoUWZzbMxUNRkS7KoNoTenlspNTU24evWqUWtSq9UoLi5GVVUVAgIC4OHhccvXkkqleOWVV/D999/j3XffRU1NDbZs2WKUkFhCWEJvTPNEczBiVpRKJcaOHYtjx471K5RcS3tMrrCwELa2tnB0dDTLXCSNRoOUlBSEhYX1OH8cbAqFAvn5+Xplh6lUKhQUFECpVJoshzYrKwtubm7w9PQ0+mt1xTAMzp07h1WrViEiIgJr1669Kb+strYWCQkJOHPmDJycnExWW0BAAFxdXcHhcPDiiy/ihRdegIuLCxQKha52V1dX3b/JXYHmYEZAi1zktiOTyTB79mwkJyfDysqqx+e0t7cjLy8PLS0tEIvFcHZ2vuWYV69ehaurK7y8vIxRcr9kZ2fD2dnZaOHqXYNdhUKhXt0vc3Jy4OjoCB8fH6PU1B/GCuvvOgn39vaGn5+fXi2lGxoa8NRTT6GsrAybNm1CUlIStaIm5o4mWOaJ5mDE7OzZsweXLl3Cli1b+nV9XV0d5HI5nJ2dERAQAEtLS0gkEkRGRppNc5uu6uvrUVRUhMjIyMEupUdXrlyBu7t7ryH0Go0GxcXFrITKG0qlUuHixYuIjY01WVfuvLw8rF69GiqVCps2bcKIESN6fe7JkycxcuRIky7ClZWVgcfjobq6GomJidixYwemTZvWbVHL1dWVcrnuLjQHMwL6y4vcdrTBjHv27Lnpsc7OTuTl5SE1NRXDhg3DqFGj+lzgAgChUIiCggKzyl4QCoV9Bpn3R2trKy5fvoy8vDwEBwdj+PDhei1waWsqKioa9GD8rrRh/SqVirUxtVkVSqVSd7y1r4Wqzs5O7NmzB4mJiZgyZQqsra0RFxdHC1yEEELuGM888wxSU1ORlZVl0HXaPMvS0lKMHDkSwcHBsLa2BpfLhVgsRm5urpEqHhhXV1dYWFjcdiH0XUPlGYZhJVTeUNbW1vDz8zNJfmp9fT2WLVuG5557Di+99BJ+/vnnWy5wAUBCQoLJd5nxeDwAgIeHB2bMmAGJRAJPT09UVFQAACoqKljrmknI3Yz++iK3pRUrVuCLL75AbW0tgP9rf5ySkqJbXOjrWFlX1tbW4PP5AwrxZJuVlRX8/PxYC8bvGirv4+OD6OhoODo6GjSGNhjfnELoLS0tERgYCJlMNuCxWlpakJ6ejuLiYr2zKhiGwe+//474+HgUFxfjzz//xNKlS/Hee+/h9ddfH3BNPVmwYAE8PDwwcuRI3cf+9a9/ISQkBOHh4ZgxY0avW9176uxDCCGE6MPCwgJbtmzBsmXLoNFo+nx+e3s7srKykJ2drcuzvPHon5ubGzQaDerq6oxV9oBoF5L0+XxNraf5q0Kh0IXKR0dHIyAgYNAa3fj4+KChocFoma4qlQr//ve/MXnyZISHh+Ovv/4y267VXfN2m5ubcfz4cYwcORLTpk3TZd3t3bsX06dPH8wyCbkj0CIXuS05ODjgX//6F9555x3s27cPcXFxqK6uRmxsLHx9ffu1e0bbDaalpcUIFfePj48PlEolmpqa+j2Gdqt611B5Nze3fo+nDcY3VeC7Pjw8PNDe3t7vDIOOjg5dZydfX19EREToFQ5/5coVzJgxA99++y1++OEHbNmyBS4uLgCAhx56CM3NzTh58mS/arqVZ555BseOHev2scTERGRmZiIjIwNBQUHYsGFDr9ff2NmHEEII0de9994Lb29vHDlypNfn9LSzXvv7sSdBQUGQyWRmuZBka2sLLy+vfnf3NjZtCH1tbS3S09NRWFhotFB5Q3E4HISEhCA7O5vVEPquXasbGhpw7tw5PPfccyY7FtkfVVVVGDt2LCIiIhAXF4epU6di8uTJWLZsGX7//XeIxWKcOHECy5YtG+xSCbntUSYXuW1duHAB06ZNQ1RUFLZu3YrAwMABj2mO2QtKpRJyuRzR0dEG3ZnqGirv4eEBf39/1u7kmSLw3VDNzc24cuUKYmJi9F7k1HaVLC8vNyirorKyEuvWrYNMJsPGjRtxzz339HhdcXEx3n//fbz//vsGfz59KSwsRFJSEjIzM2967MCBA9i/fz/27dt302MCgQBSqbRb90xy1zOPb2JyI5qDEbNVXl6OKVOmIDk5uVunYY1Gg/LycpSUlIDP54PH4+n9O1kul8PGxga+vr7GKrvfNBoNJBIJIiIibuqsPNg6Ojpw9epV1NbWIjw8fEA3Mo0lJycHDg4OuuN6/cUwDFJTU7FixQoIBAKsW7duwGMSMshoDmYEtJOL3Hby8/MxZ84crF27Fh988AHa29shEAhYGdvV1RVcLhfXrl1jZTw2ODs7w87ODtXV1Xpfo1QqcfHiRVy7dg1RUVEIDAxkdau6g4MDnJycUF5eztqYAzVkyBC4ubmhtLS0z+cyDIOqqipIJBJoNBq9sypaWlqwadMmzJgxAxMnTsTp06dx77339nqdn5+fURa4+vL5559jypQpPT7G4XAwceJEjBo1Crt37zZxZYQQQu4EPj4+mD17Nnbs2AHg/24apaSkoK2trV876wMCAlBaWspqxiZbtNlhbEQjsEWj0aCoqAhSqVQXPs92jitbhEIhiouLB/TftrS0FM8//zzWrFmD7du344svvqAFLkJIj2iRi9xWdu/ejSeeeALPPvssjh49itmzZ0MgEODAgQOsvUZQUBDkcrlZbZkXiUTIz8/vMxi/tbUVGRkZyM/PR0hIiEGh8obSTljMKYReIBCgvLwc7e3tvT5HuwBYW1uLqKgovbIq1Go19u3bh/j4eDg4OODChQuYNWuWWYbKr1u3DpaWlnjyySd7fPzs2bO4dOkSfv31V+zcuRNnzpwxcYWEEELuBEuWLMHBgwdx+PBhjB8/Hl9//TUiIiIgEon6dWzMwsICAQEBZpX72ZWbmxsYhtHlwQ6W3m7UicViveaKg8HS0hIBAQGQy+UGX9vY2Ig1a9Zgzpw5mD17No4fP27w6QZCyN2FjiuS20pNTQ3c3Ny6LS5UVlZi4sSJSE5OvinMtL8KCgrA4XBY2yHGhpKSErS3t0MkEt30WEdHBwoKCqBQKCAUCk22Vb2iogJKpRIhISEmeT191NTUoLKyEmFhYd0+3traCrlcjo6ODgQFBcHBwaHPsRiGwdmzZ7F69WrExMRg9erVcHd3N1bpBunpuOKXX36JTz/9FCdPntTre2HNmjVwcHAwWkA+uW3QXwrmieZgxKwVFhZi3rx5qKqqws6dO3HPPfcMeEyGYXDp0iWIxWI4OTmxUCW7tDcTY2NjB+VGl0KhgEwmg4ODA4RC4U2ZWyUlJWhra4NYLDZ5bX3RHjUMDAy8ZT6bVmdnJ7766it8+umneOGFF/DPf/4TVlZWJqiUEJOiOZgRmN82BEJuwd3d/aZJhZeXF+bOncvqsTA/Pz9UVlbeckeQqfF4vJuC8btuVXdwcEBsbKxJsxjMMYTe3d29W5emzs7Om7pK6rPAlZubizlz5mDnzp344osv8PHHH5vNAldPjh07hs2bN+Pw4cO9LnD11tmHEEII0Vd9fT3+9a9/YdasWXj77bchEAhYCxXncDgICgpCbm4uq0HlbLGzs4O7uztKSkpM+rotLS3IyMhAQUEBQkNDERoa2mOoPJ/PR319PZqbm01anz44HA6Cg4ORm5t7y9MS2q7VEyZMQFFREc6cOYP/9//+Hy1wEUL0Rju5yB1BpVJhzJgx+O9//ws/Pz9WxqyurkZ1dbVZLQLU19ejsLAQkZGRqK6uRkFBATw9PeHn5zdo7aHNMYS+tbUV6enp8PHxQVlZGXx9fcHj8fSqr7a2Fhs3bsTFixexfv16TJgwwWw+L605c+bg9OnTuHbtGjw9PbF27Vps2LAB7e3tukXOMWPGYNeuXSgvL8dzzz2Ho0ePIj8/HzNmzABwffHviSeewPLlywfzUyHmwbze4ESL5mDE7JSUlCApKQmLFy/G008/DQsLC2RlZWH+/Pk4ceIEa3OR7OxsODs7w9vbm5Xx2KRWq5GSkoKoqCijRUJodXR0ID8/H0qlEiKRCEOHDu3zGqVSiby8PERFRZnd/AW43mDA2tq6x/n6lStXsGLFCri4uGD9+vUQCoWDUCEhJmV+36R3AFrkIneMo0ePYs+ePfjqq69Y+aVu6LZqU7l06RLa2trg6uqKwMBAo0+w9MFW1xw2MAyDa9eu4erVq7C3t0dkZKRe2SDt7e3YvXs3vvnmGyxdulQ3eSfkLkATLPNEczBidhiGQXt7O2xtbbt9fPHixRCJRJg/fz4rr9PR0QGpVIrY2Nh+5XsZW01NDaqqqox2I7Rr92d/f3+9muN0lZWVBTc3N3h6ehqlvoFQq9XYvXs3HnzwQfj7+wMAqqqqsG7dOuTm5mLDhg23bOpDyB2G3uhGQMcVyR1jypQpUKlU+PPPP1kZr+u2anPYMq/dqs4wDBiGQVBQkFkscAHmE0Lf2NiI1NRUVFVVISYmBp2dnX3WpNFocODAAUyYMAFtbW04f/48nnnmGVrgIoQQQm7A4XBuWuACgNWrV2PXrl1QKBSsvI6VlRX4fD4KCgpYGY9t7u7u6OjoQH19Pavj9hQq7+PjY/CCj7ZhkTl2W7SwsICrqysWL16MlpYWbN68GQ8//DAeeOABnD59Gv/4xz9ogYsQMiC0k4vcUXJzc/HEE08gOTmZtTt/ubm5GDJkyKDtUuppq3phYSE0Gg0CAwMHpaaeVFRUQKFQIDQ01OSv3dbWhry8PF3Yqjastq6uDsXFxYiMjLzpGoZhIJVKsXLlSojFYrz77rvw8fExdemEmAP6a8I80RyM3Fb+85//ICMjA5s2bWJlPIZhkJKSgpEjR7LWWIhNLS0tyMzMRExMDCsh9F1D5dnYqV9aWorW1lazDKFXq9WIj49He3s7nnrqKSxcuLDHxVNC7gI0BzMC2slF7ihBQUGIj4/H559/ztqYgYGBg7JLqWuovJOTE2JjY3VZDH5+fqiurkZra6tJa7oVLy8vtLS0mDSEXq1WIy8vD2lpafDw8EB0dHS3bkxDhw4Fl8vF5cuXu11XXFyMBQsWYP369dixYwf27NlDC1yEEEJ7wU1MAAAgAElEQVTIACxYsABSqRRZWVmsjMfhcCAWi5GTk8PKeGyzt7fH0KFDUVZWNqBxegqVZ2OnPo/HQ319PZqamgY8Flu0XasnTpyI0NBQcLlcvPbaa4OywKVWqxEVFYWkpCQA1zurjx49GiKRCLNmzYJKpTJ5TYQQdtAiF7njrFy5Env27NF11xsoS0tL+Pv7Iz8/n5Xx+sIwDCorK7ttVb8xi4HL5UIsFiM3N9ckNelDe7wzJyfH6Mc7GYZBWVkZJBIJrK2tERcXB3d39x63t7u5ueGpp55CU1MTlEolVq1ahaeeegpz587FsWPHEBERYdRaCSGEkLuBhYUFtmzZgrfeeuuW3fMM4erqCktLS9TU1LAyHtsCAgJQWlrarwWRjo4O5ObmIjMzEzweD1FRUXp1f9aXKedl+pDJZHjiiSfw8ccf4/PPP8dXX32F2bNnY/v27YNSz4cfftjt9MGbb76JxYsXQy6Xw9XVFXv27BmUugghA0eLXOSO4+joiKVLl+K9995jbUxvb280NDQY/W6YQqGAVCqFQqFAdHQ0AgICes2G0nbRq62tNWpNhnBwcICzszPKy8uN9hq1tbWQSCRoaWlBTEwMfH19b3lMgMfjYebMmXj++ecxceJEBAYG4ty5c5g6dSplPhBCCCEs+sc//gFPT08cPXqUtTHFYjHy8vJYWzhjk4WFBQIDAyGXy/W+putOfQcHB8TGxurmdGxzdnaGnZ0dqqqqjDK+Pmpra/HGG2/gxRdfxMKFC3Ho0CHd4tKSJUuwf/9+FBUVmbSm0tJS/PLLL3juuecAXL95mpycjJkzZwIA5s2bh4MHD5q0JkIIe2iRi9yR5s6di8zMTGRmZrIyHofDQVBQkNHuhrW0tCA9PR2FhYUYPnw4QkJCYG1t3ed1QUFBkMlkZjXxM9bxzqamJqSmpqKsrAzh4eEQi8WwsrK65TUMw+C3337D8ePHcfHiRezduxcvv/xyn9cRQgghxHAcDgebNm3C+vXr0dbWxsqYtra28PT0RHFxMSvjsc3DwwNtbW1QKpW3fF7XUHm1Wt3vUHlDiUQiFBQUmDyEvr29HTt27MCDDz6IUaNG4a+//kJCQkK3z9fa2hpbtmzBhg0bTFrba6+9hs2bN+tuktbW1sLFxUWX58vn8wd8DJUQMnhokYuYBYFAgLCwMERGRiImJgbA9dDwxMREiMViJCYmGtTBhsvlYtu2bVi2bBlrC0DOzs6wtbVldcu8SqVCdnY2MjMz4efnh8jISAwZMkTv6+3s7ODh4WFWEz9LS0sEBAQYdFfzVlQqFa5evYqrV68iICAA4eHhsLOz6/O6y5cvY8aMGfjxxx/xv//9D/v378fbb79ttC37CxYsgIeHR7d24vq+h/fu3QuxWAyxWIy9e/capT5CCCHEFHg8Hh577DF8/PHHrI3p5+eHyspKtLe3szYmW7Q3Qm/VjVupVOLixYuora1FVFQUAgMDTdbF2draGr6+viaL3dBoNDh48GC3rtXz58/v9fOdMGECduzYYZLaAODIkSPw8PDAqFGjTPaahBDTokUuYjZOnTqFtLQ0SKVSAMDGjRuRkJAAmUyGhIQEbNy40aDx4uLi4Ovri0OHDrFWo0gkQl5eHtRq9YDGUavVKCwsxMWLF+Hs7IzY2Fi4urr2ayx/f39UVlaydseUDZ6engMOoVer1SgoKMClS5cwdOhQxMTEwMXFpc/rKisr8fLLL+ONN97Au+++i3379sHf3x9jxoyBr68v9u/f3++abuWZZ57BsWPHun1Mn/dwXV0d1q5diwsXLkAikWDt2rWstyQnhBBCTGnp0qX46aefWIsvsLCwgFAohEwmY2U8tvUW19Da2oqMjAzk5+cjJCQEw4cPZyVU3lA8Hg9KpdKosRvartVTp07FiRMncPToUaxevVqvm7em3GH/119/4fDhwxAIBJg9ezaSk5OxaNEiKBQK3W630tLSQeuqTggZOE4fuxoGP6WQ3BUEAgGkUimGDRum+1hwcDBOnz4Nb29vVFRUYPz48QZ32KmoqMCkSZOQnJzMWvvpoqIidHZ2QigUGnytNlS+sLAQ3t7e8PX1ZeVOXk1NDSorKxEWFjbgsdjS1NSEq1evIiYmxqCt+Dd+jfz8/PRqzd3c3IyPPvoIP//8M1asWIFHHnnkputqa2sRHx+Ps2fPwtHR0eDPqS+FhYVISkrSHZPV5z387bff4vTp0/j0008BAC+++CLGjx+POXPmsF4fIb2gcDrzRHMwcls7cOAAfvjhB3z22WesHMljGAZpaWkICAjQ66aXqXV2diIlJUV3IqGgoAAKhQJCodBomVuGaGhogEwmQ3R0NOtHJIuLi7F69WrU1dVhy5YtiIyMZHV8Yzl9+jS2bt2KI0eO4LHHHsOjjz6K2bNn45///CfCw8Px8ssvD3aJ5M5HczAjoJ1cxCxwOBxMnDgRo0aNwu7duwEAVVVV8Pb2BgB4eXn1KzTT29sbTz31FD788EPWavX19UVNTQ1aW1sNuq6+vh5SqRRKpRKjRo2CQCBgbau6u7s7Ojs7zWoHUH9C6Hv6GvW1wKVWq/H1118jPj4eLi4uuHDhAmbOnNnjdW5ubvjvf/+r13FHNujzHi4rK4Ovr6/u35QDQQghxBRKSkowYcIEDB8+HCNGjNDNlXo7as8wDBYuXAiRSITw8HBcunTpluNPnz4ddXV1uHDhAiv16nMscDBpu3FrTyUYO1TeUE5OTrC3t0dlZSVrYzY0NGD16tV48skn8fTTT+P48eO3zQLXjTZt2oTt27dDJBKhtrYWzz777GCXRAjpJ1rkImbh7NmzuHTpEn799Vfs3LkTZ86c6fY4h8Pp912nRYsW4ciRIygtLWWjVHC5XIjFYr23zDc3NyMtLQ3FxcUGhcobKjg4GLm5ubdlCL02eN+QrxHDMPjjjz+QkJCAK1eu4NSpU1i6dGmfxwBGjBihCxY1pYG8hwkhhBC2WVpaYtu2bcjKysL58+exc+dOZGVl9XrU/tdff4VMJoNMJsPu3bvx0ksv3XJ8LpeL7du34+233x5wzIPWkCFD4OLiYnY3g7Sh8kVFRWhra0NoaKhJQuUNJRKJUFhYOOAQ+o6ODvznP/9BYmIiAgICcP78eSQlJZnd59uX8ePH48iRIwCuz1klEgnkcjl+/PHHQTlWSghhBy1yEbOgPffu4eGBGTNmQCKRwNPTExUVFQCuHzv08PDo19g2NjZ49913sXz5ctbu/Lm5uYFhGNTV1fX6HG2ofFZWFvz9/REREWFQqLyh7O3t4ebmxtpiHhv6CqHv6OhATk4OMjMz4evrq/fXKCcnB7NmzcLu3bvx9ddf46OPPup21NVc6PMe5vF4KCkp0f2bciAIIYSYgre3N6KjowEAjo6OCA0NRVlZGQ4dOoR58+YBAObNm4eDBw8CAA4dOoS5c+eCw+FgzJgxUCgUut9xvRkxYgRGjx6Nb775hrW6AwMDUVJSwnoX5/7qGiofHR2NyMhIyOVys9xtZmVlBT8/P+Tl5fXrem3X6vj4eJSXl+PPP/+krtWEELNDi1xk0DU3N6OxsVH3/48fP46RI0di2rRpuk5ze/fuxfTp0/v9GlOnTkVLSwvOnj3LSs0AEBQUBJlMdtPOqa6B6S4uLoiJiel3qLyhAgICUFZWBpVKZZLX04c2hL5ra22NRoOioiJIpVI4OTkhNjYWQ4cO7XOsa9euYenSpXj55Zfx+uuv43//+x+Cg4ONWf6A6PMenjRpEo4fP476+nrU19fj+PHjmDRpkqlLJYQQchcrLCxEamoqRo8e3etR+/4er1+zZg3+/e9/Q6FQsFKr9ligqboF9qa3UHlHR0c4ODiweiyQTT4+PmhoaNDNvfV1+fJlPPzww/jxxx/x008/YdOmTWaZjUYIIbTIRQZdVVUVxo4di4iICMTFxWHq1KmYPHkyli1bht9//x1isRgnTpzAsmXL+v0aHA4H27dvx4oVKwa8RVvLzs6u284phmFQUVEBiUQCDoeDuLg4eHl5mXTrtoWFBQIDA82q+xCHw+l2lLKqqgoSiQQajQZxcXHw9vbu82vU1taGDz74AElJSbjnnntw9uxZjB8/3qy2xc+ZMwf33HMPcnJywOfzsWfPnl7fw1KpFM899xwAYOjQoVi5ciViY2MRGxuLVatW6bXgRwghhLChqakJjz76KD744AM4OTl1e4yNo/aurq549dVXDe6SfSve3t5oaGgwarfA3nR0dCA3NxcZGRng8XiIioqCg4NDt+cIhUJWjgUag3ZelpOTo9dus8rKSrzyyivdulYLBALjF0oIIf1E3RXJXeX111+Hn5+fboFhoNRqNSQSCQIDA1FUVARnZ2cEBAQYJXNLXwzDIDU1FUKhEM7OzoNWx40uX76MhoYGuLq6QigU6pV1oNFocODAAWzduhWPPfYYlixZwlqXTEKIjvmsFpOuaA5GjK6jowNJSUmYNGkSlixZAqD3zsA3dv/t+ry+qNVqjB07Fjt37kRISAgrtSuVSsjlcqN0C+yJRqNBaWkpysrK4Ofn12fmVllZGZqbmxEUFGT02vojOzsbzs7Ovf73a25uxo4dO3D48GEsX74cjz76qF7drgkhBqE5mBHQTypyV1m5ciX+85//3DJLyxBtbW3gcrnIzc3FyJEjERwcPKgLXMD/dR/S9w6dsbW2tuLy5cu6I5QikajPBS6GYXDhwgVMmTIFf/75J3777TesWLGCFrgIIYQQljAMg2effRahoaG6BS6g96P206ZNw1dffQWGYXD+/PlbLpDcyMLCAps3b8Zbb73F2tzE2dkZdnZ2qK6uZmW83jAMg+rqakgkEnR0dCAuLg48Hq/PhTUfHx8olcpB2W2mD+1usxuzzdRqNb755hskJCTAyckJFy5cwGOPPUYLXISQ2wbt5CJ3nc8//xxSqRRbt27t9xgqlQp5eXloamqCSCRCfn4+xGLxTdv8B1Nubi7s7e3B5/MH5fU7OztRUFCAuro6iEQiuLm5obKyEnV1dRg+fHiv1xUWFmLVqlVobm7G5s2bERYWZsKqCbkr0V1E80RzMGJUZ8+exbhx4xAWFqZbwFi/fj1Gjx6Nxx9/HMXFxfD398cPP/yAoUOHgmEYvPrqqzh27Bjs7e3xxRdfICYmRu/XYxgGTz75JB555BE8+OCDrHwOKpUKFy9eRFxcHCwsLFgZsyulUgmZTAZ7e3u9d6HfeL0pd5sZ6uTJkzhw4AA+/vhjMAyDP//8E6tXr8bo0aOxatUqs2zqQ8gdxvx+MNwBaJGL3HXUajXGjRuHDz/8ECNGjDD42uLiYlRVVSEgIAAeHh7gcDhobGxEdnY2YmJizGYS09nZiZSUFMTExJi0641Go0FZWRlKS0tv2s7PMAwuXboEkUh001FKhUKBrVu34s8//8Q777yDyZMnm83XkpA7HH2jmSeag5E7TmlpKZKSkpCcnAxbW1tWxiwuLkZHRweEQiEr4wHXd6HL5XJ0dHRALBbD0dGx32NlZWXBzc0Nnp6erNXHFrVajfvuuw9Lly7F999/DwsLC2zatMmsm/oQcoehOZgR0L5TctexsLDAtm3bsGzZsps6I/aGYRiUl5dDIpGAy+UiLi4Onp6eukUYR0dHODo6mlUnHUtLSwgEgn63iTYUwzCoqalBSkoK2tvbERsbe9N2fu1RyjfeeANqtRrA9TyQXbt2YdKkSQgODsa5c+cwZcoUWuAihBBC7jB8Ph+PPvoodu7cyeqY165dQ2tr64DH6ujogEwmQ0ZGBnx8fBAdHT2gBS4Auh3/2nmPOamvr0d4eDiWLl2KJUuW4MCBAyZd4Gpra0NcXBwiIiIwYsQIrF69GgBQUFCA0aNHQyQSYdasWWbVNZwQYv5okYvclcaMGQMej4eff/65z+fW1dUhJSUFjY2NiImJgb+/f4+5BObYScfLywtNTU0Gt4k2VGNjI1JTU1FVVYWIiAiIRCJYWlr2+FxHR0dYWlrigw8+wNGjRzFhwgRcu3YNf/31F1588cVeryOEEELI7e/111/H/v37UVFRwcp4XC4XIpEIubm5/R5Do9GguLgYUqkU9vb2iIuLg5ubGyv1WVtbg8/no6CggJXx2NDW1oYPP/wQSUlJiI+Px6xZs1BaWmryG4w2NjZITk5Geno60tLScOzYMZw/fx5vvvkmFi9eDLlcDldXV+zZs8ekdRFCbm90XJHctcrLyzFlyhQkJyfDzs7upsebmpogk8lgYWEBkUikV+h5aWkpWltbIRaLjVFyvzQ2NiInJwejRo1iffLS1taGvLw8tLW16Z1JxjAM/v77bzz99NO47777sHXrVvj5+bFaFyHEILRt0jzRHIzcsf73v//hp59+wu7du1mbm6Snp4PP5xu0OKXdhZ6fnw93d3f4+/sb5WYbwzBISUnByJEjB7WJTteu1TNnzsTSpUthb28PhUKB+++/H3/88QdcXFwGpbaWlhaMHTsWn3zyCaZOnYrKykpYWlri3LlzWLNmDX777bdBqYsQI6M5mBHQTi5y1/Lx8cGcOXPw0Ucfdft4e3s7srKykJ2djYCAAISHh+s9IeHxeKivr0dzc7MxSu4XR0dHODg4sHqUUq1WIy8vD2lpafDw8EB0dLReC1zl5eV46aWXsG7dOixcuBAuLi4mWeDKyclBZGSk7n9OTk744IMPuj3n9OnTcHZ21j3nnXfeMXpdhBBCyN3o4YcfRk1NDSQSCWtjBgUFQS6X6x1FoVQqcenSJdTU1CAyMhJCodBou8m7dr4eDF27Vv/xxx84duwYVq5cqZvfuri44PXXX8eqVatMXptarUZkZCQ8PDyQmJgIoVAIFxcX3X8LPp+PsrIyk9dFCLl90bkgcldbvHgxxowZgyeffBLOzs7Yt28fIiMjERAQgNDQUIPvLmonMbm5uYiKijJS1YYTCoWQSqVwd3cf0AROm01WXFwMPp+PuLg4vVpKNzU14YMPPsCvv/6KlStX4uGHHwaHw8GkSZOQkpKC2NjYftekj+DgYKSlpQG4Ppni8XiYMWPGTc8bN24cjhw5YtRaCCGEkLsdl8vF9u3b8fzzz+P48eOsdEa0s7PDsGHDdI1vetM1VD4oKGjAmVv6cnFxgZWVFWpqauDu7m6S1wSAoqIirFq1Co2Njdi5cyfCw8N7fN5TTz2F7OxsqNVqo3Sq7I2FhQXS0tKgUCgwY8YMZGdnm+y1CSF3JtrJRe5qNjY2WLVqFV599VXce++9uHbtGmJjY7uFyhtKe/eppqaG5Wr7z8rKCr6+vsjPz+/3GLW1tUhJSUFLSwtiYmLg6+vb5wJXZ2cn9u7di4SEBHh4eODChQt45JFHwOVyweFw8NFHH+G1114zaRjryZMnIRQK4e/vb7LXJIQQQkh3I0eORExMDPbt28famAKBAGVlZT0GlXcNlff29mYlVN5QYrEYeXl5Jpn3KBQKrFixAk8//TTmz5+PX3/9tdcFLuD6jdp169aZdIGrKxcXF0yYMAHnzp2DQqHQZdyWlpaCx+MNSk2EkNsTLXKRu9qJEyewceNGFBcXY/PmzVixYgUrv9y1kxh9t8ybAo/Hg0KhQFNTk0HXNTU1ITU1FWVlZQgLC4NYLIaVldUtr2EYBqdOnUJCQgJyc3Pxxx9/4LXXXoO1tXW354WEhCAhIQEXL140+PPpr++++w5z5szp8bFz584hIiICU6ZMwZUrV0xWEyGEEHI3Wrt2LT7++GMolUpWxrOwsEBgYCDkcrnuYxqNBiUlJZBKpbCzs0NcXByGDRvGyusZysbGBt7e3igqKjLaa3R0dODTTz/FpEmTEBQUhPPnz+PBBx80y67VNTU1UCgUAK7vsPv9998RGhqKCRMmYP/+/QCAvXv3Yvr06YNZJiHkNkPB8+SulJmZiTfffBPOzs5Yt24d2tra8Mwzz+D3339nLY9B20UnICCAlfHYoFQqIZfLER0d3edkR6VSIS8vD01NTQgKCoKzs7Ner3H16lWsXLkSdnZ22Lhxo1mF8KtUKvj4+ODKlSvw9PTs9lhDQwO4XC4cHBxw9OhRLFq0CDKZbJAqJcSkzO8vHwLQHIzcJXbt2oXs7GysX7+elfEYhsGlS5cgEonQ3t5u9FB5Q2k0GqSkpCA8PLzHxkcDGfe3337DunXrMHnyZCxbtkyvvNTBlJGRgXnz5kGtVkOj0eDxxx/HqlWrkJ+fj9mzZ6Ourg5RUVH45ptvYGNjM9jlEmIMNAczAlrkInelt956CzNmzEBcXJzuY0uWLEFgYCAWLFjAymtoNBpIJBJERkbC1taWlTHZcOXKFQwbNuymRR4ttVqN4uJiVFVVISAgAB4eHnrd/auursb69euRmZmJjRs3Yty4cWZ31/DQoUPYuXMnjh8/3udzBQIBpFLpoN3tJcSEzOsblWjRHIzcFTo7O3Vd9YKDg1kZs7KyElevXoW7uztEIpFZzcMAoK6uDiUlJYiIiBjwWAzDICMjAytWrIC3tzfWr19PXasJuX3QHMwIaJGLkP+fQqHAuHHjcOzYMbi6urIyZk1NDaqqqjBy5EhWxmODSqXCxYsXERcX1+1oJsMwqKysRGFhIby9veHn56dXqHxrays++eQT/PDDD3jzzTcxZ84cva4bDLNnz8akSZMwf/78mx6rrKzUZbFJJBLMnDkTRUVFZrdQR4gR0JvcPNEcjNw1/vjjD2zcuBH79+8f0O/dtrY2yOVyqFQqWFlZwc3NDT4+PixWyp6MjAzweDy4ubn1e4yKigqsXbsWxcXF2LJlC2JiYmjeQsjthb5hjcA8/xIlZBC4uLhg0aJFrG2XBwB3d3d0dHTo8gbMgbW1NXg8nu44JQDU19dDKpVCqVRi1KhREAgEfS5UaTQa/PDDD4iPjweXy8WFCxfw5JNPmu0CV3NzM37//Xc88sgjuo/t2rULu3btAgDs378fI0eOREREBBYuXIjvvvuOJoqEEEKICdx3331wdnbGsWPH+nV9Z2cnZDIZ0tPT4eXlhaioKISEhKCoqEgXYG5ugoKCIJPJ+pXf2tTUhHXr1mHmzJmYPn06kpOTERsbS/MWQggB7eQipBu1Wo1x48bho48+wvDhw1kZs7m5GVeuXDGryYc2D0IkEqG0tBQAIBKJMGTIkD6vZRgG58+fx6pVqxAWFoa1a9f2evSREHJbMI8fTORGNAcjd5Xi4mJMmzYNp06d0jt/SaPRoKysDKWlpfD19YWPj0+3m22lpaVobW01q3zQrgoKCsDhcCAQCPR6vlqtxr59+7Bz507Mnz8fr7zyCmVVEXJ7ozmYEZjnlgtCBomFhQW2bNmCZcuWsdYZcciQIXB1dUVZWRkr47FBrVbD1tYWGRkZ8PPzQ0REhF4LXAUFBZg7dy62b9+OTz/9FLt27aIFLkIIIYQMmJ+fH2bMmIF///vffT6XYRjU1NRAIpGgvb0dsbGx4PP5N+0m5/F4qK+vR3Nzs7HKHhB/f39UVlaira3tls9jGAanT59GQkICsrOzcfr0aSxZsoQWuAghpAe0k4uQGzAMg7lz5yIpKQkPPfQQK2N2dnYiJSUFMTExsLKyYmXM/tC20S4vL4dAIMC1a9fg5eUFd3f3W15XX1+PzZs349y5c3j33XcxceJEs9mVRggZMPpmNk80ByN3ndbWVowZMwYHDhyAl5dXj89paGiATCaDra0thEJhn6HyCoUCBQUFiIyMNMu5y7Vr11BRUYGwsLAeH8/OzsbKlStha2trdl2rCSEDZn4/lO4AtMhFSA/Kysrw4IMP4tSpU6x15KmoqIBSqURISAgr4xmCYRhUV1ejoKAAnp6e8PPzg4WFBdra2pCWlobY2NhuIfRaKpUKe/bswZdffomFCxdi/vz5ZtF+mxDCKppgmSeag5G70v79+3Ho0CHs2rWr26KUNlS+vb0dQUFBcHR01HvMzMxMeHp69nlTb7CkpqbC2dkZgYGBuo/V1NRg/fr1uHz5MjZs2ID77rvPLBfpCCEDQt/URkDHFQnpAY/Hw6xZs7Bjxw7WxvTy8kJjYyMaGxtZG1MfSqUSFy9eRG1tLaKiohAQEKBb0LK1tYWXlxfkcnm3azQaDX7++WdMmDABCoUC586dw/PPP08LXIQQQggxqkceeQQVFRWQSqUAru8mz8nJ0YXKR0dHG7TABVzPHc3Ly2MtioJtzs7OmDlzJlQqFdra2rB9+3Y89NBDGDduHM6ePYv777+fFrgIIURPtJOLkF60tbVhzJgx+PHHH8Hj8VgZU7vFPjo62uiTldbWVsjlcnR2dkIsFsPBwaHH52k0Gtx///34/PPPIRKJkJaWhpUrV4LP52P9+vXg8/lGrZMQMujoLyfzRHMwcte6fPkyXnzxRUycOBFff/01PvnkE9x7770D6uBcWFgIhmEQEBDAYqXsWbp0KVpaWpCamopZs2ZhyZIlsLOzG+yyCCHGRXMwI6BtGYT0wtbWFmvWrMHKlSuxZ88eVhalnJycYGdnh+rqaqMFtnd2dqKgoAB1dXUQiURwc3O75fO5XC6WLl2KhQsXgs/no7KyElu2bDHJQhwhhBBCSFcMw6CoqAj5+flIT0/HmTNn4OrqOuBx/fz8IJFI4O3tzVoUBRu0XasvX76M3NxcJCcnY+TIkSavo6SkBHPnzkVVVRU4HA5eeOEFLFq0CHV1dZg1axYKCwshEAj+v/buPSjK82wD+LW4BRXUAZVDgApkFwSUAgoekigKxExFQLBETRWDmKpVUmPaEA0IRAOIUFKhJjaOQ7UDYZoCJigqWGpiAiinVkk5ihyCOxEROSin3e8Pv2xFzrgLi16/GWfc9/C89+LI3nu/z3s/SE5OVsi/BxGRsvBxRXqm3Lt3D+vWrcPcuXNhaWmJ7777Dnfv3oWrqyvEYjFcXV3R1NQ07PHc3d1x79495OTkKCxGkUiEqqoq9PT0KGxM4H9N5cme3SYAABYLSURBVK9evYqpU6fC0dFxyAIXALS0tKC4uBiVlZWwsLDAxYsXsWDBAha4iIiIaEwVFhbi1VdfxRdffIHMzEyUl5f32zN0NNTU1CASiVBeXq6Q8RThp1Wro6Oj8cknn+D48eOIiYkZl1iEQiGio6NRUlKCnJwcxMfHo6SkBBEREXB2dkZ5eTmcnZ0RERExLvEREQ0Xi1z0THn77bfx2muv4b///S+Ki4thaWn5VB/OampqiImJwf79+xVWlFJXV4ehoSGqq6sVMt5Py2hfvXoVnZ2dcHBwgKGh4ZBFqu7ubpw8eRIuLi4wNDREdnY2zpw5g+7uboXENRQTExPMnz8ftra2WLhwYZ/9MpkMAQEBEIlEsLGxQUFBwZjERURERGMvISEB7733HqKionDy5EnMmzcPO3fuxOHDhxV2jVmzZqG7u3tENzyVoampCfv27cOWLVvw1ltvIT09HfPmzYOHhwcaGhoUenN1uAwMDGBvbw8AmDZtGiwtLVFfX4+0tDT4+voCAHx9fZGamjrmsRERjQR7ctEzo7m5Gba2tqiqqupV4LGwsEB2djYMDAzQ0NAAJycnlJaWjmjs3/3udzA3N8eWLVsUEqtUKsXVq1dhY2PzVP0WWlpaUF5eDg0NjWEtow08Kh5lZWUhLCwMy5cvxwcffCCfdn7w4EFMmTIFe/fuHXVMw2ViYoJr165h1qxZ/e4/e/Ysjh49irNnzyI3Nxdvv/02cnNzlR4X0XOI0zZVE3Mweq50dHRAXV29Vw7X3d2Nl156CcePH4dYLFbIddrb23H9+nU4ODiM+az1zs5OfPbZZ0hISMDu3bvh5+fXZ1Gf0tJSvPnmm/j6668VNottpKqrq7Fs2TJcv34dP//5z3Hv3j0Aj3JIbW1t+WsiemrMwZSAM7nomXHz5k3Mnj0bb775Juzs7ODv74+2tjZIJBIYGBgAeLTCoUQiGfHYBw4cwLFjxxT2oa6mpgaxWIyysrJRnf/w4UPcuHEDZWVlEIlEsLa2HlaBq6SkBF5eXjh9+jQ+//xzREdH9+qr8O677+L06dNoaGgYVVyKlJaWhs2bN0MgEGDx4sW4d++eSsRFREREiqehodGn6CQUChEZGYn3338fQ9yYH7apU6dCW1sb9fX1ChlvOKRSKb766iv5qtXffvst3nrrrX5XrbawsMCBAwfGbGb9k1pbW+Ht7Y3Y2FhMnz691z6BQMB2FkSk8ljkomdGd3c3CgoKsGPHDhQWFkJTU7PPo4mj/XDW1tbG7t27ER4erqhwoaOjAwBobGwc9jk9PT2orKxEUVERdHV1YW9v3ycB6Y9EIkFAQAD27NmDoKAgfP7553jxxRf7HDd58mTExsaOyR06gUCAV199FQsWLMDx48f77K+vr4exsbH8tZGR0ZgmpERERDT+li9fDi0tLVy4cEFhY5qamqK2thZdXV0KG7M/MpkMhYWFcHd3x5dffokzZ87g4MGDmDZt2qDnrVq1ChoaGkqNrT9dXV3w9vbGG2+8AS8vLwCAnp6e/CZjQ0MDdHV1xzwuIqKRYJGLnhlGRkYwMjLCokWLAADr1q1DQUGBwj6ct27dimvXruH7779XWMzm5uaoqKiAVCod9DiZTIb6+nrk5eVBXV0djo6OmD179pAFuwcPHiAqKgqenp5wdnbGv/71L7z88suDnrd8+XJYWlqO6v2MxDfffIOCggKcO3cO8fHxuHz5stKvSURERBOLQCBAVFQUwsLC0NHRoZAxhUIhTExMUFlZqZDx+lNfX4/f/OY3CAoKQlRUFP7617/2unmnamQyGbZu3QpLS0u888478u3u7u5ISEgA8KhvmoeHx3iFSEQ0LCxy0TNDX18fxsbG8n5bWVlZsLKyUtiH86RJk3D48GG8//77QxalhmvKlCmYNWsWamtrBzymsbERV69eRXt7OxYuXAhjY2OoqQ3+X1cqlSIpKQkrVqyAhoYGcnNzsWHDhiHPG0uGhoYAAF1dXaxduxZ5eXl99j/+c6mrq5OfQ0RERM+POXPmwMPDA5988onCxtTX10draytaWloUNibwqF9qWFgYfHx84O3tjczMzAmxavWVK1dw6tQpXLp0Cba2trC1tcXZs2cRGBiIixcvQiwWIzMzE4GBgeMdKhHRoNh4np4pRUVF8Pf3R2dnJ8zMzHDy5ElIpVL4+PigpqYGc+bMQXJysvxRwZGSyWT49a9/DU9PT6xevVohMff09CAvLw8LFiyAurq6fHtra6t86WyxWDysBvUymQzffvstgoODYWdnh5CQEJWcVt7W1gapVIpp06ahra0Nrq6uCA4OxmuvvSY/Jj09HXFxcfLG8wEBAX0KYUSkEKr9zev5xRyM6DHt7e1YsmQJUlJSoK+vr5Ax79+/j/Lyctjb2z91Eaq7uxunT5/GsWPH4O/vjx07dvTK64iI+sEcTAlY5CIaobq6Ori5ueHSpUvDavY+HBKJBHfu3IG1tTU6OztRWVmJ1tZWmJubY8aMGcMao6KiAsHBwejp6UFkZCSsrKwUEpsyVFVVYe3atQAeJYUbN27E/v375Xdot2/fDplMhl27diEjIwNTp07FyZMnsXDhwvEMm+hZxQRLNTEHI3pCcnIy0tPT8ec//1lhM6NKSkqgo6Mz6sKZTCbDpUuXEBoa2mfVaiKiITAHUwIWuYhG4eDBg5BKpXj33XcVMp5MJkN+fj40NTXR3NwMU1NT6OrqDiuBu3v3LiIjI5GXl4dDhw7B2dlZ5afEE5FK4S8M1cQcjOgJUqkULi4uCAkJUdiNr87OTuTn58PR0RGTJk0a0bklJSUICgqClpYWwsPDIRKJFBITET03mIMpAYtcRKPw8OFDLFq0CP/4xz9gYGDwVGPJZDLcvn0bVVVVkMlkWLJkybCSrI6ODvzlL3/BqVOnsGfPHvj6+o44OSMiAhMsVcUcjKgfxcXF2LlzJ86fP6+wXqM1NTXo7OwcdpFKIpHg0KFD+P777xEZGYmXXnqJNxiJaDT4i0MJVKcLNdEEMnnyZISEhCAoKAhDFIoH1dTUhGvXruH+/ftwcHDA7NmzIZFIBj1HKpUiLS0NK1asQFtbG3JycuDn58cCFxERET3zfvGLX8DGxgaJiYkKG9PIyAiNjY1ob28f9LgHDx7gyJEj8PT0xMqVK/H1118PuWo1ERGNLRa5iEbJw8MDjY2No2qG3t7ejuLiYtTU1MDKygoWFhZQV1eHmZkZbt26he7u7j7n/PRI4+rVq3H+/Hmkp6cjNDQUmpqaing7RERERE+ttrYWK1asgJWVFaytrfHxxx8DAEJCQmBoaNhr5b6f/PSon4WFBc6fPz/kNT788EP86U9/UtjKiGpqahCLxSgrK+t3/+OrVqurqyM3NxcbN25UqVWriYjoET6uSPQUbty4AX9/f1y4cGFYM6m6urpQVVWF5uZmiMXifhuT1tbWIj8/H56enr22hYSE4Mcff0RUVBTs7OwU+j6I6LnGKQiqiTkYTUgNDQ1oaGiAvb09WlpasGDBAqSmpiI5ORlaWlp9+pmWlJRgw4YNyMvLww8//AAXFxeUlZUNmVfFxcWhuroaYWFhCos9JycHAoEAixYtAtB71WpbW1uEhoaq5KrVRDRhMQdTAuF4B0A0kVlbW8PBwQF/+9vfsHnz5gGPk0qlqK2txQ8//AATExOYm5sPOLX9hRdegLe3N0xNTWFqaoro6GhkZWUhNDQUq1ev5l1DIiIiUlkGBgbyfqXTpk2DpaUl6uvrBzw+LS0N69evh4aGBkxNTSESiZCXl4clS5YMep3t27dj6dKlqKioUFjD95kzZ8LHxwc5OTmora3FgQMH0NXVhRMnTqj0qtVERPQ//LZM9JRCQkIQFxeH5ubmPvtkMhkkEgny8vIglUrh6OgIAwODQXs3TJo0CR9++CF27NgBFxcXzJkzB7m5uVizZg0LXERERDRhVFdXo7CwUD4zKi4uDjY2NvDz80NTUxMAoL6+HsbGxvJzjIyMBi2K/UQoFCIyMhL79u17qv6ojxOLxVixYgU2bdqEbdu2YefOnfjyyy/HtMDl5+cHXV1dzJs3T77t7t27cHV1hVgshqurq/xnR0REffEbM9FT0tHRwa5duxAREdFre3NzM/Lz89HY2Ag7OzuYmpoOOfVeJpPhwoULOHToEHp6erB//37s2rULP/vZz5T5FoiIiIgUqrW1Fd7e3oiNjcX06dOxY8cOVFZWoqioCAYGBti7d+9TX8PJyQmTJ09GZmbmU4/V2dmJ+Ph4fPfdd/j3v/+NlJQUuLq6jnlT+S1btiAjI6PXtoiICDg7O6O8vBzOzs59ck4iIvofFrmIFMDf3x+5ubkoLS1FWVkZ9u7di8rKSsydOxdWVlbQ0NAYcowbN25g7dq1SEpKQnJyMi5cuICoqCg8fPhQ6fEP1CT2cdnZ2ZgxY4a8Yawie2AQERHRs6Orqwve3t5444034OXlBQDQ09PDpEmToKamhm3btskX7jE0NERtba383Lq6OhgaGg7rOgKBAEeOHEFoaCg6OztHFatUKsWZM2fg5OSElpYWXLlyBR9//DGCgoJGNd7TWrZsGXR0dHptS0tLg6+vLwDA19cXqamp4xEaEdGEwCIXkQIIhUIEBwdj27ZtWL9+PV5++WXY29tDS0tryHNv376NXbt2Ye/evQgJCUFiYiLMzMxgYGCAjRs3IiYmZkzij46ORklJCXJychAfH4+SkpI+x73yyisoKipCUVERgoODlR4XERERTSwymQxbt26FpaUl3nnnHfn2hoYG+d9TUlLkj+O5u7sjKSkJHR0duHnzJsrLy+Ho6Djs65mYmMDNzQ2ffvrpiOPMz8+Hm5sbMjIykJ6ejrCwMGhpacHT0xP19fWjWkFbGSQSibzPmb6+PiQSyThHRESkuth4nugpdXd34/jx4/j000+hqamJ0NBQrFmzZsjz2tvbcfToUaSmpmLfvn347LPP+vTcCggIwJIlS7B582YYGRkp6y0M2CSWTVaJiIhoJK5cuYJTp05h/vz5sLW1BQB89NFHSExMRFFREQQCAUxMTORFKWtra/j4+MDKygpCoRDx8fHDWrH6cYGBgVi8eDFef/31Ya1+WFdXhwMHDuDHH39ETEwM7Ozsej2WKBAI8Mc//hHbtm3D5cuXVaonqkAgGPNHKImIJhLBEI0auXw10SC++uorhISE4Je//CX+8Ic/oKmpCWvWrME///nPAR9R7OnpQVJSEo4ePYpNmzYhICBg0McZy8vLYWxsjMmTJyvrbfRSXV2NZcuW4fr165g+fbp8e3Z2Nry9vWFkZIQXXngBR44cgbW19ZjERERKxW9Lqok5GNEIJCUl4fz584iLixuwCHT//n3ExMQgMzMTISEhcHNzG7SA9Z///Afz5s0b86JSdXU13NzccP36dQCAhYUFsrOzYWBggIaGBjg5OaG0tHRMYyIipWAOpgSqc1uCaAKqqKhAamqqfHq7sbExvLy8EB8f3+dYmUyGy5cvw8XFBcXFxcjKysLvf//7Ift1icXiMStwPdkk9nH29va4desWiouLsXv3bnh6eo5JTERERERD8fHxQXV1NQoKCvrs6+7uxokTJ+Di4gJjY2Pk5ubC3d19yBla8+fPV4lZU+7u7khISAAAJCQkwMPDY5wjIiJSXZzJRaRgDx48wOLFi5GSkgJ9fX0AQFlZGYKDgyEQCBAZGYm5c+eOc5R9dXV1wc3NDatWrerVQ2MgJiYmuHbtGmbNmjUG0RGREo3/NzjqD3MwohEqLCzE7t27kZGRATU1NchkMmRmZiIsLAwrV67Evn37oK2tPd5hDmrDhg3Izs7GnTt3oKenh9DQUHh6esLHxwc1NTWYM2cOkpOT+zSnJ6IJiTmYErDIRaQEX3zxBVJSUhAeHo7w8HAUFBQgPDwcTk5OKnFH8EkymQy+vr7Q0dFBbGxsv8fcvn0benp6EAgEyMvLw7p163Dr1i2VfD9ENCL8T6yamIMRjcL27dvh4OAAGxsb7N+/H9ra2ggPD4eZmdl4h0ZE9CTmYErAIheREkilUtjZ2aGtrQ0ffPABNm3aNOImqmPpm2++wSuvvIL58+fLp+5/9NFHqKmpAfAoYYyLi8OxY8cgFAoxZcoUxMTEYOnSpeMZNhEpBhMs1cQcjGgU7ty5A0tLS5ibm+Pw4cNYunQpb8gRkariLyclYJGLSEkKCwthbGzMx/mISNUxwVJNzMGIRunSpUtYvny5St9gJCICczClYJGLiIjo+cYESzUxByMiInq2MQdTAq6uSEREREREREREEx6LXERERERERERENOGxyEVERERERERERBMei1xERERERERERDThschF9ITS0lLY2trK/0yfPh2xsbG4e/cuXF1dIRaL4erqiqampvEOlYiIiIiIiIj+H4tcRE+wsLBAUVERioqKkJ+fj6lTp2Lt2rWIiIiAs7MzysvL4ezsjIiICKXGkZGRAQsLC4hEon6v1dHRgddffx0ikQiLFi1CdXW1UuMhIiIiel4NlZcREZFqYJGLaBBZWVl48cUXMWfOHKSlpcHX1xcA4Ovri9TUVKVdt6enB7/97W9x7tw5lJSUIDExESUlJb2OOXHiBLS1tVFRUYE9e/bgvffeU1o8RERERM+r4eRlRESkGljkIhpEUlISNmzYAACQSCQwMDAAAOjr60MikSjtunl5eRCJRDAzM4O6ujrWr1+PtLS0Xsc8XnRbt24dsrKyIJPJlBYTERER0fNoOHkZERGpBha5iAbQ2dmJM2fO4Fe/+lWffQKBAAKBQGnXrq+vh7Gxsfy1kZER6uvrBzxGKBRixowZaGxsVFpMRERERM+j4eRlRESkGljkIhrAuXPnYG9vDz09PQCAnp4eGhoaAAANDQ3Q1dUdz/CIiIiIiIiI6DEschENIDExUf6oIgC4u7sjISEBAJCQkAAPDw+lXdvQ0BC1tbXy13V1dTA0NBzwmO7ubjQ3N2PmzJlKi4mIiIjoeTScvIyIiFQDi1xE/Whra8PFixfh5eUl3xYYGIiLFy9CLBYjMzMTgYGBSru+g4MDysvLcfPmTXR2diIpKQnu7u69jnm86Pb3v/8dK1euVOojlERERETPo+HkZUREpBqE4x0AkSrS1NTs099q5syZyMrKGpPrC4VCxMXFYdWqVejp6YGfnx+sra0RHByMhQsXwt3dHVu3bsWmTZsgEomgo6ODpKSkMYmNiIiI6HkyUF5GRESqRzDEamxcqo2IiOjZximgqok5GBER0bONOZgS8HFFIiIiIiIiIiKa8FjkIiIiIiIiIiKiCY9FLiIiIiIiIiIimvBY5CIiIiIiIiIiogmPRS4iIiIiIiIiIprwWOQiIiIiIiIiIqIJj0UuIiIiIiIiIiKa8FjkIiIiIiIiIiKiCU84xH7BmERBRERERI9jDkZEREQ0QpzJRUREREREREREEx6LXERERERERERENOGxyEVERERERERERBMei1xERERERERERDThschFREREREREREQTHotcREREREREREQ04f0f9tZQTudQWmIAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 1440x720 with 2 Axes>"
       ]
@@ -455,14 +431,16 @@
    "source": [
     "def save_array_to_grid(window_bound: tuple, array: np.ndarray, outfilepath: str):\n",
     "    \"\"\"\n",
-    "    Saves a numpy array to geotiff and netcdf format\n",
+    "    Saves a numpy array to geotiff and netcdf format.\n",
+    "    Appends \".tif\" and \".nc\" file extension to the outfilepath\n",
+    "    for geotiff and netcdf outputs respectively.\n",
     "    \"\"\"\n",
     "\n",
     "    assert array.ndim == 4\n",
-    "    assert array.shape[3] == 1  # check that there is only one channel\n",
+    "    assert array.shape[1] == 1  # check that there is only one channel\n",
     "\n",
     "    transform = rasterio.transform.from_bounds(\n",
-    "        *window_bound, height=array.shape[1], width=array.shape[2]\n",
+    "        *window_bound, height=array.shape[2], width=array.shape[3]\n",
     "    )\n",
     "\n",
     "    # Save array as a GeoTiff first\n",
@@ -470,14 +448,14 @@
     "        f\"{outfilepath}.tif\",\n",
     "        mode=\"w\",\n",
     "        driver=\"GTiff\",\n",
-    "        height=array.shape[1],\n",
-    "        width=array.shape[2],\n",
+    "        height=array.shape[2],\n",
+    "        width=array.shape[3],\n",
     "        count=1,\n",
     "        crs=\"EPSG:3031\",\n",
     "        transform=transform,\n",
     "        dtype=array.dtype,\n",
     "    ) as new_geotiff:\n",
-    "        new_geotiff.write(array[0, :, :, 0], 1)\n",
+    "        new_geotiff.write(array[0, 0, :, :], 1)\n",
     "\n",
     "    # Convert deepbedmap3 and cubicbedmap2 from geotiff to netcdf format\n",
     "    xr.open_rasterio(f\"{outfilepath}.tif\").to_netcdf(f\"{outfilepath}.nc\")"
@@ -503,17 +481,17 @@
    "source": [
     "# Save Bicubic Resampled BEDMAP2 to GeoTiff and NetCDF format\n",
     "cubicbedmap2 = skimage.transform.rescale(\n",
-    "    image=X_tile[0].astype(np.int32),\n",
-    "    scale=4,\n",
-    "    order=3,\n",
+    "    image=X_tile[0, 0, :, :].astype(np.int32),\n",
+    "    scale=4,  # 4x upscaling\n",
+    "    order=3,  # cubic interpolation\n",
     "    mode=\"reflect\",\n",
     "    anti_aliasing=True,\n",
-    "    multichannel=True,\n",
+    "    multichannel=False,\n",
     "    preserve_range=True,\n",
     ")\n",
     "save_array_to_grid(\n",
     "    window_bound=window_bound,\n",
-    "    array=np.expand_dims(cubicbedmap2, axis=0),\n",
+    "    array=np.expand_dims(np.expand_dims(cubicbedmap2, axis=0), axis=0),\n",
     "    outfilepath=\"model/cubicbedmap\",\n",
     ")"
    ]
@@ -567,10 +545,10 @@
       "\n",
       "==> track_deepbedmap3.xyzi <==\n",
       "# x\ty\tz\n",
-      "-1593496.33\t-104797.8003\t-1074.669904\t-1258.33842113\n",
-      "-1593491.331\t-104797.7531\t-1074.68\t-1258.2212441\n",
-      "-1593486.331\t-104797.7058\t-1074.683558\t-1258.10331017\n",
-      "-1593481.331\t-104797.6599\t-1074.695031\t-1257.98472776\n",
+      "-1593496.33\t-104797.8003\t-1074.669904\t-1242.23582656\n",
+      "-1593491.331\t-104797.7531\t-1074.68\t-1242.21284675\n",
+      "-1593486.331\t-104797.7058\t-1074.683558\t-1242.19061904\n",
+      "-1593481.331\t-104797.6599\t-1074.695031\t-1242.16911184\n",
       "\n",
       "==> track_groundtruth.xyzi <==\n",
       "# x\ty\tz\n",
@@ -783,56 +761,56 @@
        "      <td>-1.582823e+06</td>\n",
        "      <td>-127943.948452</td>\n",
        "      <td>-1255.901352</td>\n",
-       "      <td>-1357.840108</td>\n",
-       "      <td>-101.938756</td>\n",
+       "      <td>-1382.773941</td>\n",
+       "      <td>-126.872590</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>4.306205e+03</td>\n",
        "      <td>29434.912966</td>\n",
        "      <td>73.216368</td>\n",
-       "      <td>78.215768</td>\n",
-       "      <td>41.523210</td>\n",
+       "      <td>85.661650</td>\n",
+       "      <td>51.655060</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>-1.593587e+06</td>\n",
        "      <td>-164048.233300</td>\n",
        "      <td>-1390.940804</td>\n",
-       "      <td>-1499.558819</td>\n",
-       "      <td>-240.194979</td>\n",
+       "      <td>-1530.420887</td>\n",
+       "      <td>-240.956202</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>-1.585696e+06</td>\n",
        "      <td>-160901.037700</td>\n",
        "      <td>-1327.500988</td>\n",
-       "      <td>-1432.212115</td>\n",
-       "      <td>-131.163062</td>\n",
+       "      <td>-1447.730568</td>\n",
+       "      <td>-166.523946</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>-1.582073e+06</td>\n",
        "      <td>-104396.422700</td>\n",
        "      <td>-1250.925200</td>\n",
-       "      <td>-1332.620263</td>\n",
-       "      <td>-110.196075</td>\n",
+       "      <td>-1386.413349</td>\n",
+       "      <td>-132.971022</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>-1.579456e+06</td>\n",
        "      <td>-101515.335350</td>\n",
        "      <td>-1195.214216</td>\n",
-       "      <td>-1303.871650</td>\n",
-       "      <td>-79.381847</td>\n",
+       "      <td>-1311.637268</td>\n",
+       "      <td>-93.415441</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td>-1.575591e+06</td>\n",
        "      <td>-98049.505510</td>\n",
        "      <td>-962.574500</td>\n",
-       "      <td>-1172.920767</td>\n",
-       "      <td>46.646893</td>\n",
+       "      <td>-1189.811013</td>\n",
+       "      <td>36.076236</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -841,13 +819,13 @@
       "text/plain": [
        "                  x              y             z  z_interpolated         error\n",
        "count  4.009500e+04   40095.000000  40095.000000    40095.000000  40095.000000\n",
-       "mean  -1.582823e+06 -127943.948452  -1255.901352    -1357.840108   -101.938756\n",
-       "std    4.306205e+03   29434.912966     73.216368       78.215768     41.523210\n",
-       "min   -1.593587e+06 -164048.233300  -1390.940804    -1499.558819   -240.194979\n",
-       "25%   -1.585696e+06 -160901.037700  -1327.500988    -1432.212115   -131.163062\n",
-       "50%   -1.582073e+06 -104396.422700  -1250.925200    -1332.620263   -110.196075\n",
-       "75%   -1.579456e+06 -101515.335350  -1195.214216    -1303.871650    -79.381847\n",
-       "max   -1.575591e+06  -98049.505510   -962.574500    -1172.920767     46.646893"
+       "mean  -1.582823e+06 -127943.948452  -1255.901352    -1382.773941   -126.872590\n",
+       "std    4.306205e+03   29434.912966     73.216368       85.661650     51.655060\n",
+       "min   -1.593587e+06 -164048.233300  -1390.940804    -1530.420887   -240.956202\n",
+       "25%   -1.585696e+06 -160901.037700  -1327.500988    -1447.730568   -166.523946\n",
+       "50%   -1.582073e+06 -104396.422700  -1250.925200    -1386.413349   -132.971022\n",
+       "75%   -1.579456e+06 -101515.335350  -1195.214216    -1311.637268    -93.415441\n",
+       "max   -1.575591e+06  -98049.505510   -962.574500    -1189.811013     36.076236"
       ]
      },
      "execution_count": 20,
@@ -993,12 +971,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Difference      : 47.51\n"
+      "Difference      : 74.43\n"
      ]
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 1152x648 with 1 Axes>"
       ]
@@ -1058,9 +1036,9 @@
      "output_type": "stream",
      "text": [
       "Groundtruth RMSE: 7.318264583382579\n",
-      "DeepBedMap3 RMSE: 110.0710862169407\n",
+      "DeepBedMap3 RMSE: 136.98478986659757\n",
       "CubicBedMap RMSE: 62.557033278894885\n",
-      "Difference      : 47.514052938045815\n"
+      "Difference      : 74.42775658770267\n"
      ]
     }
    ],
diff --git a/deepbedmap.py b/deepbedmap.py
index 9b3d1cb..e2f8c35 100644
--- a/deepbedmap.py
+++ b/deepbedmap.py
@@ -22,6 +22,7 @@
 # %%
 import math
 import os
+import typing
 
 os.environ["CUDA_VISIBLE_DEVICES"] = ""
 
@@ -36,7 +37,7 @@
 import skimage
 import xarray as xr
 
-import keras
+import chainer
 
 from features.environment import _load_ipynb_modules
 
@@ -44,7 +45,7 @@
 # ## Get bounding box of area we want to predict on
 
 # %%
-def get_image_and_bounds(filepath: str):
+def get_image_and_bounds(filepath: str) -> (np.ndarray, rasterio.coords.BoundingBox):
     """
     Retrieve raster image in numpy array format and
     geographic bounds as (xmin, ymin, xmax, ymax)
@@ -53,8 +54,9 @@ def get_image_and_bounds(filepath: str):
         groundtruth = data.z.to_masked_array()
         groundtruth = np.flipud(groundtruth)  # flip on y-axis...
         groundtruth = np.expand_dims(
-            np.expand_dims(groundtruth, axis=-1), axis=0
+            np.expand_dims(groundtruth, axis=0), axis=0
         )  # add extra dimensions (batch and channel)
+        assert groundtruth.shape[0:2] == (1, 1)  # check that shape is like (1, 1, h, w)
 
         xmin, xmax = float(data.x.min()), float(data.x.max())
         ymin, ymax = float(data.y.min()), float(data.y.max())
@@ -69,7 +71,6 @@ def get_image_and_bounds(filepath: str):
 test_file = "2007tx"  # "istarxx"
 test_filepath = f"highres/{test_file}"
 groundtruth, window_bound = get_image_and_bounds(filepath=f"{test_filepath}.nc")
-print(window_bound)
 
 # %% [markdown]
 # ## Get neural network input datasets for our area of interest
@@ -77,7 +78,7 @@ def get_image_and_bounds(filepath: str):
 # %%
 def get_deepbedmap_model_inputs(
     window_bound: rasterio.coords.BoundingBox, padding=1000
-):
+) -> typing.Dict[str, np.ndarray]:
     """
     Outputs one large tile for each of
     BEDMAP2, REMA and MEASURES Ice Flow Velocity
@@ -104,7 +105,11 @@ def get_deepbedmap_model_inputs(
         padding=padding,
     )
 
-    return X_tile, W1_tile, W2_tile
+    return (
+        np.rollaxis(X_tile, axis=3, start=1),
+        np.rollaxis(W1_tile, axis=3, start=1),
+        np.rollaxis(W2_tile, axis=3, start=1),
+    )
 
 
 # %%
@@ -116,10 +121,10 @@ def plot_3d_view(
     cm_norm: matplotlib.colors.Normalize = None,
     title: str = None,
 ):
-    # Get x, y, z data
+    # Get x, y, z data, assuming image in NCHW format
     image = img[0, :, :, :]
-    xx, yy = np.mgrid[0 : image.shape[0], 0 : image.shape[1]]
-    zz = image[:, :, 0]
+    xx, yy = np.mgrid[0 : image.shape[1], 0 : image.shape[2]]
+    zz = image[0, :, :]
 
     # Make the 3D plot
     ax.view_init(elev=elev, azim=azim)
@@ -142,11 +147,11 @@ def plot_3d_view(
 
 # %%
 fig, axarr = plt.subplots(nrows=1, ncols=3, squeeze=False, figsize=(16, 12))
-axarr[0, 0].imshow(X_tile[0, :, :, 0], cmap="BrBG")
+axarr[0, 0].imshow(X_tile[0, 0, :, :], cmap="BrBG")
 axarr[0, 0].set_title("BEDMAP2\n(1000m resolution)")
-axarr[0, 1].imshow(W1_tile[0, :, :, 0], cmap="BrBG")
+axarr[0, 1].imshow(W1_tile[0, 0, :, :], cmap="BrBG")
 axarr[0, 1].set_title("Reference Elevation Model of Antarctica\n(100m resolution)")
-axarr[0, 2].imshow(W2_tile[0, :, :, 0], cmap="BrBG")
+axarr[0, 2].imshow(W2_tile[0, 0, :, :], cmap="BrBG")
 axarr[0, 2].set_title("MEaSUREs Ice Velocity\n(450m, resampled to 500m)")
 plt.show()
 
@@ -183,29 +188,23 @@ def plot_3d_view(
 # That way we can predict directly on an arbitrarily sized window.
 
 # %%
-def load_trained_model(model_inputs: tuple):
+def load_trained_model(
+    filepath: str = "model/weights/srgan_generator_model_weights.npz"
+):
     """
-    Creates a custom DeepBedMap neural network model
-    according to the shapes of the raster image inputs.
-
-    Also loads trained parameter weights into the model.
+    Builds the Generator component of the DeepBedMap neural network.
+    Also loads trained parameter weights into the model from a .npz file.
     """
     srgan_train = _load_ipynb_modules("srgan_train.ipynb")
 
-    X_tile, W1_tile, W2_tile = model_inputs
-
-    network = srgan_train.generator_network(
-        input1_shape=X_tile.shape[1:],
-        input2_shape=W1_tile.shape[1:],
-        input3_shape=W2_tile.shape[1:],
-    )
-
-    model = keras.models.Model(
-        inputs=network.inputs, outputs=network.outputs, name="generator_model"
+    model = srgan_train.GeneratorModel(
+        inblock_class=srgan_train.DeepbedmapInputBlock,
+        resblock_class=srgan_train.ResidualBlock,
+        num_residual_blocks=16,
     )
 
     # Load trained neural network weights into model
-    model.load_weights(filepath="model/weights/srgan_generator_model_weights.hdf5")
+    chainer.serializers.load_npz(file=filepath, obj=model)
 
     return model
 
@@ -214,8 +213,8 @@ def load_trained_model(model_inputs: tuple):
 # ## Make prediction
 
 # %%
-model = load_trained_model(model_inputs=(X_tile, W1_tile, W2_tile))
-Y_hat = model.predict(x=[X_tile, W1_tile, W2_tile], verbose=1)
+model = load_trained_model()
+Y_hat = model.forward(inputs={"x": X_tile, "w1": W1_tile, "w2": W2_tile}).array
 Y_hat.shape
 
 # %% [markdown]
@@ -223,11 +222,11 @@ def load_trained_model(model_inputs: tuple):
 
 # %%
 fig, axarr = plt.subplots(nrows=1, ncols=3, squeeze=False, figsize=(16, 12))
-axarr[0, 0].imshow(X_tile[0, :, :, 0], cmap="BrBG")
+axarr[0, 0].imshow(X_tile[0, 0, :, :], cmap="BrBG")
 axarr[0, 0].set_title("BEDMAP2")
-axarr[0, 1].imshow(Y_hat[0, :, :, 0], cmap="BrBG")
+axarr[0, 1].imshow(Y_hat[0, 0, :, :], cmap="BrBG")
 axarr[0, 1].set_title("Super Resolution Generative Adversarial Network prediction")
-axarr[0, 2].imshow(groundtruth[0, :, :, 0], cmap="BrBG")
+axarr[0, 2].imshow(groundtruth[0, 0, :, :], cmap="BrBG")
 axarr[0, 2].set_title("Groundtruth grids")
 plt.show()
 
@@ -262,14 +261,16 @@ def load_trained_model(model_inputs: tuple):
 # %%
 def save_array_to_grid(window_bound: tuple, array: np.ndarray, outfilepath: str):
     """
-    Saves a numpy array to geotiff and netcdf format
+    Saves a numpy array to geotiff and netcdf format.
+    Appends ".tif" and ".nc" file extension to the outfilepath
+    for geotiff and netcdf outputs respectively.
     """
 
     assert array.ndim == 4
-    assert array.shape[3] == 1  # check that there is only one channel
+    assert array.shape[1] == 1  # check that there is only one channel
 
     transform = rasterio.transform.from_bounds(
-        *window_bound, height=array.shape[1], width=array.shape[2]
+        *window_bound, height=array.shape[2], width=array.shape[3]
     )
 
     # Save array as a GeoTiff first
@@ -277,14 +278,14 @@ def save_array_to_grid(window_bound: tuple, array: np.ndarray, outfilepath: str)
         f"{outfilepath}.tif",
         mode="w",
         driver="GTiff",
-        height=array.shape[1],
-        width=array.shape[2],
+        height=array.shape[2],
+        width=array.shape[3],
         count=1,
         crs="EPSG:3031",
         transform=transform,
         dtype=array.dtype,
     ) as new_geotiff:
-        new_geotiff.write(array[0, :, :, 0], 1)
+        new_geotiff.write(array[0, 0, :, :], 1)
 
     # Convert deepbedmap3 and cubicbedmap2 from geotiff to netcdf format
     xr.open_rasterio(f"{outfilepath}.tif").to_netcdf(f"{outfilepath}.nc")
@@ -299,17 +300,17 @@ def save_array_to_grid(window_bound: tuple, array: np.ndarray, outfilepath: str)
 # %%
 # Save Bicubic Resampled BEDMAP2 to GeoTiff and NetCDF format
 cubicbedmap2 = skimage.transform.rescale(
-    image=X_tile[0].astype(np.int32),
-    scale=4,
-    order=3,
+    image=X_tile[0, 0, :, :].astype(np.int32),
+    scale=4,  # 4x upscaling
+    order=3,  # cubic interpolation
     mode="reflect",
     anti_aliasing=True,
-    multichannel=True,
+    multichannel=False,
     preserve_range=True,
 )
 save_array_to_grid(
     window_bound=window_bound,
-    array=np.expand_dims(cubicbedmap2, axis=0),
+    array=np.expand_dims(np.expand_dims(cubicbedmap2, axis=0), axis=0),
     outfilepath="model/cubicbedmap",
 )
 
diff --git a/environment.yml b/environment.yml
index edfe329..fb029ac 100644
--- a/environment.yml
+++ b/environment.yml
@@ -4,9 +4,9 @@ channels:
   - conda-forge/label/dev
   - nodefaults
 dependencies:
-  - defaults::cudnn=7.1.2[md5=4a402b88bb77e6ab2dcf3bfe6522f9cf]
-  - hcc::cuda_driver=390.46[md5=8fb0b6c39a9bf6128b1191db53ed903e]
-  - defaults::cudatoolkit=9.0[md5=5d0febed868b80a18e74077d5d0f17bc]
+  - defaults::cudnn=7.2.1[md5=6a84069dcf4aca8ba9493d3cb320090e]
+  - hcc::cuda_driver=410.73[md5=941787b750b372f4a240287634589d24]
+  - defaults::cudatoolkit=9.2[md5=f81c96e01ccb9028800101b35e71b844]
   - gmt=6.0.0a17[md5=bea1e9a2cc29280f8ba173123f115496]
   - pip=18.1[md5=d68c7e5109ba0bf4b1cfe60f0f47870a]
   - conda-forge::python=3.6.6[md5=fe9f54422cdaf8779147b6a02cab2dd1]
diff --git a/features/environment.py b/features/environment.py
index 828dbcd..acf7b58 100644
--- a/features/environment.py
+++ b/features/environment.py
@@ -32,10 +32,15 @@ def _load_ipynb_modules(ipynb_path: str):
     source, meta = pyexporter.from_notebook_node(nb=nb)
     assert isinstance(source, str)
 
-    # parse the .py string to pick out only 'import' and 'def function's
+    # parse the .py string to pick out only 'class', 'import' and 'def function's
     parsed_code = ast.parse(source=source)
     for node in parsed_code.body[:]:
-        if node.__class__ not in [ast.FunctionDef, ast.Import, ast.ImportFrom]:
+        if node.__class__ not in [
+            ast.ClassDef,
+            ast.FunctionDef,
+            ast.Import,
+            ast.ImportFrom,
+        ]:
             parsed_code.body.remove(node)
     assert len(parsed_code.body) > 0
 
@@ -108,7 +113,7 @@ def _download_deepbedmap_model_weights_from_comet():
 
     # Download the neural network weight file (hdf5 format) to the right place!
     r = requests.get(url=asset_url, headers=authHeader)
-    open(file="model/weights/srgan_generator_model_weights.hdf5", mode="wb").write(
+    open(file="model/weights/srgan_generator_model_weights.npz", mode="wb").write(
         r.content
     )
 
diff --git a/features/steps/test_deepbedmap.py b/features/steps/test_deepbedmap.py
index 8a38aa1..1cbf123 100644
--- a/features/steps/test_deepbedmap.py
+++ b/features/steps/test_deepbedmap.py
@@ -29,22 +29,20 @@ def get_model_input_raster_images(context):
 
 @when("pass those images into our trained neural network model")
 def predict_using_trained_neural_network(context):
-    model = context.deepbedmap.load_trained_model(
-        model_inputs=(context.X_tile, context.W1_tile, context.W2_tile)
-    )
-    context.Y_hat = model.predict(
-        x=[context.X_tile, context.W1_tile, context.W2_tile], verbose=0
-    )
+    model = context.deepbedmap.load_trained_model()
+    context.Y_hat = model.forward(
+        inputs={"x": context.X_tile, "w1": context.W1_tile, "w2": context.W2_tile}
+    ).array
 
 
 @then("a four times upsampled super resolution bed elevation map is returned")
 def step_impl(context):
-    # Ensure input (X_tile) and output (Y_hat) shape is like (1, height, width, 1)
+    # Ensure input (X_tile) and output (Y_hat) shape is like (1, 1, height, width)
     assert context.X_tile.ndim == 4
     assert context.Y_hat.ndim == 4
 
     # Check that High Resolution output shape (DeepBedMap) divided by
     # Low Resolution input shape (BEDMAP2) minus 2 pixel (1km) padding
     # is exactly equal to 4
-    assert context.Y_hat.shape[1] / (context.X_tile.shape[1] - 2) == 4.0
     assert context.Y_hat.shape[2] / (context.X_tile.shape[2] - 2) == 4.0
+    assert context.Y_hat.shape[3] / (context.X_tile.shape[3] - 2) == 4.0
diff --git a/model/README.md b/model/README.md
index 312d0b2..2905b78 100644
--- a/model/README.md
+++ b/model/README.md
@@ -8,6 +8,6 @@ This folder contains the files which are directly related to the training of the
   - \*_data.npy  (\*hidden in git, preprocessed raster tiles from data_prep.ipynb)
 
 - weights/
-  - [srgan_generator_model_architecture.json](weights/srgan_generator_model_architecture.json)  (Keras model architecture of Generator Network in JSON)
-  - srgan_generator_model_weights.hdf5  (\*hidden in git but available at https://www.comet.ml/weiji14/deepbedmap under experiment assets, trained neural network weights)
-  - srgan_generator_model.hdf5  (\*hidden in git, contains both neural network model architecture and weights)
+  - [srgan_generator_model_architecture.onnx.txt](weights/srgan_generator_model_architecture.onnx.txt)  (Chainer model architecture of Generator Network in ONNX text format)
+  - srgan_generator_model_architecture.onnx  (\*hidden in git, Chainer model architecture of Generator Network in ONNX binary format)
+  - srgan_generator_model_weights.npz  (\*hidden in git but available at https://www.comet.ml/weiji14/deepbedmap under experiment assets, trained neural network weights)
diff --git a/model/weights/srgan_generator_model_architecture.json b/model/weights/srgan_generator_model_architecture.json
deleted file mode 100644
index 187a3ee..0000000
--- a/model/weights/srgan_generator_model_architecture.json
+++ /dev/null
@@ -1,3056 +0,0 @@
-{
-  "class_name": "Model",
-  "config": {
-    "name": "generator_model",
-    "layers": [
-      {
-        "name": "input_1",
-        "class_name": "InputLayer",
-        "config": {
-          "batch_input_shape": [
-            null,
-            10,
-            10,
-            1
-          ],
-          "dtype": "float32",
-          "sparse": false,
-          "name": "input_1"
-        },
-        "inbound_nodes": []
-      },
-      {
-        "name": "input_2",
-        "class_name": "InputLayer",
-        "config": {
-          "batch_input_shape": [
-            null,
-            100,
-            100,
-            1
-          ],
-          "dtype": "float32",
-          "sparse": false,
-          "name": "input_2"
-        },
-        "inbound_nodes": []
-      },
-      {
-        "name": "input_3",
-        "class_name": "InputLayer",
-        "config": {
-          "batch_input_shape": [
-            null,
-            20,
-            20,
-            1
-          ],
-          "dtype": "float32",
-          "sparse": false,
-          "name": "input_3"
-        },
-        "inbound_nodes": []
-      },
-      {
-        "name": "conv2d_1",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_1",
-          "trainable": true,
-          "filters": 32,
-          "kernel_size": [
-            3,
-            3
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "valid",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "input_1",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_2",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_2",
-          "trainable": true,
-          "filters": 32,
-          "kernel_size": [
-            30,
-            30
-          ],
-          "strides": [
-            10,
-            10
-          ],
-          "padding": "valid",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "input_2",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_3",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_3",
-          "trainable": true,
-          "filters": 32,
-          "kernel_size": [
-            6,
-            6
-          ],
-          "strides": [
-            2,
-            2
-          ],
-          "padding": "valid",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "input_3",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "concatenate_1",
-        "class_name": "Concatenate",
-        "config": {
-          "name": "concatenate_1",
-          "trainable": true,
-          "axis": -1
-        },
-        "inbound_nodes": [
-          [
-            [
-              "conv2d_1",
-              0,
-              0,
-              {}
-            ],
-            [
-              "conv2d_2",
-              0,
-              0,
-              {}
-            ],
-            [
-              "conv2d_3",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_4",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_4",
-          "trainable": true,
-          "filters": 64,
-          "kernel_size": [
-            3,
-            3
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "same",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "concatenate_1",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "leaky_re_lu_1",
-        "class_name": "LeakyReLU",
-        "config": {
-          "name": "leaky_re_lu_1",
-          "trainable": true,
-          "alpha": 0.20000000298023224
-        },
-        "inbound_nodes": [
-          [
-            [
-              "conv2d_4",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_5",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_5",
-          "trainable": true,
-          "filters": 64,
-          "kernel_size": [
-            3,
-            3
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "same",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "leaky_re_lu_1",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "leaky_re_lu_2",
-        "class_name": "LeakyReLU",
-        "config": {
-          "name": "leaky_re_lu_2",
-          "trainable": true,
-          "alpha": 0.20000000298023224
-        },
-        "inbound_nodes": [
-          [
-            [
-              "conv2d_5",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_6",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_6",
-          "trainable": true,
-          "filters": 64,
-          "kernel_size": [
-            3,
-            3
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "same",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "leaky_re_lu_2",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "add_1",
-        "class_name": "Add",
-        "config": {
-          "name": "add_1",
-          "trainable": true
-        },
-        "inbound_nodes": [
-          [
-            [
-              "conv2d_6",
-              0,
-              0,
-              {}
-            ],
-            [
-              "leaky_re_lu_1",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_7",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_7",
-          "trainable": true,
-          "filters": 64,
-          "kernel_size": [
-            3,
-            3
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "same",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "add_1",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "leaky_re_lu_3",
-        "class_name": "LeakyReLU",
-        "config": {
-          "name": "leaky_re_lu_3",
-          "trainable": true,
-          "alpha": 0.20000000298023224
-        },
-        "inbound_nodes": [
-          [
-            [
-              "conv2d_7",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_8",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_8",
-          "trainable": true,
-          "filters": 64,
-          "kernel_size": [
-            3,
-            3
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "same",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "leaky_re_lu_3",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "add_2",
-        "class_name": "Add",
-        "config": {
-          "name": "add_2",
-          "trainable": true
-        },
-        "inbound_nodes": [
-          [
-            [
-              "conv2d_8",
-              0,
-              0,
-              {}
-            ],
-            [
-              "add_1",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_9",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_9",
-          "trainable": true,
-          "filters": 64,
-          "kernel_size": [
-            3,
-            3
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "same",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "add_2",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "leaky_re_lu_4",
-        "class_name": "LeakyReLU",
-        "config": {
-          "name": "leaky_re_lu_4",
-          "trainable": true,
-          "alpha": 0.20000000298023224
-        },
-        "inbound_nodes": [
-          [
-            [
-              "conv2d_9",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_10",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_10",
-          "trainable": true,
-          "filters": 64,
-          "kernel_size": [
-            3,
-            3
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "same",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "leaky_re_lu_4",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "add_3",
-        "class_name": "Add",
-        "config": {
-          "name": "add_3",
-          "trainable": true
-        },
-        "inbound_nodes": [
-          [
-            [
-              "conv2d_10",
-              0,
-              0,
-              {}
-            ],
-            [
-              "add_2",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_11",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_11",
-          "trainable": true,
-          "filters": 64,
-          "kernel_size": [
-            3,
-            3
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "same",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "add_3",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "leaky_re_lu_5",
-        "class_name": "LeakyReLU",
-        "config": {
-          "name": "leaky_re_lu_5",
-          "trainable": true,
-          "alpha": 0.20000000298023224
-        },
-        "inbound_nodes": [
-          [
-            [
-              "conv2d_11",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_12",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_12",
-          "trainable": true,
-          "filters": 64,
-          "kernel_size": [
-            3,
-            3
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "same",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "leaky_re_lu_5",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "add_4",
-        "class_name": "Add",
-        "config": {
-          "name": "add_4",
-          "trainable": true
-        },
-        "inbound_nodes": [
-          [
-            [
-              "conv2d_12",
-              0,
-              0,
-              {}
-            ],
-            [
-              "add_3",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_13",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_13",
-          "trainable": true,
-          "filters": 64,
-          "kernel_size": [
-            3,
-            3
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "same",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "add_4",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "leaky_re_lu_6",
-        "class_name": "LeakyReLU",
-        "config": {
-          "name": "leaky_re_lu_6",
-          "trainable": true,
-          "alpha": 0.20000000298023224
-        },
-        "inbound_nodes": [
-          [
-            [
-              "conv2d_13",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_14",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_14",
-          "trainable": true,
-          "filters": 64,
-          "kernel_size": [
-            3,
-            3
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "same",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "leaky_re_lu_6",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "add_5",
-        "class_name": "Add",
-        "config": {
-          "name": "add_5",
-          "trainable": true
-        },
-        "inbound_nodes": [
-          [
-            [
-              "conv2d_14",
-              0,
-              0,
-              {}
-            ],
-            [
-              "add_4",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_15",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_15",
-          "trainable": true,
-          "filters": 64,
-          "kernel_size": [
-            3,
-            3
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "same",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "add_5",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "leaky_re_lu_7",
-        "class_name": "LeakyReLU",
-        "config": {
-          "name": "leaky_re_lu_7",
-          "trainable": true,
-          "alpha": 0.20000000298023224
-        },
-        "inbound_nodes": [
-          [
-            [
-              "conv2d_15",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_16",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_16",
-          "trainable": true,
-          "filters": 64,
-          "kernel_size": [
-            3,
-            3
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "same",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "leaky_re_lu_7",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "add_6",
-        "class_name": "Add",
-        "config": {
-          "name": "add_6",
-          "trainable": true
-        },
-        "inbound_nodes": [
-          [
-            [
-              "conv2d_16",
-              0,
-              0,
-              {}
-            ],
-            [
-              "add_5",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_17",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_17",
-          "trainable": true,
-          "filters": 64,
-          "kernel_size": [
-            3,
-            3
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "same",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "add_6",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "leaky_re_lu_8",
-        "class_name": "LeakyReLU",
-        "config": {
-          "name": "leaky_re_lu_8",
-          "trainable": true,
-          "alpha": 0.20000000298023224
-        },
-        "inbound_nodes": [
-          [
-            [
-              "conv2d_17",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_18",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_18",
-          "trainable": true,
-          "filters": 64,
-          "kernel_size": [
-            3,
-            3
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "same",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "leaky_re_lu_8",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "add_7",
-        "class_name": "Add",
-        "config": {
-          "name": "add_7",
-          "trainable": true
-        },
-        "inbound_nodes": [
-          [
-            [
-              "conv2d_18",
-              0,
-              0,
-              {}
-            ],
-            [
-              "add_6",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_19",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_19",
-          "trainable": true,
-          "filters": 64,
-          "kernel_size": [
-            3,
-            3
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "same",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "add_7",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "leaky_re_lu_9",
-        "class_name": "LeakyReLU",
-        "config": {
-          "name": "leaky_re_lu_9",
-          "trainable": true,
-          "alpha": 0.20000000298023224
-        },
-        "inbound_nodes": [
-          [
-            [
-              "conv2d_19",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_20",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_20",
-          "trainable": true,
-          "filters": 64,
-          "kernel_size": [
-            3,
-            3
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "same",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "leaky_re_lu_9",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "add_8",
-        "class_name": "Add",
-        "config": {
-          "name": "add_8",
-          "trainable": true
-        },
-        "inbound_nodes": [
-          [
-            [
-              "conv2d_20",
-              0,
-              0,
-              {}
-            ],
-            [
-              "add_7",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_21",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_21",
-          "trainable": true,
-          "filters": 64,
-          "kernel_size": [
-            3,
-            3
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "same",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "add_8",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "leaky_re_lu_10",
-        "class_name": "LeakyReLU",
-        "config": {
-          "name": "leaky_re_lu_10",
-          "trainable": true,
-          "alpha": 0.20000000298023224
-        },
-        "inbound_nodes": [
-          [
-            [
-              "conv2d_21",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_22",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_22",
-          "trainable": true,
-          "filters": 64,
-          "kernel_size": [
-            3,
-            3
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "same",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "leaky_re_lu_10",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "add_9",
-        "class_name": "Add",
-        "config": {
-          "name": "add_9",
-          "trainable": true
-        },
-        "inbound_nodes": [
-          [
-            [
-              "conv2d_22",
-              0,
-              0,
-              {}
-            ],
-            [
-              "add_8",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_23",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_23",
-          "trainable": true,
-          "filters": 64,
-          "kernel_size": [
-            3,
-            3
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "same",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "add_9",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "leaky_re_lu_11",
-        "class_name": "LeakyReLU",
-        "config": {
-          "name": "leaky_re_lu_11",
-          "trainable": true,
-          "alpha": 0.20000000298023224
-        },
-        "inbound_nodes": [
-          [
-            [
-              "conv2d_23",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_24",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_24",
-          "trainable": true,
-          "filters": 64,
-          "kernel_size": [
-            3,
-            3
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "same",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "leaky_re_lu_11",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "add_10",
-        "class_name": "Add",
-        "config": {
-          "name": "add_10",
-          "trainable": true
-        },
-        "inbound_nodes": [
-          [
-            [
-              "conv2d_24",
-              0,
-              0,
-              {}
-            ],
-            [
-              "add_9",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_25",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_25",
-          "trainable": true,
-          "filters": 64,
-          "kernel_size": [
-            3,
-            3
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "same",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "add_10",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "leaky_re_lu_12",
-        "class_name": "LeakyReLU",
-        "config": {
-          "name": "leaky_re_lu_12",
-          "trainable": true,
-          "alpha": 0.20000000298023224
-        },
-        "inbound_nodes": [
-          [
-            [
-              "conv2d_25",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_26",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_26",
-          "trainable": true,
-          "filters": 64,
-          "kernel_size": [
-            3,
-            3
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "same",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "leaky_re_lu_12",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "add_11",
-        "class_name": "Add",
-        "config": {
-          "name": "add_11",
-          "trainable": true
-        },
-        "inbound_nodes": [
-          [
-            [
-              "conv2d_26",
-              0,
-              0,
-              {}
-            ],
-            [
-              "add_10",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_27",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_27",
-          "trainable": true,
-          "filters": 64,
-          "kernel_size": [
-            3,
-            3
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "same",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "add_11",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "leaky_re_lu_13",
-        "class_name": "LeakyReLU",
-        "config": {
-          "name": "leaky_re_lu_13",
-          "trainable": true,
-          "alpha": 0.20000000298023224
-        },
-        "inbound_nodes": [
-          [
-            [
-              "conv2d_27",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_28",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_28",
-          "trainable": true,
-          "filters": 64,
-          "kernel_size": [
-            3,
-            3
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "same",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "leaky_re_lu_13",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "add_12",
-        "class_name": "Add",
-        "config": {
-          "name": "add_12",
-          "trainable": true
-        },
-        "inbound_nodes": [
-          [
-            [
-              "conv2d_28",
-              0,
-              0,
-              {}
-            ],
-            [
-              "add_11",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_29",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_29",
-          "trainable": true,
-          "filters": 64,
-          "kernel_size": [
-            3,
-            3
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "same",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "add_12",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "leaky_re_lu_14",
-        "class_name": "LeakyReLU",
-        "config": {
-          "name": "leaky_re_lu_14",
-          "trainable": true,
-          "alpha": 0.20000000298023224
-        },
-        "inbound_nodes": [
-          [
-            [
-              "conv2d_29",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_30",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_30",
-          "trainable": true,
-          "filters": 64,
-          "kernel_size": [
-            3,
-            3
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "same",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "leaky_re_lu_14",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "add_13",
-        "class_name": "Add",
-        "config": {
-          "name": "add_13",
-          "trainable": true
-        },
-        "inbound_nodes": [
-          [
-            [
-              "conv2d_30",
-              0,
-              0,
-              {}
-            ],
-            [
-              "add_12",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_31",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_31",
-          "trainable": true,
-          "filters": 64,
-          "kernel_size": [
-            3,
-            3
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "same",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "add_13",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "leaky_re_lu_15",
-        "class_name": "LeakyReLU",
-        "config": {
-          "name": "leaky_re_lu_15",
-          "trainable": true,
-          "alpha": 0.20000000298023224
-        },
-        "inbound_nodes": [
-          [
-            [
-              "conv2d_31",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_32",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_32",
-          "trainable": true,
-          "filters": 64,
-          "kernel_size": [
-            3,
-            3
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "same",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "leaky_re_lu_15",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "add_14",
-        "class_name": "Add",
-        "config": {
-          "name": "add_14",
-          "trainable": true
-        },
-        "inbound_nodes": [
-          [
-            [
-              "conv2d_32",
-              0,
-              0,
-              {}
-            ],
-            [
-              "add_13",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_33",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_33",
-          "trainable": true,
-          "filters": 64,
-          "kernel_size": [
-            3,
-            3
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "same",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "add_14",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "leaky_re_lu_16",
-        "class_name": "LeakyReLU",
-        "config": {
-          "name": "leaky_re_lu_16",
-          "trainable": true,
-          "alpha": 0.20000000298023224
-        },
-        "inbound_nodes": [
-          [
-            [
-              "conv2d_33",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_34",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_34",
-          "trainable": true,
-          "filters": 64,
-          "kernel_size": [
-            3,
-            3
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "same",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "leaky_re_lu_16",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "add_15",
-        "class_name": "Add",
-        "config": {
-          "name": "add_15",
-          "trainable": true
-        },
-        "inbound_nodes": [
-          [
-            [
-              "conv2d_34",
-              0,
-              0,
-              {}
-            ],
-            [
-              "add_14",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_35",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_35",
-          "trainable": true,
-          "filters": 64,
-          "kernel_size": [
-            3,
-            3
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "same",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "add_15",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "leaky_re_lu_17",
-        "class_name": "LeakyReLU",
-        "config": {
-          "name": "leaky_re_lu_17",
-          "trainable": true,
-          "alpha": 0.20000000298023224
-        },
-        "inbound_nodes": [
-          [
-            [
-              "conv2d_35",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_36",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_36",
-          "trainable": true,
-          "filters": 64,
-          "kernel_size": [
-            3,
-            3
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "same",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "leaky_re_lu_17",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "add_16",
-        "class_name": "Add",
-        "config": {
-          "name": "add_16",
-          "trainable": true
-        },
-        "inbound_nodes": [
-          [
-            [
-              "conv2d_36",
-              0,
-              0,
-              {}
-            ],
-            [
-              "add_15",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_37",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_37",
-          "trainable": true,
-          "filters": 64,
-          "kernel_size": [
-            3,
-            3
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "same",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "add_16",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "add_17",
-        "class_name": "Add",
-        "config": {
-          "name": "add_17",
-          "trainable": true
-        },
-        "inbound_nodes": [
-          [
-            [
-              "conv2d_37",
-              0,
-              0,
-              {}
-            ],
-            [
-              "leaky_re_lu_1",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_38",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_38",
-          "trainable": true,
-          "filters": 256,
-          "kernel_size": [
-            3,
-            3
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "same",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "add_17",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "pixelshuffleup_1",
-        "class_name": "Lambda",
-        "config": {
-          "name": "pixelshuffleup_1",
-          "trainable": true,
-          "function": [
-            "4wEAAAAAAAAAAQAAAAQAAABTAAAAcxAAAAB0AGoBagJ8AGQBZAKNAlMAKQNO6QIAAAApAtoFaW5w\ndXTaCmJsb2NrX3NpemUpA9oBS9oCdGbaDmRlcHRoX3RvX3NwYWNlKQHaBmltYWdlc6kAcggAAAD6\nHjxpcHl0aG9uLWlucHV0LTctNDQ1YWQyOGUzZWUyPtoIPGxhbWJkYT5YAAAAcwAAAAA=\n",
-            null,
-            null
-          ],
-          "function_type": "lambda",
-          "output_shape": null,
-          "output_shape_type": "raw",
-          "arguments": {}
-        },
-        "inbound_nodes": [
-          [
-            [
-              "conv2d_38",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "leaky_re_lu_18",
-        "class_name": "LeakyReLU",
-        "config": {
-          "name": "leaky_re_lu_18",
-          "trainable": true,
-          "alpha": 0.20000000298023224
-        },
-        "inbound_nodes": [
-          [
-            [
-              "pixelshuffleup_1",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_39",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_39",
-          "trainable": true,
-          "filters": 256,
-          "kernel_size": [
-            3,
-            3
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "same",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "leaky_re_lu_18",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "pixelshuffleup_2",
-        "class_name": "Lambda",
-        "config": {
-          "name": "pixelshuffleup_2",
-          "trainable": true,
-          "function": [
-            "4wEAAAAAAAAAAQAAAAQAAABTAAAAcxAAAAB0AGoBagJ8AGQBZAKNAlMAKQNO6QIAAAApAtoFaW5w\ndXTaCmJsb2NrX3NpemUpA9oBS9oCdGbaDmRlcHRoX3RvX3NwYWNlKQHaBmltYWdlc6kAcggAAAD6\nHjxpcHl0aG9uLWlucHV0LTctNDQ1YWQyOGUzZWUyPtoIPGxhbWJkYT5YAAAAcwAAAAA=\n",
-            null,
-            null
-          ],
-          "function_type": "lambda",
-          "output_shape": null,
-          "output_shape_type": "raw",
-          "arguments": {}
-        },
-        "inbound_nodes": [
-          [
-            [
-              "conv2d_39",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "leaky_re_lu_19",
-        "class_name": "LeakyReLU",
-        "config": {
-          "name": "leaky_re_lu_19",
-          "trainable": true,
-          "alpha": 0.20000000298023224
-        },
-        "inbound_nodes": [
-          [
-            [
-              "pixelshuffleup_2",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "generator_output",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "generator_output",
-          "trainable": true,
-          "filters": 1,
-          "kernel_size": [
-            9,
-            9
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "same",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "leaky_re_lu_19",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      }
-    ],
-    "input_layers": [
-      [
-        "input_1",
-        0,
-        0
-      ],
-      [
-        "input_2",
-        0,
-        0
-      ],
-      [
-        "input_3",
-        0,
-        0
-      ]
-    ],
-    "output_layers": [
-      [
-        "generator_output",
-        0,
-        0
-      ]
-    ]
-  },
-  "keras_version": "2.2.4",
-  "backend": "tensorflow"
-}
\ No newline at end of file
diff --git a/model/weights/srgan_generator_model_architecture.onnx.txt b/model/weights/srgan_generator_model_architecture.onnx.txt
new file mode 100644
index 0000000..473e7ef
--- /dev/null
+++ b/model/weights/srgan_generator_model_architecture.onnx.txt
@@ -0,0 +1,3343 @@
+ir_version: 3
+producer_name: "Chainer"
+producer_version: "6.0.0b1"
+graph {
+  node {
+    input: "Input_0"
+    input: "Input_1"
+    input: "Input_2"
+    output: "Conv_0"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 6
+      ints: 6
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 0
+      ints: 0
+      ints: 0
+      ints: 0
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 2
+      ints: 2
+      type: INTS
+    }
+  }
+  node {
+    input: "Input_3"
+    input: "Input_4"
+    input: "Input_5"
+    output: "Conv_1"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 30
+      ints: 30
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 0
+      ints: 0
+      ints: 0
+      ints: 0
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 10
+      ints: 10
+      type: INTS
+    }
+  }
+  node {
+    input: "Input_6"
+    input: "Input_7"
+    input: "Input_8"
+    output: "Conv_2"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 0
+      ints: 0
+      ints: 0
+      ints: 0
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Conv_2"
+    input: "Conv_1"
+    input: "Conv_0"
+    output: "Concat_0"
+    op_type: "Concat"
+    attribute {
+      name: "axis"
+      i: 1
+      type: INT
+    }
+  }
+  node {
+    input: "Concat_0"
+    input: "Input_9"
+    input: "Input_10"
+    output: "Conv_3"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 1
+      ints: 1
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Conv_3"
+    output: "LeakyRelu_0"
+    op_type: "LeakyRelu"
+    attribute {
+      name: "alpha"
+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
+    input: "LeakyRelu_0"
+    input: "Input_11"
+    input: "Input_12"
+    output: "Conv_4"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 1
+      ints: 1
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Conv_4"
+    output: "LeakyRelu_1"
+    op_type: "LeakyRelu"
+    attribute {
+      name: "alpha"
+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
+    input: "LeakyRelu_1"
+    input: "Input_13"
+    input: "Input_14"
+    output: "Conv_5"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 1
+      ints: 1
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "LeakyRelu_0"
+    input: "Conv_5"
+    output: "Add_0"
+    op_type: "Add"
+  }
+  node {
+    input: "Add_0"
+    input: "Input_15"
+    input: "Input_16"
+    output: "Conv_6"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 1
+      ints: 1
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Conv_6"
+    output: "LeakyRelu_2"
+    op_type: "LeakyRelu"
+    attribute {
+      name: "alpha"
+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
+    input: "LeakyRelu_2"
+    input: "Input_17"
+    input: "Input_18"
+    output: "Conv_7"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 1
+      ints: 1
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Add_0"
+    input: "Conv_7"
+    output: "Add_1"
+    op_type: "Add"
+  }
+  node {
+    input: "Add_1"
+    input: "Input_19"
+    input: "Input_20"
+    output: "Conv_8"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 1
+      ints: 1
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Conv_8"
+    output: "LeakyRelu_3"
+    op_type: "LeakyRelu"
+    attribute {
+      name: "alpha"
+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
+    input: "LeakyRelu_3"
+    input: "Input_21"
+    input: "Input_22"
+    output: "Conv_9"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 1
+      ints: 1
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Add_1"
+    input: "Conv_9"
+    output: "Add_2"
+    op_type: "Add"
+  }
+  node {
+    input: "Add_2"
+    input: "Input_23"
+    input: "Input_24"
+    output: "Conv_10"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 1
+      ints: 1
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Conv_10"
+    output: "LeakyRelu_4"
+    op_type: "LeakyRelu"
+    attribute {
+      name: "alpha"
+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
+    input: "LeakyRelu_4"
+    input: "Input_25"
+    input: "Input_26"
+    output: "Conv_11"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 1
+      ints: 1
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Add_2"
+    input: "Conv_11"
+    output: "Add_3"
+    op_type: "Add"
+  }
+  node {
+    input: "Add_3"
+    input: "Input_27"
+    input: "Input_28"
+    output: "Conv_12"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 1
+      ints: 1
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Conv_12"
+    output: "LeakyRelu_5"
+    op_type: "LeakyRelu"
+    attribute {
+      name: "alpha"
+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
+    input: "LeakyRelu_5"
+    input: "Input_29"
+    input: "Input_30"
+    output: "Conv_13"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 1
+      ints: 1
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Add_3"
+    input: "Conv_13"
+    output: "Add_4"
+    op_type: "Add"
+  }
+  node {
+    input: "Add_4"
+    input: "Input_31"
+    input: "Input_32"
+    output: "Conv_14"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 1
+      ints: 1
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Conv_14"
+    output: "LeakyRelu_6"
+    op_type: "LeakyRelu"
+    attribute {
+      name: "alpha"
+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
+    input: "LeakyRelu_6"
+    input: "Input_33"
+    input: "Input_34"
+    output: "Conv_15"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 1
+      ints: 1
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Add_4"
+    input: "Conv_15"
+    output: "Add_5"
+    op_type: "Add"
+  }
+  node {
+    input: "Add_5"
+    input: "Input_35"
+    input: "Input_36"
+    output: "Conv_16"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 1
+      ints: 1
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Conv_16"
+    output: "LeakyRelu_7"
+    op_type: "LeakyRelu"
+    attribute {
+      name: "alpha"
+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
+    input: "LeakyRelu_7"
+    input: "Input_37"
+    input: "Input_38"
+    output: "Conv_17"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 1
+      ints: 1
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Add_5"
+    input: "Conv_17"
+    output: "Add_6"
+    op_type: "Add"
+  }
+  node {
+    input: "Add_6"
+    input: "Input_39"
+    input: "Input_40"
+    output: "Conv_18"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 1
+      ints: 1
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Conv_18"
+    output: "LeakyRelu_8"
+    op_type: "LeakyRelu"
+    attribute {
+      name: "alpha"
+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
+    input: "LeakyRelu_8"
+    input: "Input_41"
+    input: "Input_42"
+    output: "Conv_19"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 1
+      ints: 1
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Add_6"
+    input: "Conv_19"
+    output: "Add_7"
+    op_type: "Add"
+  }
+  node {
+    input: "Add_7"
+    input: "Input_43"
+    input: "Input_44"
+    output: "Conv_20"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 1
+      ints: 1
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Conv_20"
+    output: "LeakyRelu_9"
+    op_type: "LeakyRelu"
+    attribute {
+      name: "alpha"
+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
+    input: "LeakyRelu_9"
+    input: "Input_45"
+    input: "Input_46"
+    output: "Conv_21"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 1
+      ints: 1
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Add_7"
+    input: "Conv_21"
+    output: "Add_8"
+    op_type: "Add"
+  }
+  node {
+    input: "Add_8"
+    input: "Input_47"
+    input: "Input_48"
+    output: "Conv_22"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 1
+      ints: 1
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Conv_22"
+    output: "LeakyRelu_10"
+    op_type: "LeakyRelu"
+    attribute {
+      name: "alpha"
+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
+    input: "LeakyRelu_10"
+    input: "Input_49"
+    input: "Input_50"
+    output: "Conv_23"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 1
+      ints: 1
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Add_8"
+    input: "Conv_23"
+    output: "Add_9"
+    op_type: "Add"
+  }
+  node {
+    input: "Add_9"
+    input: "Input_51"
+    input: "Input_52"
+    output: "Conv_24"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 1
+      ints: 1
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Conv_24"
+    output: "LeakyRelu_11"
+    op_type: "LeakyRelu"
+    attribute {
+      name: "alpha"
+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
+    input: "LeakyRelu_11"
+    input: "Input_53"
+    input: "Input_54"
+    output: "Conv_25"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 1
+      ints: 1
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Add_9"
+    input: "Conv_25"
+    output: "Add_10"
+    op_type: "Add"
+  }
+  node {
+    input: "Add_10"
+    input: "Input_55"
+    input: "Input_56"
+    output: "Conv_26"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 1
+      ints: 1
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Conv_26"
+    output: "LeakyRelu_12"
+    op_type: "LeakyRelu"
+    attribute {
+      name: "alpha"
+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
+    input: "LeakyRelu_12"
+    input: "Input_57"
+    input: "Input_58"
+    output: "Conv_27"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 1
+      ints: 1
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Add_10"
+    input: "Conv_27"
+    output: "Add_11"
+    op_type: "Add"
+  }
+  node {
+    input: "Add_11"
+    input: "Input_59"
+    input: "Input_60"
+    output: "Conv_28"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 1
+      ints: 1
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Conv_28"
+    output: "LeakyRelu_13"
+    op_type: "LeakyRelu"
+    attribute {
+      name: "alpha"
+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
+    input: "LeakyRelu_13"
+    input: "Input_61"
+    input: "Input_62"
+    output: "Conv_29"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 1
+      ints: 1
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Add_11"
+    input: "Conv_29"
+    output: "Add_12"
+    op_type: "Add"
+  }
+  node {
+    input: "Add_12"
+    input: "Input_63"
+    input: "Input_64"
+    output: "Conv_30"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 1
+      ints: 1
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Conv_30"
+    output: "LeakyRelu_14"
+    op_type: "LeakyRelu"
+    attribute {
+      name: "alpha"
+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
+    input: "LeakyRelu_14"
+    input: "Input_65"
+    input: "Input_66"
+    output: "Conv_31"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 1
+      ints: 1
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Add_12"
+    input: "Conv_31"
+    output: "Add_13"
+    op_type: "Add"
+  }
+  node {
+    input: "Add_13"
+    input: "Input_67"
+    input: "Input_68"
+    output: "Conv_32"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 1
+      ints: 1
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Conv_32"
+    output: "LeakyRelu_15"
+    op_type: "LeakyRelu"
+    attribute {
+      name: "alpha"
+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
+    input: "LeakyRelu_15"
+    input: "Input_69"
+    input: "Input_70"
+    output: "Conv_33"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 1
+      ints: 1
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Add_13"
+    input: "Conv_33"
+    output: "Add_14"
+    op_type: "Add"
+  }
+  node {
+    input: "Add_14"
+    input: "Input_71"
+    input: "Input_72"
+    output: "Conv_34"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 1
+      ints: 1
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Conv_34"
+    output: "LeakyRelu_16"
+    op_type: "LeakyRelu"
+    attribute {
+      name: "alpha"
+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
+    input: "LeakyRelu_16"
+    input: "Input_73"
+    input: "Input_74"
+    output: "Conv_35"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 1
+      ints: 1
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Add_14"
+    input: "Conv_35"
+    output: "Add_15"
+    op_type: "Add"
+  }
+  node {
+    input: "Add_15"
+    input: "Input_75"
+    input: "Input_76"
+    output: "Conv_36"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 1
+      ints: 1
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "LeakyRelu_0"
+    input: "Conv_36"
+    output: "Add_16"
+    op_type: "Add"
+  }
+  node {
+    input: "Add_16"
+    input: "Input_77"
+    input: "Input_78"
+    output: "Conv_37"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 1
+      ints: 1
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Conv_37"
+    output: "DepthToSpace_0"
+    op_type: "DepthToSpace"
+    attribute {
+      name: "blocksize"
+      i: 2
+      type: INT
+    }
+  }
+  node {
+    input: "DepthToSpace_0"
+    output: "LeakyRelu_17"
+    op_type: "LeakyRelu"
+    attribute {
+      name: "alpha"
+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
+    input: "LeakyRelu_17"
+    input: "Input_79"
+    input: "Input_80"
+    output: "Conv_38"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 1
+      ints: 1
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Conv_38"
+    output: "DepthToSpace_1"
+    op_type: "DepthToSpace"
+    attribute {
+      name: "blocksize"
+      i: 2
+      type: INT
+    }
+  }
+  node {
+    input: "DepthToSpace_1"
+    output: "LeakyRelu_18"
+    op_type: "LeakyRelu"
+    attribute {
+      name: "alpha"
+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
+    input: "LeakyRelu_18"
+    input: "Input_81"
+    input: "Input_82"
+    output: "Conv_39"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 9
+      ints: 9
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 4
+      ints: 4
+      ints: 4
+      ints: 4
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  name: "Graph"
+  input {
+    name: "Input_4"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 30
+          }
+          dim {
+            dim_value: 30
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_5"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_1"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 6
+          }
+          dim {
+            dim_value: 6
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_2"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_7"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_8"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_75"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_76"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_81"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 9
+          }
+          dim {
+            dim_value: 9
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_82"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 1
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_9"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 96
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_10"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_77"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 256
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_78"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 256
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_79"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 256
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_80"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 256
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_11"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_12"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_13"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_14"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_71"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_72"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_73"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_74"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_67"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_68"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_69"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_70"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_63"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_64"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_65"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_66"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_59"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_60"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_61"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_62"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_55"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_56"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_57"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_58"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_51"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_52"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_53"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_54"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_47"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_48"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_49"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_50"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_43"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_44"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_45"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_46"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_39"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_40"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_41"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_42"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_35"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_36"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_37"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_38"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_31"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_32"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_33"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_34"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_27"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_28"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_29"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_30"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_23"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_24"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_25"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_26"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_19"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_20"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_21"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_22"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_15"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_16"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_17"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_18"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_6"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 10
+          }
+          dim {
+            dim_value: 10
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_3"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 100
+          }
+          dim {
+            dim_value: 100
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_0"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 20
+          }
+          dim {
+            dim_value: 20
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "Conv_39"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 32
+          }
+          dim {
+            dim_value: 32
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  domain: ""
+  version: 8
+}
+
diff --git a/srgan_train.ipynb b/srgan_train.ipynb
index deeea65..a0da7a2 100644
--- a/srgan_train.ipynb
+++ b/srgan_train.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Super-Resolution Generative Adversarial Network training\n",
+    "# **Super-Resolution Generative Adversarial Network training**\n",
     "\n",
     "Here in this jupyter notebook, we will train a super-resolution generative adversarial network (SRGAN), to create a high-resolution Antarctic bed Digital Elevation Model(DEM) from a low-resolution DEM.\n",
     "In addition to that, we use additional correlated inputs that can also tell us something about the bed topography.\n",
@@ -16,7 +16,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 0. Setup libraries"
+    "# 0. Setup libraries"
    ]
   },
   {
@@ -24,32 +24,25 @@
    "execution_count": 1,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Python       : 3.6.6 | packaged by conda-forge | (default, Oct 11 2018, 14:33:06) \n",
-      "Numpy        : 1.14.5\n",
-      "Keras        : 2.2.4\n",
-      "Tensorflow   : 1.10.1\n"
+      "Platform: Linux-4.15.0-43-generic-x86_64-with-debian-stretch-sid\n",
+      "Chainer: 6.0.0b1\n",
+      "NumPy: 1.14.5\n",
+      "CuPy:\n",
+      "  CuPy Version          : 6.0.0b1\n",
+      "  CUDA Root             : /usr/local/cuda\n",
+      "  CUDA Build Version    : 9020\n",
+      "  CUDA Driver Version   : 10000\n",
+      "  CUDA Runtime Version  : 9020\n",
+      "  cuDNN Build Version   : 7301\n",
+      "  cuDNN Version         : 7201\n",
+      "  NCCL Build Version    : 2307\n",
+      "iDeep: Not Available\n"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'/device:GPU:0'"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -67,33 +60,19 @@
     "import pandas as pd\n",
     "import quilt\n",
     "import skimage.transform\n",
-    "import sklearn.model_selection\n",
     "import tqdm\n",
     "\n",
-    "import keras\n",
-    "from keras import backend as K\n",
-    "from keras.layers import (\n",
-    "    Add,\n",
-    "    BatchNormalization,\n",
-    "    Concatenate,\n",
-    "    Conv2D,\n",
-    "    Conv2DTranspose,\n",
-    "    Dense,\n",
-    "    Flatten,\n",
-    "    Input,\n",
-    "    Lambda,\n",
-    ")\n",
-    "from keras.layers.advanced_activations import LeakyReLU\n",
-    "from keras.models import Model\n",
+    "import chainer\n",
+    "import chainer.functions as F\n",
+    "import chainer.links as L\n",
+    "import cupy\n",
     "import livelossplot\n",
+    "import onnx_chainer\n",
     "\n",
     "from features.environment import _load_ipynb_modules\n",
     "\n",
     "print(\"Python       :\", sys.version.split(\"\\n\")[0])\n",
-    "print(\"Numpy        :\", np.__version__)\n",
-    "print(\"Keras        :\", keras.__version__)\n",
-    "print(\"Tensorflow   :\", K.tf.__version__)\n",
-    "K.tf.test.gpu_device_name()"
+    "chainer.print_runtime_info()"
    ]
   },
   {
@@ -105,7 +84,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "COMET INFO: Experiment is live on comet.ml https://www.comet.ml/weiji14/deepbedmap/497bd90c68d74aaa97a63818161b3897\n",
+      "COMET INFO: old comet version (1.0.42) detected. current: 1.0.43 please update your comet lib with command: `pip install --no-cache-dir --upgrade comet_ml`\n",
+      "COMET INFO: Experiment is live on comet.ml https://www.comet.ml/weiji14/deepbedmap/bc5b3144750442a1ab0230509489940b\n",
       "\n"
      ]
     }
@@ -115,17 +95,19 @@
     "seed = 42\n",
     "random.seed = seed\n",
     "np.random.seed(seed=seed)\n",
-    "K.tf.set_random_seed(seed=seed)\n",
+    "# cupy.random.seed(seed=seed)\n",
     "\n",
     "# Start tracking experiment using Comet.ML\n",
-    "experiment = comet_ml.Experiment(workspace=\"weiji14\", project_name=\"deepbedmap\", disabled=False)"
+    "experiment = comet_ml.Experiment(\n",
+    "    workspace=\"weiji14\", project_name=\"deepbedmap\", disabled=False\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 1. Load data"
+    "# 1. Load data"
    ]
   },
   {
@@ -178,76 +160,133 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Split dataset into training (train) and development (dev) sets"
+    "## 1.1 Convert arrays for Chainer\n",
+    "- From Numpy (CPU) to CuPy (GPU) format\n",
+    "- From NHWC format to NCHW format, where N=number of tiles, H=height, W=width, C=channels"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using GPU\n"
+     ]
+    }
+   ],
    "source": [
-    "def train_dev_split(dataset: np.ndarray, test_size=0.05, random_state=42):\n",
-    "    \"\"\"\n",
-    "    Split our dataset up into training and development sets.\n",
-    "    Used for cross validation purposes to check for overfitting.\n",
-    "\n",
-    "    >>> dataset = np.ones(shape=(100, 4, 4, 1))\n",
-    "    >>> train, dev = train_dev_split(dataset=dataset, test_size=0.05, random_state=42)\n",
-    "    >>> train.shape\n",
-    "    (95, 4, 4, 1)\n",
-    "    >>> dev.shape\n",
-    "    (5, 4, 4, 1)\n",
-    "    \"\"\"\n",
-    "    return sklearn.model_selection.train_test_split(\n",
-    "        dataset,\n",
-    "        test_size=test_size,\n",
-    "        train_size=1 - test_size,\n",
-    "        random_state=random_state,\n",
-    "        shuffle=True,\n",
-    "    )"
+    "# Detect if there is a CUDA GPU first\n",
+    "try:\n",
+    "    cupy.cuda.get_device_id()\n",
+    "    xp = cupy\n",
+    "    print(\"Using GPU\")\n",
+    "\n",
+    "    W1_data = chainer.backend.cuda.to_gpu(array=W1_data)\n",
+    "    W2_data = chainer.backend.cuda.to_gpu(array=W2_data)\n",
+    "    X_data = chainer.backend.cuda.to_gpu(array=X_data)\n",
+    "    Y_data = chainer.backend.cuda.to_gpu(array=Y_data)\n",
+    "except:  # CUDARuntimeError\n",
+    "    xp = np\n",
+    "    print(\"Using CPU only\")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2480, 1, 100, 100) (2480, 1, 20, 20) (2480, 1, 10, 10) (2480, 1, 32, 32)\n"
+     ]
+    }
+   ],
+   "source": [
+    "W1_data = xp.rollaxis(a=W1_data, axis=3, start=1)\n",
+    "W2_data = xp.rollaxis(a=W2_data, axis=3, start=1)\n",
+    "X_data = xp.rollaxis(a=X_data, axis=3, start=1)\n",
+    "Y_data = xp.rollaxis(a=Y_data, axis=3, start=1)\n",
+    "print(W1_data.shape, W2_data.shape, X_data.shape, Y_data.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.2 Split dataset into training (train) and development (dev) sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training dataset: 2356 tiles, Test dataset: 124 tiles\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset = chainer.datasets.DictDataset(X=X_data, W1=W1_data, W2=W2_data, Y=Y_data)\n",
+    "train_set, dev_set = chainer.datasets.split_dataset_random(\n",
+    "    dataset=dataset, first_size=int(len(X_data) * 0.95), seed=seed\n",
+    ")\n",
+    "print(f\"Training dataset: {len(train_set)} tiles, Test dataset: {len(dev_set)} tiles\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "W1_train, W1_dev = train_dev_split(dataset=W1_data)\n",
-    "W2_train, W2_dev = train_dev_split(dataset=W2_data)\n",
-    "X_train, X_dev = train_dev_split(dataset=X_data)\n",
-    "Y_train, Y_dev = train_dev_split(dataset=Y_data)"
+    "batch_size = 32\n",
+    "train_iter = chainer.iterators.SerialIterator(\n",
+    "    dataset=train_set, batch_size=batch_size, repeat=True, shuffle=True\n",
+    ")\n",
+    "dev_iter = chainer.iterators.SerialIterator(\n",
+    "    dataset=dev_set, batch_size=batch_size, repeat=True, shuffle=False\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 2. Architect model **(Note: Work in Progress!!)**\n",
+    "# 2. Architect model **(Note: Work in Progress!!)**\n",
     "\n",
     "Enhanced Super Resolution Generative Adversarial Network (ESRGAN) model based on [Wang et al. 2018](https://arxiv.org/abs/1809.00219v2).\n",
     "Refer to original Pytorch implementation at https://github.com/xinntao/ESRGAN.\n",
-    "\n",
-    "See also previous (non-enhanced) SRGAN model architecture based on [Ledig et al. 2017](https://arxiv.org/abs/1609.04802).\n",
-    "Keras implementation below takes some hints from  https://github.com/eriklindernoren/Keras-GAN/blob/master/srgan/srgan.py"
+    "See also previous (non-enhanced) SRGAN model architecture by [Ledig et al. 2017](https://arxiv.org/abs/1609.04802)."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Generator Network Architecture\n",
+    "## 2.1 Generator Network Architecture\n",
     "\n",
     "![ESRGAN architecture - Generator Network composed of many Dense Convolutional Blocks](https://github.com/xinntao/ESRGAN/raw/master/figures/architecture.jpg)\n",
-    "![The Residual in Residual Dense Block in detail](https://github.com/xinntao/ESRGAN/raw/master/figures/RRDB.png)\n",
-    "![3 inputs feeding into the Generator Network, producing a high resolution prediction output](https://yuml.me/01862e1a.png)\n",
     "\n",
-    "Details of the first convolutional layer:\n",
+    "3 main components: 1) Input Block, 2) Residual Blocks, 3) Upsampling Blocks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.1.1 Input block, specially customized for DeepBedMap to take in 3 different inputs\n",
+    "\n",
+    "Details of the first convolutional layer for each input:\n",
     "\n",
     "- Input tiles are 8000m by 8000m.\n",
     "- Convolution filter kernels are 3000m by 3000m.\n",
@@ -259,139 +298,299 @@
     "- Convolution filter kernels are 30pixels by 30pixels\n",
     "- Strides are 10pixels by 10pixels\n",
     "\n",
-    "Note that first convolutional layer uses '**valid**' padding, see https://keras.io/layers/convolutional/ for more information.\n",
+    "Note that these first convolutional layers uses '**valid**' padding, see https://keras.io/layers/convolutional/ for more information."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "class DeepbedmapInputBlock(chainer.Chain):\n",
+    "    \"\"\"\n",
+    "    Custom input block for DeepBedMap.\n",
+    "\n",
+    "    Each filter kernel is 3km by 3km in size, with a 1km stride and no padding.\n",
+    "    So for a 1km resolution image, (i.e. 1km pixel size):\n",
+    "    kernel size is (3, 3), stride is (1, 1), and pad is (0, 0)\n",
+    "\n",
+    "      (?,1,10,10) --Conv2D-- (?,32,8,8) \\\n",
+    "    (?,1,100,100) --Conv2D-- (?,32,8,8) --Concat-- (?,96,8,8)\n",
+    "      (?,1,20,20) --Conv2D-- (?,32,8,8) /\n",
+    "\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, out_channels=32):\n",
+    "        super().__init__()\n",
+    "        init_weights = chainer.initializers.GlorotUniform(scale=1.0)\n",
+    "\n",
+    "        with self.init_scope():\n",
+    "            self.conv_on_X = L.Convolution2D(\n",
+    "                in_channels=1,\n",
+    "                out_channels=out_channels,\n",
+    "                ksize=(3, 3),\n",
+    "                stride=(1, 1),\n",
+    "                pad=(0, 0),  # 'valid' padding\n",
+    "                initialW=init_weights,\n",
+    "            )\n",
+    "            self.conv_on_W1 = L.Convolution2D(\n",
+    "                in_channels=1,\n",
+    "                out_channels=out_channels,\n",
+    "                ksize=(30, 30),\n",
+    "                stride=(10, 10),\n",
+    "                pad=(0, 0),  # 'valid' padding\n",
+    "                initialW=init_weights,\n",
+    "            )\n",
+    "            self.conv_on_W2 = L.Convolution2D(\n",
+    "                in_channels=1,\n",
+    "                out_channels=out_channels,\n",
+    "                ksize=(6, 6),\n",
+    "                stride=(2, 2),\n",
+    "                pad=(0, 0),  # 'valid' padding\n",
+    "                initialW=init_weights,\n",
+    "            )\n",
+    "\n",
+    "    def forward(self, x, w1, w2):\n",
+    "        \"\"\"\n",
+    "        Forward computation, i.e. evaluate based on inputs X, W1 and W2\n",
+    "        \"\"\"\n",
+    "        x_ = self.conv_on_X(x)\n",
+    "        w1_ = self.conv_on_W1(w1)\n",
+    "        w2_ = self.conv_on_W2(w2)\n",
+    "\n",
+    "        a = F.concat(xs=(x_, w1_, w2_))\n",
+    "        return a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.1.2 Residual Block\n",
+    "\n",
+    "![The Residual in Residual Dense Block in detail](https://arxiv-sanity-sanity-production.s3.amazonaws.com/render-output/518727/x4.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "class ResidualBlock(chainer.Chain):\n",
+    "    \"\"\"\n",
+    "    Residual block made of Convoutional2D-LeakyReLU-Convoutional2D layers\n",
+    "\n",
+    "       -----------------------------\n",
+    "      |                             |\n",
+    "    -----Conv2D--LeakyReLu--Conv2D-(+)--\n",
+    "\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, out_channels=64):\n",
+    "        super().__init__()\n",
+    "        init_weights = chainer.initializers.GlorotUniform(scale=1.0)\n",
+    "\n",
+    "        with self.init_scope():\n",
+    "            self.conv_layer1 = L.Convolution2D(\n",
+    "                in_channels=None,\n",
+    "                out_channels=out_channels,\n",
+    "                ksize=(3, 3),\n",
+    "                stride=(1, 1),\n",
+    "                pad=1,  # 'same' padding\n",
+    "                initialW=init_weights,\n",
+    "            )\n",
+    "            self.conv_layer2 = L.Convolution2D(\n",
+    "                in_channels=out_channels,\n",
+    "                out_channels=out_channels,\n",
+    "                ksize=(3, 3),\n",
+    "                stride=(1, 1),\n",
+    "                pad=1,  # 'same' padding\n",
+    "                initialW=init_weights,\n",
+    "            )\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        \"\"\"\n",
+    "        Forward computation, i.e. evaluate based on input x\n",
+    "        \"\"\"\n",
+    "        a = self.conv_layer1(x)\n",
+    "        a = F.leaky_relu(x=a, slope=0.2)\n",
+    "        a = self.conv_layer2(a)\n",
+    "\n",
+    "        a = F.add(x, a)\n",
+    "        return a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.1.3 Build the Generator Network, with upsampling layers!\n",
+    "\n",
+    "![3 inputs feeding into the Generator Network, producing a high resolution prediction output](https://yuml.me/dffffcb0.png)\n",
     "\n",
     "<!--[W2_input(MEASURES)|20x20x1]-k6n32s2>[W2_inter|8x8x32],[W2_inter]->[Concat|8x8x96]\n",
     "[X_input(BEDMAP2)|10x10x1]-k3n32s1>[X_inter|8x8x32],[X_inter]->[Concat|8x8x96]\n",
     "[W1_input(REMA)|100x100x1]-k30n32s10>[W1_inter|8x8x32],[W1_inter]->[Concat|8x8x96]\n",
-    "[Concat|8x8x96]->[Generator-Network],[Generator-Network]->[Y_hat(High-Resolution_DEM)|32x32x1]-->"
+    "[Concat|8x8x96]->[Generator-Network|Many-Residual-Blocks],[Generator-Network]->[Y_hat(High-Resolution_DEM)|32x32x1]-->"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "metadata": {
     "lines_to_next_cell": 2
    },
    "outputs": [],
    "source": [
-    "def generator_network(\n",
-    "    input1_shape: typing.Tuple[int, int, int] = (10, 10, 1),\n",
-    "    input2_shape: typing.Tuple[int, int, int] = (100, 100, 1),\n",
-    "    input3_shape: typing.Tuple[int, int, int] = (20, 20, 1),\n",
-    "    num_residual_blocks: int = 16,\n",
-    "    scaling: int = 4,\n",
-    "    output_channels: int = 1,\n",
-    ") -> keras.engine.network.Network:\n",
+    "class GeneratorModel(chainer.Chain):\n",
     "    \"\"\"\n",
     "    The generator network which is a deconvolutional neural network.\n",
     "    Converts a low resolution input into a super resolution output.\n",
     "\n",
+    "    Glues the input block with several residual blocks and upsampling layers\n",
+    "\n",
     "    Parameters:\n",
     "      input_shape -- shape of input tensor in tuple format (height, width, channels)\n",
     "      num_residual_blocks -- how many Conv-LeakyReLU-Conv blocks to use\n",
     "      scaling -- even numbered integer to increase resolution (e.g. 0, 2, 4, 6, 8)\n",
-    "      output_channels -- integer representing number of output channels/filters/kernels\n",
+    "      out_channels -- integer representing number of output channels/filters/kernels\n",
     "\n",
     "    Example:\n",
     "      An input_shape of (8,8,1) passing through 16 residual blocks with a scaling of 4\n",
     "      and output_channels 1 will result in an image of shape (32,32,1)\n",
     "\n",
-    "    >>> generator_network().input_shape\n",
-    "    [(None, 10, 10, 1), (None, 100, 100, 1), (None, 20, 20, 1)]\n",
-    "    >>> generator_network().output_shape\n",
-    "    (None, 32, 32, 1)\n",
-    "    >>> generator_network().count_params()\n",
+    "    >>> generator_model = GeneratorModel(\n",
+    "    ...     inblock_class=DeepbedmapInputBlock,\n",
+    "    ...     resblock_class=ResidualBlock,\n",
+    "    ...     num_residual_blocks=16,\n",
+    "    ... )\n",
+    "    >>> y_pred = generator_model.forward(\n",
+    "    ...     inputs={\n",
+    "    ...         \"x\": np.random.rand(1, 1, 10, 10).astype(\"float32\"),\n",
+    "    ...         \"w1\": np.random.rand(1, 1, 100, 100).astype(\"float32\"),\n",
+    "    ...         \"w2\": np.random.rand(1, 1, 20, 20).astype(\"float32\"),\n",
+    "    ...     }\n",
+    "    ... )\n",
+    "    >>> y_pred.shape\n",
+    "    (1, 1, 32, 32)\n",
+    "    >>> generator_model.count_params()\n",
     "    1604929\n",
     "    \"\"\"\n",
     "\n",
-    "    assert num_residual_blocks >= 1  # ensure that we have 1 or more residual blocks\n",
-    "    assert scaling % 2 == 0  # ensure scaling factor is even, i.e. 0, 2, 4, 8, etc\n",
-    "    assert scaling >= 0  # ensure that scaling factor is zero or a positive number\n",
-    "    assert output_channels >= 1  # ensure that we have 1 or more output channels\n",
-    "\n",
-    "    ## Input images\n",
-    "    inp1 = Input(shape=input1_shape)  # low resolution image\n",
-    "    assert inp1.shape.ndims == 4  # has to be shape like (?,10,10,1) for 10x10 grid\n",
-    "    inp2 = Input(shape=input2_shape)  # other image (e.g. REMA)\n",
-    "    assert inp2.shape.ndims == 4  # has to be shape like (?,100,100,1) for 100x100 grid\n",
-    "    inp3 = Input(shape=input3_shape)  # other image (MEASURES Ice Flow)\n",
-    "    assert inp3.shape.ndims == 4  # has to be shape like (?,20,20,1) for 20x20 grid\n",
-    "\n",
-    "    # 0 part\n",
-    "    # Resize inputs to right scale using convolution (hardcoded kernel_size and strides)\n",
-    "    inp1r = Conv2D(filters=32, kernel_size=(3, 3), strides=(1, 1), padding=\"valid\")(\n",
-    "        inp1\n",
-    "    )\n",
-    "    inp2r = Conv2D(filters=32, kernel_size=(30, 30), strides=(10, 10), padding=\"valid\")(\n",
-    "        inp2\n",
-    "    )\n",
-    "    inp3r = Conv2D(filters=32, kernel_size=(6, 6), strides=(2, 2), padding=\"valid\")(\n",
-    "        inp3\n",
-    "    )\n",
-    "\n",
-    "    # Concatenate all inputs\n",
-    "    # SEE https://distill.pub/2016/deconv-checkerboard/\n",
-    "    X = Concatenate()([inp1r, inp2r, inp3r])  # Concatenate all the inputs together\n",
-    "\n",
-    "    # 1st part\n",
-    "    # Pre-residual k3n64s1 (originally k9n64s1)\n",
-    "    X0 = Conv2D(filters=64, kernel_size=(3, 3), strides=(1, 1), padding=\"same\")(X)\n",
-    "    X0 = LeakyReLU(alpha=0.2)(X0)\n",
-    "\n",
-    "    # 2nd part\n",
-    "    # Residual blocks k3n64s1\n",
-    "    def residual_block(input_tensor):\n",
-    "        x = Conv2D(filters=64, kernel_size=(3, 3), strides=(1, 1), padding=\"same\")(\n",
-    "            input_tensor\n",
-    "        )\n",
-    "        x = LeakyReLU(alpha=0.2)(x)\n",
-    "        x = Conv2D(filters=64, kernel_size=(3, 3), strides=(1, 1), padding=\"same\")(x)\n",
-    "        return Add()([x, input_tensor])\n",
-    "\n",
-    "    X = residual_block(X0)\n",
-    "    for _ in range(num_residual_blocks - 1):\n",
-    "        X = residual_block(X)\n",
-    "\n",
-    "    # 3rd part\n",
-    "    # Post-residual blocks k3n64s1\n",
-    "    X = Conv2D(filters=64, kernel_size=(3, 3), strides=(1, 1), padding=\"same\")(X)\n",
-    "    X = Add()([X, X0])\n",
-    "\n",
-    "    # 4th part\n",
-    "    # Upsampling (if 4; run twice, if 8; run thrice, etc.) k3n256s1\n",
-    "    for p, _ in enumerate(range(scaling // 2), start=1):\n",
-    "        X = Conv2D(filters=256, kernel_size=(3, 3), strides=(1, 1), padding=\"same\")(X)\n",
-    "        pixelshuffleup = lambda images: K.tf.depth_to_space(input=images, block_size=2)\n",
-    "        X = Lambda(function=pixelshuffleup, name=f\"pixelshuffleup_{p}\")(X)\n",
-    "        X = LeakyReLU(alpha=0.2)(X)\n",
-    "\n",
-    "    # 5th part\n",
-    "    # Generate high resolution output k9n1s1 (originally k9n3s1 for RGB image)\n",
-    "    outp = Conv2D(\n",
-    "        filters=output_channels,\n",
-    "        kernel_size=(9, 9),\n",
-    "        strides=(1, 1),\n",
-    "        padding=\"same\",\n",
-    "        name=\"generator_output\",\n",
-    "    )(X)\n",
-    "\n",
-    "    # Create neural network with input low-res images and output prediction\n",
-    "    network = keras.engine.network.Network(\n",
-    "        inputs=[inp1, inp2, inp3], outputs=[outp], name=\"generator_network\"\n",
-    "    )\n",
-    "\n",
-    "    return network"
+    "    def __init__(\n",
+    "        self,\n",
+    "        inblock_class,\n",
+    "        resblock_class,\n",
+    "        num_residual_blocks: int = 16,\n",
+    "        out_channels: int = 1,\n",
+    "    ):\n",
+    "        super().__init__()\n",
+    "        init_weights = chainer.initializers.GlorotUniform(scale=1.0)\n",
+    "\n",
+    "        with self.init_scope():\n",
+    "\n",
+    "            # Initial Input and Residual Blocks\n",
+    "            self.input_block = inblock_class()\n",
+    "            self.pre_residual_conv_layer = L.Convolution2D(\n",
+    "                in_channels=None,\n",
+    "                out_channels=64,\n",
+    "                ksize=(3, 3),\n",
+    "                stride=(1, 1),\n",
+    "                pad=1,  # 'same' padding\n",
+    "                initialW=init_weights,\n",
+    "            )\n",
+    "            self.residual_network = resblock_class().repeat(\n",
+    "                n_repeat=num_residual_blocks\n",
+    "            )\n",
+    "            self.post_residual_conv_layer = L.Convolution2D(\n",
+    "                in_channels=None,\n",
+    "                out_channels=64,\n",
+    "                ksize=(3, 3),\n",
+    "                stride=(1, 1),\n",
+    "                pad=1,  # 'same' padding\n",
+    "                initialW=init_weights,\n",
+    "            )\n",
+    "\n",
+    "            # Upsampling Layers\n",
+    "            self.pre_upsample_conv_layer_1 = L.Convolution2D(\n",
+    "                in_channels=None,\n",
+    "                out_channels=256,\n",
+    "                ksize=(3, 3),\n",
+    "                stride=(1, 1),\n",
+    "                pad=1,  # 'same' padding\n",
+    "                initialW=init_weights,\n",
+    "            )\n",
+    "            self.pre_upsample_conv_layer_2 = L.Convolution2D(\n",
+    "                in_channels=None,\n",
+    "                out_channels=256,\n",
+    "                ksize=(3, 3),\n",
+    "                stride=(1, 1),\n",
+    "                pad=1,  # 'same' padding\n",
+    "                initialW=init_weights,\n",
+    "            )\n",
+    "            self.post_upsample_conv_layer = L.Convolution2D(\n",
+    "                in_channels=None,\n",
+    "                out_channels=out_channels,\n",
+    "                ksize=(9, 9),\n",
+    "                stride=(1, 1),\n",
+    "                pad=4,  # 'same' padding\n",
+    "                initialW=init_weights,\n",
+    "            )\n",
+    "\n",
+    "    def forward(self, inputs: dict):\n",
+    "        \"\"\"\n",
+    "        Forward computation, i.e. evaluate based on inputs\n",
+    "\n",
+    "        Input dictionary needs to have keys \"x\", \"w1\", \"w2\"\n",
+    "        \"\"\"\n",
+    "        # 0 part\n",
+    "        # Resize inputs o right scale using convolution (hardcoded kernel_size and strides)\n",
+    "        # Also concatenate all inputs\n",
+    "        a0 = self.input_block(x=inputs[\"x\"], w1=inputs[\"w1\"], w2=inputs[\"w2\"])\n",
+    "\n",
+    "        # 1st part\n",
+    "        # Pre-residual k3n64s1 (originally k9n64s1)\n",
+    "        a1 = self.pre_residual_conv_layer(a0)\n",
+    "        a1 = F.leaky_relu(x=a1, slope=0.2)\n",
+    "\n",
+    "        # 2nd part\n",
+    "        # Residual blocks k3n64s1\n",
+    "        a2 = self.residual_network(a1)\n",
+    "\n",
+    "        # 3rd part\n",
+    "        # Post-residual blocks k3n64s1\n",
+    "        a3 = self.post_residual_conv_layer(a2)\n",
+    "        a3 = F.add(a1, a3)\n",
+    "\n",
+    "        # 4th part\n",
+    "        # Upsampling (if 4; run twice, if 8; run thrice, etc.) k3n256s1\n",
+    "        a4_1 = self.pre_upsample_conv_layer_1(a3)\n",
+    "        a4_1 = F.depth2space(X=a4_1, r=2)\n",
+    "        a4_1 = F.leaky_relu(x=a4_1, slope=0.2)\n",
+    "        a4_2 = self.pre_upsample_conv_layer_2(a4_1)\n",
+    "        a4_2 = F.depth2space(X=a4_2, r=2)\n",
+    "        a4_2 = F.leaky_relu(x=a4_2, slope=0.2)\n",
+    "\n",
+    "        # 5th part\n",
+    "        # Generate high resolution output k9n1s1 (originally k9n3s1 for RGB image)\n",
+    "        a5 = self.post_upsample_conv_layer(a4_2)\n",
+    "\n",
+    "        return a5"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Discriminator Network Architecture\n",
+    "## 2.2 Discriminator Network Architecture\n",
     "\n",
     "Discriminator component is based on Deep Convolutional Generative Adversarial Networks by [Radford et al., 2015](https://arxiv.org/abs/1511.06434).\n",
-    "Keras implementation below takes some hints from https://github.com/erilyth/DCGANs/blob/master/DCGAN-CIFAR10/dcgan.py and https://github.com/yashk2810/DCGAN-Keras/blob/master/DCGAN.ipynb\n",
     "\n",
     "Note that figure below shows the 2017 (non-enhanced) SRGAN discriminator neural network architecture.\n",
     "The 2018 ESRGAN version is basically the same architecture, as only the loss function was changed.\n",
@@ -404,83 +603,131 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 12,
    "metadata": {
     "lines_to_next_cell": 2
    },
    "outputs": [],
    "source": [
-    "def discriminator_network(\n",
-    "    input_shape: typing.Tuple[int, int, int] = (32, 32, 1)\n",
-    ") -> keras.engine.network.Network:\n",
+    "class DiscriminatorModel(chainer.Chain):\n",
     "    \"\"\"\n",
     "    The discriminator network which is a convolutional neural network.\n",
     "    Takes ONE high resolution input image and predicts whether it is\n",
     "    real or fake on a scale of 0 to 1, where 0 is fake and 1 is real.\n",
     "\n",
-    "    >>> discriminator_network().input_shape\n",
-    "    (None, 32, 32, 1)\n",
-    "    >>> discriminator_network().output_shape\n",
-    "    (None, 1)\n",
-    "    >>> discriminator_network().count_params()\n",
-    "    6828033\n",
-    "    \"\"\"\n",
-    "\n",
-    "    ## Input images\n",
-    "    inp = Input(shape=input_shape)  # high resolution/groundtruth image to discriminate\n",
-    "    assert inp.shape.ndims == 4  # needs to be shape like (?,32,32,1) for 8x8 grid\n",
+    "    Consists of several Conv2D-BatchNorm-LeakyReLU blocks, followed by\n",
+    "    a fully connected linear layer with LeakyReLU activation and a final\n",
+    "    fully connected linear layer with Sigmoid activation.\n",
     "\n",
-    "    # 1st part\n",
-    "    # Convolutonal Block without Batch Normalization k3n64s1\n",
-    "    X = Conv2D(filters=64, kernel_size=(3, 3), strides=(1, 1), padding=\"same\")(inp)\n",
-    "    X = LeakyReLU(alpha=0.2)(X)\n",
-    "\n",
-    "    # 2nd part\n",
-    "    # Convolutional Blocks with Batch Normalization k3n{64*f}s{1or2}\n",
-    "    for f, s in zip([1, 1, 2, 2, 4, 4, 8, 8], [1, 2, 1, 2, 1, 2, 1, 2]):\n",
-    "        X = Conv2D(filters=64 * f, kernel_size=(3, 3), strides=(s, s), padding=\"same\")(\n",
-    "            X\n",
-    "        )\n",
-    "        X = BatchNormalization()(X)\n",
-    "        X = LeakyReLU(alpha=0.2)(X)\n",
-    "\n",
-    "    # 3rd part\n",
-    "    # Flatten, Dense (Fully Connected) Layers and Output\n",
-    "    X = Flatten()(X)\n",
-    "    X = Dense(units=1024)(X)  # ??!! Flatten?\n",
-    "    X = LeakyReLU(alpha=0.2)(X)\n",
-    "    outp = Dense(units=1, activation=\"sigmoid\", name=\"discriminator_output\")(X)\n",
-    "\n",
-    "    # Create neural network with input highres/groundtruth images, output validity 0/1\n",
-    "    network = keras.engine.network.Network(\n",
-    "        inputs=[inp], outputs=[outp], name=\"discriminator_network\"\n",
-    "    )\n",
+    "    >>> discriminator_model = DiscriminatorModel()\n",
+    "    >>> y_pred = discriminator_model.forward(\n",
+    "    ...     inputs={\n",
+    "    ...         \"x\": np.random.rand(2, 1, 32, 32).astype(\"float32\"),\n",
+    "    ...     }\n",
+    "    ... )\n",
+    "    >>> y_pred.shape\n",
+    "    (2, 1)\n",
+    "    >>> discriminator_model.count_params()\n",
+    "    6824193\n",
+    "    \"\"\"\n",
     "\n",
-    "    return network"
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "        init_weights = chainer.initializers.GlorotUniform(scale=1.0)\n",
+    "\n",
+    "        with self.init_scope():\n",
+    "\n",
+    "            self.conv_layer0 = L.Convolution2D(\n",
+    "                in_channels=None,\n",
+    "                out_channels=64,\n",
+    "                ksize=(3, 3),\n",
+    "                stride=(1, 1),\n",
+    "                pad=1,  # 'same' padding\n",
+    "                nobias=False,  # default, have bias\n",
+    "                initialW=init_weights,\n",
+    "            )\n",
+    "            self.conv_layer1 = L.Convolution2D(None, 64, 3, 1, 1, False, init_weights)\n",
+    "            self.conv_layer2 = L.Convolution2D(None, 64, 3, 2, 1, False, init_weights)\n",
+    "            self.conv_layer3 = L.Convolution2D(None, 128, 3, 1, 1, False, init_weights)\n",
+    "            self.conv_layer4 = L.Convolution2D(None, 128, 3, 2, 1, False, init_weights)\n",
+    "            self.conv_layer5 = L.Convolution2D(None, 256, 3, 1, 1, False, init_weights)\n",
+    "            self.conv_layer6 = L.Convolution2D(None, 256, 3, 2, 1, False, init_weights)\n",
+    "            self.conv_layer7 = L.Convolution2D(None, 512, 3, 1, 1, False, init_weights)\n",
+    "            self.conv_layer8 = L.Convolution2D(None, 512, 3, 2, 1, False, init_weights)\n",
+    "\n",
+    "            self.batch_norm1 = L.BatchNormalization(axis=(0, 2, 3), eps=0.001)\n",
+    "            self.batch_norm2 = L.BatchNormalization(axis=(0, 2, 3), eps=0.001)\n",
+    "            self.batch_norm3 = L.BatchNormalization(axis=(0, 2, 3), eps=0.001)\n",
+    "            self.batch_norm4 = L.BatchNormalization(axis=(0, 2, 3), eps=0.001)\n",
+    "            self.batch_norm5 = L.BatchNormalization(axis=(0, 2, 3), eps=0.001)\n",
+    "            self.batch_norm6 = L.BatchNormalization(axis=(0, 2, 3), eps=0.001)\n",
+    "            self.batch_norm7 = L.BatchNormalization(axis=(0, 2, 3), eps=0.001)\n",
+    "            self.batch_norm8 = L.BatchNormalization(axis=(0, 2, 3), eps=0.001)\n",
+    "\n",
+    "            self.linear_1 = L.Linear(in_size=None, out_size=1024, initialW=init_weights)\n",
+    "            self.linear_2 = L.Linear(in_size=None, out_size=1, initialW=init_weights)\n",
+    "\n",
+    "    def forward(self, inputs: dict):\n",
+    "        \"\"\"\n",
+    "        Forward computation, i.e. evaluate based on inputs\n",
+    "\n",
+    "        Input dictionary needs to have keys \"x\"\n",
+    "        \"\"\"\n",
+    "\n",
+    "        # 1st part\n",
+    "        # Convolutonal Block without Batch Normalization k3n64s1\n",
+    "        a0 = self.conv_layer0(x=inputs[\"x\"])\n",
+    "        a0 = F.leaky_relu(x=a0, slope=0.2)\n",
+    "\n",
+    "        # 2nd part\n",
+    "        # Convolutional Blocks with Batch Normalization k3n{64*f}s{1or2}\n",
+    "        a1 = self.conv_layer1(x=a0)\n",
+    "        a1 = self.batch_norm1(x=a1)\n",
+    "        a1 = F.leaky_relu(x=a1, slope=0.2)\n",
+    "        a2 = self.conv_layer2(x=a1)\n",
+    "        a2 = self.batch_norm2(x=a2)\n",
+    "        a2 = F.leaky_relu(x=a2, slope=0.2)\n",
+    "        a3 = self.conv_layer3(x=a2)\n",
+    "        a3 = self.batch_norm3(x=a3)\n",
+    "        a3 = F.leaky_relu(x=a3, slope=0.2)\n",
+    "        a4 = self.conv_layer4(x=a3)\n",
+    "        a4 = self.batch_norm4(x=a4)\n",
+    "        a4 = F.leaky_relu(x=a4, slope=0.2)\n",
+    "        a5 = self.conv_layer5(x=a4)\n",
+    "        a5 = self.batch_norm5(x=a5)\n",
+    "        a5 = F.leaky_relu(x=a5, slope=0.2)\n",
+    "        a6 = self.conv_layer6(x=a5)\n",
+    "        a6 = self.batch_norm6(x=a6)\n",
+    "        a6 = F.leaky_relu(x=a6, slope=0.2)\n",
+    "        a7 = self.conv_layer7(x=a6)\n",
+    "        a7 = self.batch_norm7(x=a7)\n",
+    "        a7 = F.leaky_relu(x=a7, slope=0.2)\n",
+    "        a8 = self.conv_layer8(x=a7)\n",
+    "        a8 = self.batch_norm8(x=a8)\n",
+    "        a8 = F.leaky_relu(x=a8, slope=0.2)\n",
+    "\n",
+    "        # 3rd part\n",
+    "        # Flatten, Dense (Fully Connected) Layers and Output\n",
+    "        a9 = F.reshape(x=a8, shape=(len(a8), -1))  # flatten while keeping batch_size\n",
+    "        a9 = self.linear_1(x=a9)\n",
+    "        a9 = F.leaky_relu(x=a9, slope=0.2)\n",
+    "        a10 = self.linear_2(x=a9)\n",
+    "        # a10 = F.sigmoid(x=a10)  # no sigmoid activation, as it is in the loss function\n",
+    "\n",
+    "        return a10"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Combine Generator and Discriminator Networks\n",
+    "## 2.3 Define Loss function and Metrics for the Generator and Discriminator Networks\n",
     "\n",
-    "Here we combine the Generator and Discriminator neural network models together, and define the Perceptual Loss function where:\n",
+    "Now we define the Perceptual Loss function for our Generator and Discriminator neural network models, where:\n",
     "\n",
     "$$Perceptual Loss = Content Loss + Adversarial Loss$$\n",
     "\n",
-    "The original SRGAN paper by [Ledig et al. 2017](https://arxiv.org/abs/1609.04802v5) calculates *Content Loss* based on the ReLU activation layers of the pre-trained 19 layer VGG network.\n",
-    "The implementation below is less advanced, simply using an L1 loss, i.e., a pixel-wise [Mean Absolute Error (MAE) loss](https://keras.io/losses/#mean_absolute_error) as the *Content Loss*.\n",
-    "Specifically, the *Content Loss* is calculated as the MAE difference between the output of the generator model (i.e. the predicted Super Resolution Image) and that of the groundtruth image (i.e. the true High Resolution Image).\n",
-    "\n",
-    "The *Adversarial Loss* or *Generative Loss* (confusing I know) is the same as in the original SRGAN paper.\n",
-    "It is defined based on the probabilities of the discriminator believing that the reconstructed Super Resolution Image is a natural High Resolution Image.\n",
-    "The implementation below uses the [Binary CrossEntropy loss](https://keras.io/losses/#binary_crossentropy).\n",
-    "Specifically, this *Adversarial Loss* is calculated between the output of the discriminator model (a value between 0 and 1) and that of the groundtruth label (a boolean value of either 0 or 1).\n",
-    "\n",
-    "Source code for the implementations of these loss functions in Keras can be found at https://github.com/keras-team/keras/blob/master/keras/losses.py.\n",
-    "\n",
-    "![Perceptual Loss in an Enhanced Super Resolution Generative Adversarial Network](https://yuml.me/db58d683.png )\n",
+    "![Perceptual Loss in an Enhanced Super Resolution Generative Adversarial Network](https://yuml.me/db58d683.png)\n",
     "\n",
     "<!--\n",
     "[LowRes-Inputs]-Generator>[SuperResolution_DEM]\n",
@@ -496,164 +743,229 @@
     "-->"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Content Loss\n",
+    "\n",
+    "The original SRGAN paper by [Ledig et al. 2017](https://arxiv.org/abs/1609.04802v5) calculates *Content Loss* based on the ReLU activation layers of the pre-trained 19 layer VGG network.\n",
+    "The implementation below is less advanced, simply using an L1 loss, i.e., a pixel-wise [Mean Absolute Error (MAE) loss](https://keras.io/losses/#mean_absolute_error) as the *Content Loss*.\n",
+    "Specifically, the *Content Loss* is calculated as the MAE difference between the output of the generator model (i.e. the predicted Super Resolution Image) and that of the groundtruth image (i.e. the true High Resolution Image).\n",
+    "\n",
+    "$$ e_i = ||G(x_{i}) - y_i||_{1} $$\n",
+    "\n",
+    "$$ Loss_{Content} = Mean Absolute Error = \\dfrac{1}{n} \\sum\\limits_{i=1}^n e_i $$\n",
+    "\n",
+    "where $G(x_{i})$ is the Generator Network's predicted value, and $y_i$ is the groundtruth value, respectively at pixel $i$.\n",
+    "$e_i$ thus represents the absolute error (L1 loss) (denoted by $||\\dots||_{1}$) between the predicted and groundtruth value.\n",
+    "We then sum all the pixel-wise errors $e_i,\\dots,e_n$ and divide by the number of pixels $n$ to get the Arithmetic Mean $\\dfrac{1}{n} \\sum\\limits_{i=1}^n$ of our error which is our *Content Loss*."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adversarial Loss\n",
+    "\n",
+    "The *Adversarial Loss* or *Generative Loss* (confusing I know) is the same as in the original SRGAN paper.\n",
+    "It is defined based on the probabilities of the discriminator believing that the reconstructed Super Resolution Image is a natural High Resolution Image.\n",
+    "The implementation below uses the [Binary CrossEntropy loss](https://keras.io/losses/#binary_crossentropy).\n",
+    "Specifically, this *Adversarial Loss* is calculated between the output of the discriminator model (a value between 0 and 1) and that of the groundtruth label (a boolean value of either 0 or 1).\n",
+    "\n",
+    "$$ Loss_{Adversarial} = Binary Cross Entropy Loss = -\\dfrac{1}{n} \\sum\\limits_{i=1}^n ( y_i ln(\\sigma(x_i)) + (1-y_i) ln(1 - \\sigma(x_i) ) $$\n",
+    "\n",
+    "where $\\sigma$ is the [Sigmoid](https://en.wikipedia.org/wiki/Sigmoid_function) activation function, $\\sigma = \\dfrac{1}{1+e^{-x}} = \\dfrac{e^x}{e^x+1}$, $y_i$ is the groundtruth label (1 for real, 0 for fake) and $x_i$ is the prediction (before sigmoid activation is applied), all respectively at pixel $i$.\n",
+    "\n",
+    "$\\sigma(x)$ is basically the sigmoid activated output from a Standard Discriminator neural network, which some people also denote as $D(.)$.\n",
+    "Technically, some people also write $D(x) = \\sigma(C(x))$, where $C(x)$ is the raw, non-transformed output from the Discriminator neural network (i.e. no sigmoid activation applied) on the input data $x$.\n",
+    "For simplicity, we now denote $C(x)$ simply as $x$ in the following equations, i.e. using $\\sigma(x)$ to replace $\\sigma(C(x))$.\n",
+    "\n",
+    "Again, the [Binary Cross Entropy Loss](https://en.wikipedia.org/wiki/Cross_entropy#Cross-entropy_error_function_and_logistic_regression) calculated on one pixel is defined as follows:\n",
+    "\n",
+    "$$ -( y ln(\\sigma(x)) + (1-y) ln(1 - \\sigma(x) )$$\n",
+    "\n",
+    "With the full expansion as such:\n",
+    "\n",
+    "$$ -\\bigg[ y ln\\big(\\dfrac{e^x}{e^x+1}\\big) + (1-y) ln\\big(1 - \\dfrac{e^x}{e^x+1}\\big) \\bigg] $$\n",
+    "\n",
+    "The above equation is mathematically equivalent to the one below, and can be derived using [Logarithm rules](https://en.wikipedia.org/wiki/Logarithm#Product,_quotient,_power,_and_root) such as the Power Rule and Product Rule, and using the fact that $ln(e)=1$ and $ln(1)=0$:\n",
+    "\n",
+    "$$ -[ xy - ln(1+e^x) ] $$\n",
+    "\n",
+    "However, this reformed equation is numerically unstable (see discussion [here](https://www.reddit.com/r/MachineLearning/comments/4euzmk/logsumexp_for_logistic_regression/)), and is good for values of $x<0$.\n",
+    "For values of $x>=0$, there is an alternative representation which we can derive:\n",
+    "\n",
+    "$$ -[ xy - ln(1+e^x) - x + x ] $$\n",
+    "$$ -[ x(y-1) - ln(1 + e^x) + ln(e^x) ] $$\n",
+    "$$ -\\bigg[ x(y-1) - ln\\big(\\dfrac{e^x}{1+e^x}\\big) \\bigg] $$\n",
+    "$$ -\\bigg[ x(y-1) - ln\\big(\\dfrac{1}{1+e^{-x}}\\big) \\bigg] $$\n",
+    "$$ - [ x(y-1) - ln(1) + ln(1+e^{-x}) ] $$\n",
+    "$$ - [ x(y-1) + ln(1+e^{-x}) $$\n",
+    "\n",
+    "In order to have a numerically stable function that works for both $x<0$ and $x>=0$, we can write it like so as in Caffe's implementation:\n",
+    "\n",
+    "$$ -[ x(y - 1_{x>=0} - ln(1+e^{x-2x\\cdot1_{x>=0}}) ] $$\n",
+    "\n",
+    "Alternatively, Chainer does it like so:\n",
+    "\n",
+    "$$ -[ x(y - 1_{x>=0} - ln(1+e^{-|x|}) ] $$\n",
+    "\n",
+    "Or in Python code (the Chainer implemention from [here](https://github.com/chainer/chainer/blob/v6.0.0b1/chainer/functions/loss/sigmoid_cross_entropy.py#L41-L44)), bearing in mind that the natural logarithm $ln$ is `np.log` in Numpy:\n",
+    "\n",
+    "```python\n",
+    "    sigmoidbinarycrossentropyloss = -(x * (y - (x >= 0)) - np.log1p(np.exp(-np.abs(x))))\n",
+    "```\n",
+    "\n",
+    "See also how [Pytorch](https://pytorch.org/docs/stable/nn.html?highlight=bcewithlogitsloss#torch.nn.BCEWithLogitsLoss) and [Tensorflow](https://www.tensorflow.org/api_docs/python/tf/nn/sigmoid_cross_entropy_with_logits) implements this in a numerically stable manner."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 13,
    "metadata": {
     "lines_to_next_cell": 2
    },
    "outputs": [],
    "source": [
-    "def compile_srgan_model(\n",
-    "    g_network: keras.engine.network.Network,\n",
-    "    d_network: keras.engine.network.Network,\n",
-    "    metrics: typing.Dict[str, str] = None,\n",
-    ") -> typing.Dict[str, keras.engine.training.Model]:\n",
+    "def calculate_generator_loss(\n",
+    "    y_pred: chainer.variable.Variable,\n",
+    "    y_true: cupy.ndarray,\n",
+    "    pred_labels: cupy.ndarray,\n",
+    "    true_labels: cupy.ndarray,\n",
+    ") -> chainer.variable.Variable:\n",
     "    \"\"\"\n",
-    "    Creates a Super Resolution Generative Adversarial Network (SRGAN)\n",
-    "    by joining a generator network with a discriminator network.\n",
-    "\n",
-    "    Returns a dictionary containing:\n",
-    "    1) generator model (trainable, not compiled)\n",
-    "    2) discriminator model (trainable, compiled)\n",
-    "    3) srgan model (trainable generator, untrainable discriminator, compiled)\n",
-    "\n",
-    "    The SRGAN model will be compiled with an optimizer (e.g. Adam)\n",
-    "    and have separate loss functions and metrics for its\n",
-    "    generator and discriminator component.\n",
-    "\n",
-    "    >>> metrics = {\"generator_network\": 'mse', \"discriminator_network\": 'accuracy'}\n",
-    "    >>> models = compile_srgan_model(\n",
-    "    ...     g_network=generator_network(),\n",
-    "    ...     d_network=discriminator_network(),\n",
-    "    ...     metrics=metrics,\n",
+    "    Calculate the batchwise loss of the Generator Network.\n",
+    "\n",
+    "    >>> calculate_generator_loss(\n",
+    "    ...     y_pred=chainer.variable.Variable(data=np.ones(shape=(2, 1, 3, 3))),\n",
+    "    ...     y_true=np.full(shape=(2, 1, 3, 3), fill_value=10.0),\n",
+    "    ...     pred_labels=np.zeros(shape=(2, 1, 3, 3)),\n",
+    "    ...     true_labels=np.ones(shape=(2, 1, 3, 3)).astype(np.int32),\n",
     "    ... )\n",
-    "    >>> models['discriminator_model'].trainable\n",
-    "    True\n",
-    "    >>> models['srgan_model'].get_layer(name='generator_network').trainable\n",
-    "    True\n",
-    "    >>> models['srgan_model'].get_layer(name='discriminator_network').trainable\n",
-    "    False\n",
-    "    >>> models['srgan_model'].count_params()\n",
-    "    8432962\n",
+    "    variable(9.69314718)\n",
     "    \"\"\"\n",
+    "    # Content Loss (L1, Mean Absolute Error)\n",
+    "    content_loss = F.mean_absolute_error(x0=y_pred, x1=y_true)\n",
     "\n",
-    "    # Check that our neural networks are named properly\n",
-    "    assert g_network.name == \"generator_network\"\n",
-    "    assert d_network.name == \"discriminator_network\"\n",
-    "    assert g_network.trainable == True  # check that generator is trainable\n",
-    "    assert d_network.trainable == True  # check that discriminator is trainable\n",
+    "    # Adversarial Loss\n",
+    "    adversarial_loss = F.sigmoid_cross_entropy(x=pred_labels, t=true_labels)\n",
     "\n",
-    "    ## Both trainable\n",
-    "    # Create keras models (trainable) out of the networks (graph only)\n",
-    "    g_model = Model(\n",
-    "        inputs=g_network.inputs, outputs=g_network.outputs, name=\"generator_model\"\n",
-    "    )\n",
-    "    d_model = Model(\n",
-    "        inputs=d_network.inputs, outputs=d_network.outputs, name=\"discriminator_model\"\n",
-    "    )\n",
-    "    d_model.compile(\n",
-    "        optimizer=keras.optimizers.Adam(lr=0.001),\n",
-    "        loss={\"discriminator_output\": keras.losses.binary_crossentropy},\n",
-    "    )\n",
+    "    # Get generator loss\n",
+    "    g_loss = (1 * content_loss) + (1 * adversarial_loss)\n",
+    "    g_loss\n",
+    "    return g_loss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "def psnr(\n",
+    "    y_true: cupy.ndarray, y_pred: cupy.ndarray, data_range=2 ** 32\n",
+    ") -> cupy.ndarray:\n",
+    "    \"\"\"\n",
+    "    Peak Signal-Noise Ratio (PSNR) metric, calculated batchwise.\n",
+    "    See https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio#Definition\n",
     "\n",
-    "    ## One trainable (generator), one untrainable (discriminator)\n",
-    "    # Connect Generator Network to Discriminator Network\n",
-    "    g_out = g_network(inputs=g_network.inputs)  # g_in --(g_network)--> g_out\n",
-    "    d_out = d_network(inputs=g_out)  # g_out --(d_network)--> d_out\n",
-    "\n",
-    "    # Create and Compile the Super Resolution Generative Adversarial Network Model!\n",
-    "    model = Model(inputs=g_network.inputs, outputs=[g_out, d_out])\n",
-    "    model.get_layer(\n",
-    "        name=\"discriminator_network\"\n",
-    "    ).trainable = False  # combined model should not train discriminator\n",
-    "    model.compile(\n",
-    "        optimizer=keras.optimizers.Adam(lr=0.001),\n",
-    "        loss={\n",
-    "            \"generator_network\": keras.losses.mean_absolute_error,\n",
-    "            \"discriminator_network\": keras.losses.binary_crossentropy,\n",
-    "        },\n",
-    "        metrics=metrics,\n",
-    "    )\n",
+    "    Can take in either numpy (CPU) or cupy (GPU) arrays as input.\n",
+    "    Implementation is same as skimage.measure.compare_psnr with data_range=2**32\n",
     "\n",
-    "    return {\n",
-    "        \"generator_model\": g_model,\n",
-    "        \"discriminator_model\": d_model,\n",
-    "        \"srgan_model\": model,\n",
-    "    }"
+    "    >>> psnr(\n",
+    "    ...     y_true=np.ones(shape=(2, 1, 3, 3)),\n",
+    "    ...     y_pred=np.full(shape=(2, 1, 3, 3), fill_value=2),\n",
+    "    ... )\n",
+    "    192.65919722494797\n",
+    "    \"\"\"\n",
+    "    xp = chainer.backend.get_array_module(y_true)\n",
+    "\n",
+    "    # Calculate Mean Squred Error along predetermined axes\n",
+    "    mse = xp.mean(xp.square(xp.subtract(y_pred, y_true)), axis=None)\n",
+    "\n",
+    "    # Calculate Peak Signal-Noise Ratio, setting MAX_I as 2^32, i.e. max for int32\n",
+    "    return xp.multiply(20, xp.log10(data_range / xp.sqrt(mse)))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 15,
    "metadata": {
     "lines_to_next_cell": 2
    },
    "outputs": [],
    "source": [
-    "def psnr(y_true: np.ndarray, y_pred: np.ndarray) -> np.ndarray:\n",
+    "def calculate_discriminator_loss(\n",
+    "    y_pred: chainer.variable.Variable, y_true: cupy.ndarray\n",
+    ") -> chainer.variable.Variable:\n",
     "    \"\"\"\n",
-    "    Peak Signal-Noise Ratio (PSNR) metric.\n",
-    "    See https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio#Definition\n",
+    "    Calculate the batchwise loss of the Discriminator Network.\n",
+    "\n",
+    "    Original formula:\n",
+    "    -(y * np.log(sigmoid(x)) + (1 - y) * np.log(1 - sigmoid(x)))\n",
     "\n",
-    "    >>> y_true, y_pred = np.ones(shape=(3, 3)), np.full(shape=(3, 3), fill_value=2)\n",
-    "    >>> K.eval(psnr(y_true=y_true, y_pred=y_pred))\n",
-    "    array([221.80709678, 221.80709678, 221.80709678])\n",
+    "    Numerically stable formula:\n",
+    "    -(x * (y - (x >= 0)) - np.log1p(np.exp(-np.abs(x))))\n",
+    "\n",
+    "    >>> calculate_discriminator_loss(\n",
+    "    ...     y_pred=chainer.variable.Variable(data=np.array([[0.5], [1.5], [-0.5]])),\n",
+    "    ...     y_true=np.array([[0], [1], [0]]),\n",
+    "    ... )\n",
+    "    variable(0.54985575)\n",
     "    \"\"\"\n",
     "\n",
-    "    mse = (\n",
-    "        K.mean(K.square(K.np.subtract(y_pred, y_true)), axis=-1) + K.epsilon()\n",
-    "    )  # add epsilon to prevent zero division\n",
-    "    return K.np.multiply(\n",
-    "        20, K.log(2 ** 16 / K.sqrt(mse))\n",
-    "    )  # setting MAX_I as 2^16, i.e. max for int16"
+    "    # Binary Cross-Entropy Loss\n",
+    "    bce_loss = F.sigmoid_cross_entropy(x=y_pred, t=y_true)\n",
+    "\n",
+    "    # Get discriminator loss\n",
+    "    d_loss = bce_loss\n",
+    "\n",
+    "    return d_loss"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "__________________________________________________________________________________________________\n",
-      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
-      "==================================================================================================\n",
-      "input_1 (InputLayer)            (None, 10, 10, 1)    0                                            \n",
-      "__________________________________________________________________________________________________\n",
-      "input_2 (InputLayer)            (None, 100, 100, 1)  0                                            \n",
-      "__________________________________________________________________________________________________\n",
-      "input_3 (InputLayer)            (None, 20, 20, 1)    0                                            \n",
-      "__________________________________________________________________________________________________\n",
-      "generator_network (Network)     (None, 32, 32, 1)    1604929     input_1[0][0]                    \n",
-      "                                                                 input_2[0][0]                    \n",
-      "                                                                 input_3[0][0]                    \n",
-      "__________________________________________________________________________________________________\n",
-      "discriminator_network (Network) (None, 1)            6828033     generator_network[1][0]          \n",
-      "==================================================================================================\n",
-      "Total params: 8,432,962\n",
-      "Trainable params: 1,604,929\n",
-      "Non-trainable params: 6,828,033\n",
-      "__________________________________________________________________________________________________\n"
-     ]
-    }
-   ],
+   "outputs": [],
+   "source": [
+    "# Build the models\n",
+    "generator_model = GeneratorModel(\n",
+    "    inblock_class=DeepbedmapInputBlock,\n",
+    "    resblock_class=ResidualBlock,\n",
+    "    num_residual_blocks=16,\n",
+    ")\n",
+    "discriminator_model = DiscriminatorModel()\n",
+    "\n",
+    "# Transfer models to GPU if available\n",
+    "if xp == cupy:  # Check if CuPy was loaded, i.e. GPU is available\n",
+    "    generator_model.to_gpu(device=0)\n",
+    "    discriminator_model.to_gpu(device=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "K.clear_session()  # Reset Keras/Tensorflow graph\n",
-    "metrics = {\"generator_network\": psnr, \"discriminator_network\": \"accuracy\"}\n",
-    "models = compile_srgan_model(\n",
-    "    g_network=generator_network(), d_network=discriminator_network(), metrics=metrics\n",
+    "# Setup optimizer, using Adam\n",
+    "generator_optimizer = chainer.optimizers.Adam(alpha=0.001, eps=1e-7).setup(\n",
+    "    link=generator_model\n",
     ")\n",
-    "models[\"srgan_model\"].summary()"
+    "discriminator_optimizer = chainer.optimizers.Adam(alpha=0.001, eps=1e-7).setup(\n",
+    "    link=discriminator_model\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 3. Train model\n",
+    "# 3. Train model\n",
     "\n",
     "[Gherkin](https://en.wikipedia.org/wiki/Gherkin_(language))/Plain English statement at what the Super-Resolution Generative Adversarial Network below does\n",
     "\n",
@@ -667,30 +979,32 @@
     "      Scenario: Train discriminator to beat generator\n",
     "        Given fake generated images from a generator\n",
     "          And real groundtruth images\n",
-    "         When the two sets of images are fed into the discriminator\n",
-    "         Then the discriminator should know the fakes from the real images\n",
+    "         When the two sets of images are fed into the discriminator for comparison\n",
+    "         Then the discriminator should learn to know the fakes from the real images\n",
     "\n",
     "      Scenario: Train generator to fool discriminator\n",
-    "        Given what we think the discriminator believes is real\n",
-    "         When our inputs are fed into the super resolution model\n",
-    "         Then the generator should create a more authentic looking image\n",
+    "        Given fake generated images from a generator\n",
+    "          And what we think the discriminator believes is real\n",
+    "         When we compare the fake images to the real ones\n",
+    "         Then the generator should learn to create a more authentic looking image\n",
     "```"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 18,
    "metadata": {
     "lines_to_next_cell": 2
    },
    "outputs": [],
    "source": [
-    "def train_discriminator(\n",
-    "    models: typing.Dict[str, keras.engine.training.Model],\n",
-    "    generator_inputs: typing.List[np.ndarray],\n",
-    "    groundtruth_images: np.ndarray,\n",
-    "    verbose: int = 1,\n",
-    ") -> (typing.Dict[str, keras.engine.training.Model], list):\n",
+    "def train_eval_discriminator(\n",
+    "    input_arrays: typing.Dict[str, cupy.ndarray],\n",
+    "    g_model,\n",
+    "    d_model,\n",
+    "    d_optimizer=None,\n",
+    "    train: bool = True,\n",
+    ") -> (float, float):\n",
     "    \"\"\"\n",
     "    Trains the Discriminator within a Super Resolution Generative Adversarial Network.\n",
     "    Discriminator is trainable, Generator is not trained (only produces predictions).\n",
@@ -700,137 +1014,170 @@
     "    - Fake images combined with real groundtruth images\n",
     "    - Discriminator trained with these images and their Fake(0)/Real(1) labels\n",
     "\n",
-    "    >>> generator_inputs = [\n",
-    "    ...     np.random.RandomState(seed=42).rand(32, s, s, 1) for s in [10, 100, 20]\n",
-    "    ... ]\n",
-    "    >>> groundtruth_images = np.random.RandomState(seed=42).rand(32,32,32,1)\n",
-    "    >>> models = compile_srgan_model(\n",
-    "    ...     g_network=generator_network(), d_network=discriminator_network()\n",
+    "    >>> train_arrays = {\n",
+    "    ...     \"X\": np.random.RandomState(seed=42).rand(2, 1, 10, 10).astype(np.float32),\n",
+    "    ...     \"W1\": np.random.RandomState(seed=42).rand(2, 1, 100, 100).astype(np.float32),\n",
+    "    ...     \"W2\": np.random.RandomState(seed=42).rand(2, 1, 20, 20).astype(np.float32),\n",
+    "    ...     \"Y\": np.random.RandomState(seed=42).rand(2, 1, 32, 32).astype(np.float32),\n",
+    "    ... }\n",
+    "    >>> discriminator_model = DiscriminatorModel()\n",
+    "    >>> discriminator_optimizer = chainer.optimizers.Adam(alpha=0.001, eps=1e-7).setup(\n",
+    "    ...     link=discriminator_model\n",
     "    ... )\n",
-    "\n",
-    "    >>> d_weight0 = K.eval(models['discriminator_model'].weights[0][0,0,0,0])\n",
-    "    >>> _, _ = train_discriminator(\n",
-    "    ...     models=models,\n",
-    "    ...     generator_inputs=generator_inputs,\n",
-    "    ...     groundtruth_images=groundtruth_images,\n",
-    "    ...     verbose=0,\n",
+    "    >>> generator_model = GeneratorModel(\n",
+    "    ...     inblock_class=DeepbedmapInputBlock,\n",
+    "    ...     resblock_class=ResidualBlock,\n",
+    "    ...     num_residual_blocks=1,\n",
     "    ... )\n",
-    "    >>> d_weight1 = K.eval(models['discriminator_model'].weights[0][0,0,0,0])\n",
     "\n",
+    "    >>> d_weight0 = [d for d in discriminator_model.params()][-1][0].array\n",
+    "    >>> d_train_loss, d_train_accu = train_eval_discriminator(\n",
+    "    ...     input_arrays=train_arrays,\n",
+    "    ...     g_model=generator_model,\n",
+    "    ...     d_model=discriminator_model,\n",
+    "    ...     d_optimizer=discriminator_optimizer,\n",
+    "    ... )\n",
+    "    >>> d_weight1 = [d for d in discriminator_model.params()][-1][0].array\n",
     "    >>> d_weight0 != d_weight1  #check that training has occurred (i.e. weights changed)\n",
     "    True\n",
     "    \"\"\"\n",
-    "\n",
-    "    # hardcoded check that we are passing in 3 numpy arrays as input\n",
-    "    assert len(generator_inputs) == 3\n",
-    "    # check that X_data and W1_data have same length (batch size)\n",
-    "    assert generator_inputs[0].shape[0] == generator_inputs[1].shape[0]\n",
-    "    # check that X_data and W2_data have same length (batch size)\n",
-    "    assert generator_inputs[0].shape[0] == generator_inputs[2].shape[0]\n",
-    "\n",
     "    # @pytest.fixture\n",
-    "    g_model = models[\"generator_model\"]\n",
-    "    d_model = models[\"discriminator_model\"]\n",
+    "    if train == True:\n",
+    "        assert d_optimizer is not None  # Optimizer required for neural network training\n",
+    "    xp = chainer.backend.get_array_module(input_arrays[\"Y\"])\n",
     "\n",
     "    # @given(\"fake generated images from a generator\")\n",
-    "    fake_images = g_model.predict(x=generator_inputs, batch_size=32)\n",
-    "    fake_labels = np.zeros(shape=len(generator_inputs[0]))\n",
+    "    generator_inputs = {\n",
+    "        \"x\": input_arrays[\"X\"],\n",
+    "        \"w1\": input_arrays[\"W1\"],\n",
+    "        \"w2\": input_arrays[\"W2\"],\n",
+    "    }\n",
+    "    fake_images = g_model.forward(inputs=generator_inputs).array\n",
+    "    fake_labels = xp.zeros(shape=(len(fake_images), 1)).astype(xp.int32)\n",
     "\n",
     "    # @given(\"real groundtruth images\")\n",
-    "    real_images = groundtruth_images  # groundtruth images i.e. Y_data\n",
-    "    real_labels = np.ones(shape=len(groundtruth_images))\n",
+    "    real_images = input_arrays[\"Y\"]\n",
+    "    real_labels = xp.ones(shape=(len(real_images), 1)).astype(xp.int32)\n",
+    "\n",
+    "    # @when(\"the two sets of images are fed into the discriminator for comparison\")\n",
+    "    images = xp.concatenate([fake_images, real_images])\n",
+    "    labels = xp.concatenate([fake_labels, real_labels])\n",
+    "    y_pred = d_model.forward(inputs={\"x\": images})\n",
     "\n",
-    "    # @when(\"the two sets of images are fed into the discriminator\")\n",
-    "    images = np.concatenate([fake_images, real_images])\n",
-    "    labels = np.concatenate([fake_labels, real_labels])\n",
-    "    assert d_model.trainable == True\n",
-    "    d_metrics = d_model.fit(\n",
-    "        x=images, y=labels, epochs=1, batch_size=32, shuffle=True, verbose=verbose\n",
-    "    ).history\n",
+    "    d_loss = calculate_discriminator_loss(y_pred=y_pred, y_true=labels)\n",
+    "    d_accu = F.binary_accuracy(y=y_pred, t=labels)\n",
     "\n",
-    "    # @then(\"the discriminator should know the fakes from the real images\")\n",
-    "    # assert d_weight0 != d_weight1  # check that training occurred i.e. weights changed\n",
+    "    # @then(\"the discriminator should learn to know the fakes from the real images\")\n",
+    "    if train == True:\n",
+    "        d_model.cleargrads()  # clear/zero all gradients\n",
+    "        d_loss.backward()  # renew gradients\n",
+    "        d_optimizer.update()  # backpropagate the loss using optimizer\n",
     "\n",
-    "    return models, d_metrics[\"loss\"][0]"
+    "    return float(d_loss.array), float(d_accu.array)  # return discriminator metrics"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 19,
    "metadata": {
     "lines_to_next_cell": 2
    },
    "outputs": [],
    "source": [
-    "def train_generator(\n",
-    "    models: typing.Dict[str, keras.engine.training.Model],\n",
-    "    generator_inputs: typing.List[np.ndarray],\n",
-    "    groundtruth_images: np.ndarray,\n",
-    "    verbose: int = 1,\n",
-    ") -> (typing.Dict[str, keras.engine.training.Model], list):\n",
+    "def train_eval_generator(\n",
+    "    input_arrays: typing.Dict[str, cupy.ndarray],\n",
+    "    g_model,\n",
+    "    d_model,\n",
+    "    g_optimizer=None,\n",
+    "    train: bool = True,\n",
+    ") -> (float, float):\n",
     "    \"\"\"\n",
-    "    Trains the Generator within a Super Resolution Generative Adversarial Network.\n",
+    "    Evaluates and/or trains the Generator for one minibatch\n",
+    "    within a Super Resolution Generative Adversarial Network.\n",
     "    Discriminator is not trainable, Generator is trained.\n",
     "\n",
+    "    If train is set to False, only forward pass is run, i.e. evaluation/prediction only\n",
+    "    If train is set to True, forward and backward pass are run, i.e. train with backprop\n",
+    "\n",
     "    Steps:\n",
-    "    - Labels of the SRGAN output are set to Real(1)\n",
-    "    - Generator is trained to match these Real(1) labels\n",
-    "\n",
-    "    >>> generator_inputs = [\n",
-    "    ...     np.random.RandomState(seed=42).rand(32, s, s, 1) for s in [10, 100, 20]\n",
-    "    ... ]\n",
-    "    >>> groundtruth_images = np.random.RandomState(seed=42).rand(32,32,32,1)\n",
-    "    >>> models = compile_srgan_model(\n",
-    "    ...     g_network=generator_network(), d_network=discriminator_network()\n",
+    "    - Generator produces fake images\n",
+    "    - Fake images compared with real groundtruth images\n",
+    "    - Generator is trained to be more like real image\n",
+    "\n",
+    "    >>> train_arrays = {\n",
+    "    ...     \"X\": np.random.RandomState(seed=42).rand(2, 1, 10, 10).astype(np.float32),\n",
+    "    ...     \"W1\": np.random.RandomState(seed=42).rand(2, 1, 100, 100).astype(np.float32),\n",
+    "    ...     \"W2\": np.random.RandomState(seed=42).rand(2, 1, 20, 20).astype(np.float32),\n",
+    "    ...     \"Y\": np.random.RandomState(seed=42).rand(2, 1, 32, 32).astype(np.float32),\n",
+    "    ... }\n",
+    "    >>> generator_model = GeneratorModel(\n",
+    "    ...     inblock_class=DeepbedmapInputBlock,\n",
+    "    ...     resblock_class=ResidualBlock,\n",
+    "    ...     num_residual_blocks=1,\n",
     "    ... )\n",
-    "\n",
-    "    >>> g_weight0 = K.eval(models['generator_model'].weights[0][0,0,0,0])\n",
-    "    >>> _, _ = train_generator(\n",
-    "    ...     models=models,\n",
-    "    ...     generator_inputs=generator_inputs,\n",
-    "    ...     groundtruth_images=groundtruth_images,\n",
-    "    ...     verbose=0,\n",
+    "    >>> generator_optimizer = chainer.optimizers.Adam(alpha=0.001, eps=1e-7).setup(\n",
+    "    ...     link=generator_model\n",
     "    ... )\n",
-    "    >>> g_weight1 = K.eval(models['generator_model'].weights[0][0,0,0,0])\n",
-    "\n",
+    "    >>> discriminator_model = DiscriminatorModel()\n",
+    "\n",
+    "    >>> g_weight0 = [g for g in generator_model.params()][0][0, 0, 0, 0].array\n",
+    "    >>> _ = train_eval_generator(\n",
+    "    ...     input_arrays=train_arrays,\n",
+    "    ...     g_model=generator_model,\n",
+    "    ...     d_model=discriminator_model,\n",
+    "    ...     g_optimizer=generator_optimizer,\n",
+    "    ... )\n",
+    "    >>> g_weight1 = [g for g in generator_model.params()][0][0, 0, 0, 0].array\n",
     "    >>> g_weight0 != g_weight1  #check that training has occurred (i.e. weights changed)\n",
     "    True\n",
     "    \"\"\"\n",
     "\n",
     "    # @pytest.fixture\n",
-    "    srgan_model = models[\"srgan_model\"]\n",
+    "    if train == True:\n",
+    "        assert g_optimizer is not None  # Optimizer required for neural network training\n",
+    "    xp = chainer.backend.get_array_module(input_arrays[\"Y\"])\n",
+    "\n",
+    "    # @given(\"fake generated images from a generator\")\n",
+    "    generator_inputs = {\n",
+    "        \"x\": input_arrays[\"X\"],\n",
+    "        \"w1\": input_arrays[\"W1\"],\n",
+    "        \"w2\": input_arrays[\"W2\"],\n",
+    "    }\n",
+    "    y_pred = g_model.forward(inputs=generator_inputs)\n",
+    "    predicted_labels = d_model.forward(inputs={\"x\": y_pred}).array\n",
     "\n",
     "    # @given(\"what we think the discriminator believes is real\")\n",
-    "    true_labels = np.ones(shape=len(generator_inputs[0]))\n",
-    "\n",
-    "    # @when(\"our inputs are fed into the super resolution model\")\n",
-    "    assert srgan_model.get_layer(name=\"discriminator_network\").trainable == False\n",
-    "    g_metrics = srgan_model.fit(\n",
-    "        x=generator_inputs,\n",
-    "        y={\n",
-    "            \"generator_network\": groundtruth_images,\n",
-    "            \"discriminator_network\": true_labels,\n",
-    "        },\n",
-    "        batch_size=32,\n",
-    "        verbose=verbose,\n",
-    "    ).history\n",
-    "\n",
-    "    # @then(\"the generator should create a more authentic looking image\")\n",
-    "    # assert g_weight0 != g_weight1  # check that training occurred i.e. weights changed\n",
-    "\n",
-    "    return models, [m[0] for m in g_metrics.values()]"
+    "    groundtruth_images = input_arrays[\"Y\"]\n",
+    "    groundtruth_labels = xp.ones(shape=(len(groundtruth_images), 1)).astype(xp.int32)\n",
+    "\n",
+    "    # @when(\"we compare the fake images to the real ones\")\n",
+    "    g_loss = calculate_generator_loss(\n",
+    "        y_pred=y_pred,\n",
+    "        y_true=groundtruth_images,\n",
+    "        pred_labels=predicted_labels,\n",
+    "        true_labels=groundtruth_labels,\n",
+    "    )\n",
+    "    g_psnr = psnr(y_pred=y_pred.array, y_true=groundtruth_images)\n",
+    "\n",
+    "    # @then(\"the generator should learn to create a more authentic looking image\")\n",
+    "    if train == True:\n",
+    "        g_model.cleargrads()  # clear/zero all gradients\n",
+    "        g_loss.backward()  # renew gradients\n",
+    "        g_optimizer.update()  # backpropagate the loss using optimizer\n",
+    "\n",
+    "    return float(g_loss.array), float(g_psnr)  # return generator loss and metric values"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAABeAAAAFZCAYAAADwyI+qAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzs3Xd4VFX6wPHvSTLpvUB6Qgg19F6kqKiIKKKgYsFe1l4WV1dde1t1fzYsq6uIBVARBRtIk957AiEBUgkppPfMzPn9cSekFyBAhPfzPDyTuffce89Mwpk7733ve5TWGiGEEEIIIYQQQgghhBBCtC27M90BIYQQQgghhBBCCCGEEOJsJAF4IYQQQgghhBBCCCGEEOIUkAC8EEIIIYQQQgghhBBCCHEKSABeCCGEEEIIIYQQQgghhDgFJAAvhBBCCCGEEEIIIYQQQpwCEoAXQgghhBBCCCGEEEIIIU4BCcALIYQQQgghhBCAUuoWpdSaM90PIYQQQpw9JAAv2oRSapZS6iWl1CilVPwp2H+xUirqBLeNVUqNbeMutSk50RdCtDcyrgshhGiv5NxZCCHOPjK2i7OZBOBFm9Jar9ZadzsF+3XXWh88wW1jtNYrT7YP8mEghDgXybguhBDidFJKOZwNxxBCCFHjXBjblUHirKJR8och2rUzPYC2pbPptQghxIk6m8bCs+m1CCFENaXUAKXUdqVUkVLqO6XUPKXUS7Z1E5VSO5RS+UqpdUqpPrW2S1JK/V0ptUspVWDbzrnW+pa2/YdSahdQopRyUEo9oZQ6YOtHnFJqsq1tD+AjYLjtbqp823IvpdRspVS2UipZKfV0dSDEdsF1rVLq/5RSR4HnjuP9GKGU2mx7TZuVUiNqrbtFKXXQ1sdDSqkbbMujlVJ/2rbJUUrNO6FfhhBCtJFzfWyv1fZ92+vYp5S6sN76xsbzW5RSa5RSbyql8mzrLq213Uql1MtKqbVAKXBCd/iKs58E4MUJUUr1V0ptsw1O8wBn2/KxSqm0Wu3+oZRKt7WLrx7glFL2Sql/1hp4tyqlwmzrtFLqPqVUApBQa1m07edZSqkPlFK/2QbmtUqpQKXU27YBcZ9Sqn+tPiQppcbZfn5OKfWtbQAvUkYZg0G12sqJvhDinCTjepuf5HdWSi1XSh21jclfK6W8a60PU0r9YDvOUaXU+7XW3amU2lurzwPqv2e13reXju83LYQQTVNKOQILgFmALzAHqB43+wOfAXcDfsDHwEKllFOtXVwDjAc6AX2AW45j22nAZYC31toMHABGAV7A88BXSqkgrfVe4B5gve1uquqx9T1b2yhgDDAduLXW/ocCB4GOwMutfD98gV+Ad239/g/wi1LKTynlZlt+qdbaAxgB7LBt+iKwBPABQm19E0KIM0LG9jptDwD+wLPAD0op3xbG8+rt4m3b/Rv4n1JK1Vp/E3AX4AEkt9AHcY6SALw4brbB+0fgS4zB+zvg6kbadQPuBwbbBrFLgCTb6kcxBuIJgCdwG8bVwmpXYgxyPZvoxjXA0xgDYAWwHthme/49xslxU64A5gLewELg/Vrr5ERfCHHOkXH9lIzrCngVCAZ6AGHYAvZKKXvgZ4wT9EggxNZ/lFJTbe2mY7yPVwBHmzmOEEK0pWGAA/Cu1rpKa/0DsMm27i7gY631Rq21RWv9BcZ4PazW9u9qrQ9rrXOBRUC/49w2VWtdBqC1/s62L6vWeh7GBdwhjXXaNq5eBzyptS7SWicBb2EERaod1lq/p7U2Vx+jFS4DErTWX9q2mwPsAy63rbcCvZRSLlrrDK11rG15FRABBGuty7XWUu5MCHEmydhuyALetr0H8zCC6pfZ1jU1ngMka60/0VpbgC+AIIzvAtVmaa1jbX2oaqEP4hwlAXhxIoYBJmoGru+BzY20swBOQE+llElrnaS1PmBbdwfwtNY6Xht2aq1rBxhe1VrnNjOALtBab9Val2NcyS3XWs+2DYjzgP5NbAewRmv9q63tl0Df6hVyoi+EOEfJuE7bjuta60St9R9a6wqtdTbGBYQxttVDMALzM7TWJfXG7DuAf2utN9vex0SttWTSCCFOl2AgXWutay1LtT1GAI8po8xAvu3OoTDbNtWO1Pq5FHA/jm1Ta/2MUmq6qilrkA/0wrgo2xh/jM+x2uNlMsYFzkb330rBNMxmTAZCtNYlwLUYF3MzlFK/KKW629o8jnEhdpPtzqzbTuDYQgjRVmRsN9R/D5Ix4ifNjedQ6/VrrasTjNxrrT+RzxdxjpEAvDgRjQ3eDYIDWutE4GGMTL4spdRcpVT1QByGkZXYlJYGsMxaP5c18tydptX/8HBWtjq+cqIvhDhHybhuaLNxXSnV0fb+pCulCoGvah03DCOTxtzIpi29j0IIcSplACH1bq0Psz2mAi9rrb1r/XO1JYu0pDXbHvsMUkpFAJ9g3HXlp427lPZgnOvWaWuTQ00ySrVwIL2x/R+Hw/X2WWe/WuvFWuuLMLIh99n6jNb6iNb6Tq11MEZphg9qlxATQojTTMZ2Q/33IBxjnG9yPG+lE/l8EecYCcCLE9HY4B3eWEOt9Tda6/MwBkwNvG5blQp0buYYp30AawcfBtXkRF8IcbrJuG5oy3H9FVvb3lprT+DGWsdNBcJV45O4Nvc+lgKutZ4HtrIvQgjRWusx7na6XxmT5U2i5q6hT4B7lFJDlcFNKXWZUsqjFfs93m3dMMbQbACl1K0YF1CrZQKhthJq2O6A+hZ4WSnlYRv/H8W4+HkyfgW6KqWut70f12KUUvvZdqF1kq1EZAVQjHGnKkqpqUqpUNs+8myvxXqSfRFCiBMlY7uhA/CgUspkK/vYA/i1ufFciLYiAXhxItYDZmoGrqto5HZ+pVQ3pdQFypiAoxwjg7F6EPsUeFEp1cU2UPdRSvmdrhfQhDP9YVBNTvSFEKebjOu0+bjugTFGFyilQoAZtdZtwrjo8Zrti4qzUmqkbd2nwN+VUgNt72O0rR9gzPlxvTImvB1PTUkbIYRoE1rrSuAq4HYgH+Pi4c9AhdZ6C3AnxjwbeUAiton4WrHf49pWax2HUQJsPcZY3RtYW6vJciAWOKKUyrEtewAowZinYw3wDcbkgCfMVkptIvAYxnwcjwMTtdY5GN+lH8VInsnFGJP/Ztt0MLBRKVWMMTfJQ1rrgyfTFyGEOFEyth+zEeiCkXTzMjDFNs43N54L0SYay7wSolla60pbcOYT4CWMgPEPjTR1Al7DuKpYBazDmKQDjFq4ThiThvpjZHJPPrU9b57WOk4pVf1hYAVm0/SHgVVr7Y/xYfAexodBOcZ7ctIn+kqpicA7wIcYH2ITtdY5SqkgjA+G2RhBpR3UPdF/WynlhfFhJif6QohWkXH9lIzrz9uOV4Axjn8JPGLrl0UpdTnGpNopGOP5N8BarfV3tgsX32CUvknCqEGfDDyEMfHTfRiT5v54Av0SQohm2QIq1RPsoZTaiDHpHlrr34Hfm9gust7z5+o9b/W2tmVPAU810b6SmonzqpflYQSVGms/C5jV2LqW2trm6BjYSLsMmrgQqrV+HCNYL4QQ7cK5PrbXbKLvx7g7tvbC5sbzBsfQWqtaP489juOLc5iqW+5VCCGEEEIIIcS5Sik1BojHyBC8AfgIiLIFKIQQQvwFnetju1LqFuAOWylNIU47KUEjhBBCCCGEEKJaN2AnRpmCxzBu0T+rAjRKqY+UUsWN/PvoTPdNCCFOERnbhTiDJANeiNPENug3duvUV1rre053f4QQQpwcGdeFEEIIIYQQQrREAvBCCCGEEEIIIYQQQgghxCkgJWiEEEIIIYQQQgghhBBCiFPA4Uwd2N/fX0dGRp6pwwshRLu3devWHK11wJnux/GS8V0IIZon47sQQpydZHwXQoiz08mO72csAB8ZGcmWLVvO1OGFEKLdU0oln+k+nAgZ34UQonkyvgshxNlJxnchhDg7nez4LiVohBBCCCGEEEIIIYQQQohTQALwQgghhBBCCCGEEEIIIcQpIAF4IYQQQgghhBBCCCGEEOIUkAC8EEIIIYQQQgghhBBCCHEKSABeCCGEEEIIIYQQQgghhDgFJAAvhBBCCCGEEEIIIYQQQpwCEoAXQgghhBBCCCGEEEIIIU4BCcALIcQ5Sin1mVIqSym1p4n1Sin1rlIqUSm1Syk14HT3UQghhBBCCCGEEOKvTALwQghx7poFjG9m/aVAF9u/u4APT0OfhBBCCCGEEEIIIc4aDme6A80qzgZzGXiHn+meCCHEWUdrvUopFdlMk0nAbK21BjYopbyVUkFa64zT0kFx+hRnQ2UR+Ea10C4LKorAr3Pz7YqOQFkedOjRdn1sSwXpkLYZgLzSKpKOlmD8mYMCPFxM+Lk54uXiiF1gDFbfaIorzVgsGh83R2MfaVuM98HFp+6+K4ohZz+EnPwNI1prDmXkkLN/A8UdB+FkMuHm5ECfEC/s7NRx7ae4wkxWUQU5R4+ij+yhtONAnEwOODnYUWXRVJgtlFdZcTLZEeDuRAdPJ/zcnDh2mLQtqIBu4Ox50q/rlMtLhsPbjz2tcvajoMNgCsrNlFdZ6B7oib2dgqx9kL3PaGPVHCkox93JHg9nEw4mR4geBw5OzR8rdTMUpgOggcrgwVS4dKDC9l56OptAa0heByXZp+oVnzYFZVWYLVYc7CWHRwghhBBC/PUkZBbh7+5U873uNGnfAfjfn4C8JLhz2ZnuiRBCnItCgNRaz9NsyxoE4JVSd2FkyRMeLhdN/3J+m2EECB+JA/tmTg2+v80IWD4S23RgsjgbPr0IClKg/40w7nlw8z/hrpVUmJm9Ppl1B3LIKqwgq6icKovm2sFh3D06ig6eznXaa63ZlpLPt5tT2XO4gI9uHEiYr2udNuXzbsP58AYAfGz/mlKJA89U3co8y/kAdPFz4gXnbxh+dD5WFz/sLnoO+t0ISkHsAlj8FBQdhtuXQtjg43696fllLN5zhHUHcjiStI83LP9miF0KW6xd+VfVLcTpSKYNCePVq/q0uK/ErCJ+3H6YhTsPk5JbwiS7tfzT9A0dVT7fmsfwjPlWKmj5xHOq/UreMP2XAntfysY+S+B5Nxuv9yRVmC2sjM9m4c7DJGQWUVhmprC8ivIqCx7OJjxdHPB1deSlK3vTO9SrdTvNSYRPL4DygmOLTMA+SwzPmm/hgA5hZIg9MwN/wTv2S4ywudEmrN6ufg5+kG6THqdLR4/Gj7V/CXwz9dhTBeRqXyZVvEgWPtjbKS7o3oEZnsvouuOV1r4t7VpKbinlZivuEoAXQgghhBAnYe6mFCL83Bje2e+0HbOgrIorZ66ld6gXc+4chmqD7zSt1b4D8MWZRradEEKIdk1r/V/gvwCDBg3SZ7g74nilbDQ+c5PXQtSYxttkxkLSauPn2B+h77UN25grYN4NUJIFA26GHV/D3kUw6jHwCjXaKHuIvhCcmghq2lQcWs/arTtYEptJcYUZL/8uOPvHMLiTD4VlZmatS+LLDclMGxxGJ3838kqrsOanUXZwHUcKynF0sCMQdx6ca8e3dw/HZAsYZqUfosPhDXxqmcDejlcwpJMv/UK9cTQZ67XWHC2p4khBGdn5xYxJeY/Xiz7h5sg8toTdxtDt/6Db0d18aR5Ht5JUhix8gKQlH6BNrnQq2kqSKZqOyhW99iNcr6sbgC+rtJCeX4qzyR5XR+MULDW3lOTcUkoS17IxpZhfMn2pwoFrvON51+4/ONhDZu+H6Lv3K36peJpNfldy26bLGBblx6R+IY2+d2WVFm7+bBObknKxU3BteBFzTR8TXLCNIr8+5ARdxTV7PmNCx1xiz5uJxTMEV0sxXgV7KXHyJ80ulKyiCvJKqggu2MpVez4jya0fRUVF9F72EAfXfYrL1TMJiu7b7O8wNnYXFVUWBvTrX2d5UXkVc7//ls8SXckod8TPzZGBET54u5ro4FBGl9KtVFRWUVZlZXNaGbPWOPPWdYOaPVbK0VIyjhwm5tersLconvP8N9uzNT6ujtwems6Y9I/5w+FJDgVNwPfwSjxyitgRfA1b/Cby/bbD+Lk5cuOwCCxak1tcyYVxTxGc/hsX/d8wRnT2Y0LvIDoHuNM5wI0ADyeU1QJLngbfzmwY/DavL44nUOXzjt1b/BIwkz+GfU5yoebI5oV0trzKCruh7Ii+l75h3vQJ9YLn/7pTalgsMsQLIYQQQogTV2G28K+FsXg6O7DssbF4uZiOex9Wq2ZzUi6DI31bfXfwd1tSKam0sOFgLotjjzC+V1Crtos7XHjc/auvfQfgK0ug4uRfpBBCiBOSTt2k0FDbMnE2KcwwMrYB4n5sOgC/+VOwdwKPQNj034YBeK1h4YOQuhGmzoKYyTDsXiO7/o9/1Wma4NiTn/t/TEx4B4Z28sPLte4Jl7kgA4cvJnABVi4AcAQKgc41GfWPXdyVD1Yc4OuNKZitmm4qhTlOL+NLEbWTuq9NfYa3l/ox45LulFdZ+PGbD7kLuOCGx7mje93AcLU6hXgsE2H5C/Rc+w49Dy8Ae0cskz+hl89FbE3KJS72Wy7P/AiHsirecryLdd6Xc3XWTKbs+5GsjOfoEGT8F9p3pJDbZ20hPb+swfH6qwQWOD3LNOAtFwfMfl1xytkLHXrCdV/R0TcKLn4EVrzCkM2f8o3nIab/4EzvEC+iAtwb7O+bTSlsSsrlqQuDuaH0K1x3fA7OXnD5O3j0n46HnR3EXIj7gnsYuuRKY13eIWNjZUevwXfA+U9BWS588iT4diLyjp/ItTjz6/y3GXHwPbK/nk7JE1txc2r8VFJrjXn+XURbkvkqcSbTrpqMvZ0iJaeEP//7KHdWzuVae1/Sxz5Jl3G342CnjAs2S5+F0qPH9nOzgllxsZRUzG5wLK01axOP8tGfB9iQeIQvTK9jskvn+sqnyHPqxp2To5jcPwRnkz2U/A2WPkfn7V9SFTKUNxzu5KN4VzgE04aM5MkJ3Y1yMdV84whZ/iLPj/Hi4x0lPP1jzVzVHs4OPOCxiruK4vk68hWe+qmImODu/PPGgThmxRAw93quP/wajJ6B3v4ehZ49+d77GVYllFK0uwQoafQ9+6swW61nugtCCCGEEKKdOlpcgYtjTcJRY/akF1BptpJTXMl/lsTz/KRex32cd5Yl8M6yBD6/dTDnd+vQYnurVfPlhmT6h3tTWmHh5V/3cn73Djg52De7XXGFmfu+2Xbc/auvnQfgi41as1q3ya3OQgghjstC4H6l1FxgKFAg9d/PQodtJxM+kRC3EC59o2EZmvIC2DkPek+BwD7w+z+MGtvBtQLYa/4Pds01Arcxk41lHbrD9IWQd4g9KTk8uzCWHtb9vFT5AZ3WPcFdf95LjyAvfntoVN0urZ9HOFaWDfyAC4cNAm2FHd/Ahg+MjPqx/yRiwHRen9KHf07ogc7cjdd3D6DsPWDq90ZddqsZZl3GU56rmbSyByM6+zN3cyrTi1ZQ5NONqCaC7w3YO8BFL0BQP9g2Gy5+EfvA3vQH+of7wOgnwfwYoHnMwYnHgNhdLjj+8DsLP3+NKx54i91pBTw4Zzvuzg78e0of0FBaacaiIczbmVGr30IXd0SNfwX7I7uwz9gJkcPg4pfA0c3oh4sPTHgDFdSXvj/dxz/tZnPfN14suHeEEWCu/lVVWfjozwP8PXAnd+54CEpyYNCtcMEz4Opb87p6TAT/5bD4STC5woCbjN/t/sXGxZY9P4CTO6Dh+nng4o0vMOHmJ0j+1USXTS8wc+ES7ps6odG37UB2CWGWVDxVKVfsvpeXsosZNuoikr5/iruZT07EZfibj+C5YQYc/gEsVZC+BcKGwTVfHitblPP7q1yfuJDlGzcyfvTImv2nZ/HdnP9xOLeISBcTz4TH0y0rlswL3uZ/g27Ay8VU95ZSN3+Y9D6Mfw2ToxtPKMWI/dm4ONozONK3fveNv+HlL3Kz905u+se9ZBSWczC7mANZxaQeyeTa2C/ZQg+e2hfBlIGhvHRlL+P34DsBLnreuOi0fzHKyROvW79jplcIFqtmb0Yh6w8c5a7XW/fn1x5ZrJIBL4QQp5tS6jNgIpClte5Va/kDwH2ABfhFa/24bfmTwO225Q9qrRef/l4LIc415VUWLn1nNeN6duSVyb2bbLclKQ+Ay/oE8eWGZKYOCqNXSCtLTgIr47N4d3kCALtSC1oVgF+5P4vko6X8/eJu+Lg6cuP/NvLZmiT+Nrbp+c201jy1YDfJR08+gaadB+BLQFugqrTmC6gQQog2oZSaA4wF/JVSacCzGKWQ0Vp/BPwKTAASgVLg1jPTU3FKpW81ysKc/xT8cCckr4GosXXb7JwLVSUw+A5j4tFlL8CmT+HKmcb6vYtg2fPQawqMnlF3W6X4IcmRJ+YfJcSnE7fePBX2+nLl8pfwi+zDTQmjSMsrJdSnpk67XdxP7LeGMOCCqVA9Oc7FLxo15X+dYVwAWPEK9LkGr06jYdFD4OAMt/xcd4LY/jfSe/0HDPGdzq2zNuNrzuY95/0w8Onjf596XWX8a4xD3TrqMX0GU7B+JBMO/8ZV700ivaiKmGBPPp0+mECvujXriV0AmVvh8neNCxy9pzTfj/43QnY81657lz1ZQTy/yItXJvc+FmyetzmV80t/5/6qTyBkENzwXd0LJbUFdIUb59dd1uUiIxj/6wzjIsuNPzSYdDfivGmw6QUqds5n29DhDAhvWEV/7e5EblbFFPe7HRW/mEePPM5vc4dwt8OfFPWchv+UD4yGO76Cpc8Zf4NXfgR9r6uTdOE36VXK/vMbPutfhdE/A6DNlZTOmsITVTuNux0sQBYw6jE6jm5hmHKquWNgdNeAptv5dTYuSMQuwG74fYR4uxDi7cKoLgGwdDZYCxh454/s8e+De/27AEY8CDkJxkWMad+Al1EqyN5O0SvEi14hXsaEGX9RZgnACyHEmTALeB+YXb1AKXU+MAnoq7WuUEp1sC3vCVwHxADBwFKlVFetteW091oIcU6Zvy2NrKIKNh/KbbbdluQ8Iv1ceWVybzYePMrTP+7hh7+NaFUpmfT8Mh6et4NuHT0oqTQTe7igxW0AZq1LpqOnE+N7BWKyt2Ncj468vzyBqweG0MHDudFt5m1O5acdh3nsoq48+FqrDtOk9j2DUkWx7bHozPZDCCHOQlrraVrrIK21SWsdqrX+n9b6I1vwHW24T2vdWWvdW2u95Uz3WZwC6dugY0/ocTmY3IyAcG1awyZbMDdkADh7Ye41laqd33HRywvYuflP+OEuY/2k9xvcsfbTjnQe/XYnAyN8WHDvCDoHuMOov0PvaxiV+iHj7TaxMj67ZoOiIwQXbmeL+9iGM9MHdIPpP8Etv0DXS4yM9G9vApNLw+A7wKDbUdrK+912YafgH+H7jOU9J7fRm9c0r9F/I1gdpV/ZRsbHBPLt3cMJrEqDrL01jcwV8Mez0CHGCKy31rjnoOulPG/6kowtC5m5IhEwailuWL6QV0yfoztfALctbjr43pygvsa2f0+ATqMarvcMxhw6jEmmTTwxfxeV5oYlSfbv2wWAe7fz8bhnMSbPDlzj8CeVfW/EY8oHYGdn/BswHR6Lh0fjoN+0Bn8/yjOIHWHTGVq2mpy4P0FrUr+6l95VO9kU8wzcv9X49/BuuPBfDfpxUmImQ9pmyE+pWZafatyJ0edaVMiAhsF3MF7DFe/B3/dDyMC27VM7IBnwQghx+mmtVwH1I1p/A17TWlfY2lRPoDcJmKu1rtBaH8JIphly2jorhDgnWayaT1cbZS0PZBdTUmFutJ3Wmq3JeQyM8MXLxcSTl/ZgR2o+87aktniMCrOFe7/ehtmi+fDGgfQN9Sa2FfXZD2QXs2p/NjcMjTg2N9hTl/Wg0mLlzcXxjW6z70ghzy6M5bxof+49P7rFY7Sk/QbgtTZK0IAE4IUQQohTQWujBE3IQCOI3W28kc1uqXWydHAlHE2AIXcCsCUpl7v39cekK7jZPJ8OP99KpaM3XPeNsY9asosqeHZhLP3DvZl9+xC8XW0BdVuAUocM4g3HT9gcl3hsm4pdC7BDUxo9sfE+KwWR58HVn8Bj+4zM8dt+bxh8B/DtBF0uImD/HDY+PporHTdDYG/wP/kTqBZ1vRQ8Q3knejMfXt0Z16VPwszB8MEw+PE+KM42LmzkJxvZ/XbN1x6sw84erv4E1bEHnzm+id/yGfy4Zge/rVrPK1WvU+kZgZryecNSQsdDKXDxbnK1Q++r6KxTsGTF88HKxDrrisqrKM4wbgnFNwq8QnG+awlc9SmOk94zAu+12ZuMf00ImfA4R7QPlt//iXndTMKTvmOO0xQGXPWo8bv0jwbv8BN+qU2KudJ4jPvJeDRXwPw7AGWU9GmOUnWy7c8mkgEvhBDtRldglFJqo1LqT6VU9ezvIUDtSFaabVkdSqm7lFJblFJbsrOz668WQojj8kdcJodySriqfwhWDXEZjQfGD+aUkFtSyaBI4y7aqwaEMCTSlxcWxfH52kPNJnt8uvoQO1PzeXNqHzr5uxET7EV6fhl5JZXN9m32uiQc7e2YNqTmO0MnfzemDQlnwfb0BhcLtNY8Mm8nni4m/u/afti3cpLX5rTfALy53Cg/AzIRqxBCCHGSDueX8eQPu+ueXOQeNOq7V2fpxkw2JsBMWm081xo2fgyufpR3vZwXf45j6sfr2afDKQgYzI16Ed6qhGnFD7O7oOFte88tjKW0wsIbU/ocyzQ4xuSMuuI93ChjYPInVJiNz/zSHd8Tbw2lZ59WJGq5+sLAm5sPvg6+E4oz8dr1P1TaJuh5Zcv7bQv2DjDoFuwP/QnvDTDqqg+6HUY8YNTKf38g/Pk6RI+D6AuPf/9OHqhbf0MPu4+pDqsY+8cEBq+6BQc7hcvN3zcbPG8TPa4AFI8FxzFzRSJJOTV1Edcm5hCqjxhPfCKNR49A6DO1YfC9FSKCAvje82Y6Fu7B4Y+n+N0ymKDJL+NQ/2+qrflGGXcDxP5o/F9Y9BCkboArPwDvsJa3P0tZZBJWIYRoLxwAX2AYMAP4VqnWT56ntf6v1nqQ1npQQEAzZdmEEKIV/rvqAKE+LswY3w2AXWmNl4bZaqv/Ptgt39sJAAAgAElEQVQWgFdK8f71/Rka5cvzi+KY+tE6EjIbT8RenZBNn1AvxvcKAiAm2BNoOtgPRnLQ91vTmNgniAAPpzrrLu4ZSJVFs/HQ0TrLD+aUsDejkAcuiG6wzYlqvwH46vIzIBnwQgghxEl6c3E8czalsHRvZs3C9K3GY/AA4zF6HDi6G2VosvbCF5fD/t/I6TGdKz/eyv/WHOKGoeEseWQ0XuMeAwdnSid+yBGXLtz02UbWJOSgtZGx8NvuDH7ZncFD47oQ3cGj8U517ElG1BSmsYQ9u7ZBYQbe2VtZrIczIKJhXfETEj3OCAIve954HnPqy88cM+AWcPICv2i4ayVc9qYxserf1hmTuloq4aIXT3z/zp7Yj3+ZyjtXkeLYGX+dT+IFH6L8otroBTTDMwjCh3Mx63Cws+ONJTW3bq7Yl00XUxbaPbDN5vDxHnEz26zR7NKd+T78acZ2D2yT/bYoZrIxOeyvM2DnHBj7z6bnAjhHSAa8EEK0G2nAD7aykZsAK+APpAO1rxSH2pYJIc5iBWVV3PfNNg7lnPyEocdrS1Iu21LyueO8TgR5uRDo6czutPzG2ybn4u1qIsq/5m7RDp7OfH7LYP7v2r4czCnhsnfXsL9eEN5i1exOK6B/WE2iUXUAvrk68HM3pVJSaeGWkZEN1g2K9MHJwY7VCTl1lleXSG3N5K6t1X4nYa2UALwQQgjRFhKzivlxh/G9a8W+LCb1s92FnL4VTK4Q0N14bnKBruNh17ew/Stw8mBTzDNM39gDd+cKPrtlEBd072i07XYpPJGCn4MT33Qq4fpPNnLj/zbSN9SLm0dE8sqv++gV4sldo5sPBvtMfI6KdxbitvolqJqAQpMecgnOpuMoydIcOztj8tglTxuTajZWquZUcQ+Av8cbE8TWTkirrmXfRpPMu4b0Ivzhpaw/kM6Y3p1Oen+tFjMZh99m8I9B8Nz6DO44L49+Yd6siM/iTuejKN+2uxAwsW8oI35+lkqr4ufLB7TZflvU80pjktjNnxiTDI95/PQdu50yWyQAL4QQ7cSPwPnACqVUV4ypyXOAhcA3Sqn/YEzC2gXYdMZ6KYQ4LRbHHuGXXRkUllUx+7YhHMcNMa2Snl9GUk4JVRYrZovGwV4R7O1CkJczH686iLeriWsGG9f+eod6sSu98aD4luQ8Bob7NJhwVSnF5P6hDOnkx8jXlvNHXCZdO9YkciVkFVFSaaFfeE0A3s/diSAv5ybrwFeYLXy65iAjOvvRJ7ThHcLOJnuGRvk1EoDPonOAG2G+rsaCNW+3/Aa1QALwQgghxFnu3WUJOJvsGRTpy6qEHKxWbZzwpG8zMrFr1wrvfwPE/gD9byJtwAyunbmL86L9+c81/RrefudgPI/wc2PZY2OYvy2NT1Yd5NFvd+Jgp5h925CGpWfqcfUN4Xuva5mS9wWW1fEkWMOI6tHGAdZ+N8Dqt4zH061eXfxjlGqz7HAAbzcnxvY5DZnvtfW8An57nGlu23jffTCv/raPf03sSVZRBSFeR8C3b5sdytvVkdvHdMPBzo7ugZ5ttt8W+XaCqLFgrmx0kuFzkUzCKoQQp59Sag4wFvBXSqUBzwKfAZ8ppfYAlcDN2rgVMVYp9S0QB5iB+7Suru8rhDhbLY3LRClYnZDD0r1ZXNSzY5vtu6zSwqT315BT3HSt9QcuiMbV0fhe2TvEi6V7Mykqr8LDuWaup9ySSg5mlzBlYGiT+wnxdqFbRw82HDzKfedHwxdXQOR5bHe5DoB+YXXvlI4J9mRPE8H+BdvSySys4M2pTX8vGd3Fn5d+2cvh/DKCvV0oq7Sw8VAuNw2LqGmUsKTJ7VurHQfga90yIQF4IYQQ4oTszyxi0a7D3DOmM90DPVi1P5td6QX0C3aDI7uM7PDaOl8ATx0BBye++m0fdkrxxpS+Lda+czbZc8PQCK4bHM4fcZmY7BU9g1sXKC0acDcZKxYRVJzBL5YpXBLtf6Ivt3GuvvDovmMXDEQb8QiEiJE4xf/EQ+Nu4Jkf9/DCojhcKMelItsIXrehxy7u1qb7a7Ubvgc7Bwm+20gJGiGEOP201tOaWHVjE+1fBl4+dT0SQrQn5VUWVifkcN3gcDYn5fLSL3GM7uqPk0Pb3FU8d3MKOcWVvDGlD1EB7pjsFZVmK4cLyjmcX0ZhWRV3jKpJBuod6oXWEHu4kGFRfseWb0026r8PivBt9nhDo3z5fmsaVWVFmA79CUd2safT+Xi7moj0c63TNibYi2X7siitNB+7AABG0sjHqw7SO8SL85r5fnleF2PdmoQcrhkcxvqDOVSarYztZpsbo7IU0ja37o1qxl+kBrxMwiqEEEKciHeWJuBqsueuUVGMivJGKaMMDVlxxoTnIY1kmzs4UWG28N2WVC7s3oFAr4YTrDbF3k4xvlcgF/ZofcbFqJ4RvFJ1PeXaxCqnMfQMOgUZziZnCaCeCj2vgOx9XNfZTJS/G5uScrkosNRY18YB+DPG3iR/O7VIBrwQQgghRPuy/sBRyqosjO8VyL8m9iT5aCmfr01qk31Xmq38d9VBhnTyZeqgMAZG+NAn1JtBkb5c0TeYe8Z05vHx3fFyqcl07x3iBcDuehOxbknOxWSv6BPq1ewxh0X5UVpp4cDebcaCsjzcD/1OvzDvBqV1YoI90Rr2ZtRN3v59zxEO5ZTwt7Gdmy3H062jBwEeTqxONMrQrIzPxsVkz5BOtosEqRuNubtOUvsNwEsJGiGEEOKkJCbuo+ve91jo+y4+H/bC951InvFbwcr4rIYTsNazODaToyWV3FD71rtTpHOAG9u9LqRvxSeERfdqUA9QtGORowAwpW3g8fHGXAIXBdruYmzDGvCi/TBbrWe6C0IIIYQQopY/9mbi5mjPsChfRncNYFyPjry3LIHYwwWsS8zh643J/LIr44T2vWB7GhkF5UY5mFbyd3cixNulQR34rUl59ArxanG+r+rg9+H92wHQju6MLvmNfmEN67jH2IL9tSdi1Vrz4Z+JRPm7cUlMYLPHUkoxqos/axKysVo1K+OzGdHZr+bugaTVoE7+ToK/RgC+XDLghRBCiONlN/927rdfQKTDUaO0TKcx3Fb8CbdmvkxF4ipw8QWfyEa3/XpDMmG+Loxq63IwjVBKMbZbABU4MqrLqT+eaEMB3cHZG1LWcUlMR96/vj/jqjPgfc6SDHhRh2TACyGEEEK0H1prlu3NZHTXgGNB46cv60Glxcpl767h+k838tSCPdw/ZxtF5VXHtW+LVfPhygP0CvFk9HF+T+sVUrc2e4XZwq70AgZF+DSzlcHf3YkuHdwpS48Fe0dSu9/OSLtYhnk3rPUe7OWMj6uJ2PSa2PHqhBz2pBdy95go7FuR3DWqiz95pVUs2nWYlNzSmvIzAIdWN37X+HFqxwF4W/aUs5dkwAshhBDHqSx5G1Fle/gt5AHs798Ikz+C678lY9AMrrBbj9O+BRAysNHSGolZRWw8lMv1QyJOWzb6lf1CCPJyZmy3DqfleKKN2NlB+HBIXo9Siol9gnEpSgZXP3BpmKEi/vqkBrwQQgghRPuxJ72QzMIKxtUqARrp78Yn0wfx8uRefHPHUP5zTV+0hl1pjU9W2pRfd2eQdLSU+8ZGN1vGpTF9Qr05lFNCQZkR9P9px2EqzVYGRzZf/73a0Chf3AsT0H7RLHe7FItW9MtZ1KCdUoqYYC9iM4zXVlRexb9+2kOwlzNX9g9p1bFG2pLO/v17PEDNd9KKIuPO8U6jW7Wf5rTfAHx10N0jWALwQgghxHHKWv4+pdqJoDG31yy0s6PjhKd42P5Jiu29oOslAKxOyGbW2kNkF1UA8PXGFEz2iqmDmp6dvq0NivRl/ZMX0tGz9fXmRTsRMQJyD0BRpvE896Bkv5/FLBYJwAshhBBCtBd/7M3ETsH53esmMo3t1oEbhkYwItqfC7sbwfkdqfmt3q/WmpkrEukc0EgZF4sZirOb3b66DnxsegGJWUU8+1MsQzv5ckH31iVcDe3kR5ROI989mnWZJjY4DMJ5z1ywNMzijwn2ZP+RYirNVv4xfxepeWW8fV1/446Ag3/CnGmQta/JY3XwcKZHkCfp+WVEBbgR5mub6DVlA2jLsbKbJ6P9BuAri8HOBG7+EoAXQgghajuyB9K3Nb2+LI/AlJ/5w340/bvUreFuZ6dw6HYJ51k/obzfrTy/KJab/reJ5xbFMfzVZfztq63M35rG+F5B+Ls7neIXIs4KESOMx5R1xmNuktR/P4tJBrwQQgghRNOqLFa+3JB83OVeTtTSuEwGRvjg6+bYZBsvVxNRAW5sT8lr9X6/2pjCviNF3Ds2uu5d0RYzfH01zBwMlaVNbl8dgN94KJd7v96Gq6M9707rj4N960LRw0KdCLPLJkGHsj01n31Bk6E4ExKWNGgbE+JFpcXKMz/u4dfdR5hxSTeGhLrA70/C7Csg/lf46mooPNzk8apLoY7tWusCwaFVYO8IYUNb1efmtOMAfAk4uoGThwTghRBCCIDSXFj0MHx0Hnx3S9PNNs7GSVeQ03N6oyVkxnbvQH6ZmYvfXs3na5O4ZUQkvz00iltHRrLxUC6F5WZuOg2Tr4qzRFBfMLlC8nowV0BBqgTgz2JSA14IIYQQAqgqB3Nlg8VfrEvimR/38NWGlFPehcP5ZcRlFNYpP9OUfmHe7EjNR+uWz+X2pBfw4qI4xnYLYHL9Mi5//AsOroSyPEj8o8l9+Lg5EurjwswViSRkFfP2df2O627ngPIkABZnepFdVIFjj0vAPRDWvgNpW4wLATYxwZ4AzNuSyrgeHbirp4aPx8CGD2DIXXDbYijPh6+nNjnPaPV7eElMrffy0CoIHQyOrq3ud1PabwC+otgIvjt5QIVMwiqEEOIct/t7eG8AbJsNPhHG1f/GTp6sViybPmGTtRvDR4xtdFeju/jjYKfIL63k45sG8twVMfQI8uSpy3qy/skLWPro6GMzzwvRInuTcWKasg7ykgEtAfizmNlqPdNdEEIIIYQ482ZPgp8frrMop7iCd5YmAPDzrqazrdvKsr1GCchxPVsOwPcP9yGnuJK0vLJm2xWUVXHv19vwc3fkP9f0q5vQtXMubJgJg+8w5nyK+6nZffUJ9cJs1TxwfjSjugQ027YBW8mYFXl+APSN8IfzHoHUjfDphfB6BHxzHZTm0snPDXcnB0K8XXhzal/sNn4A+Slw43yY8AaED4NrZkP2Pph3Y6MXToZ08mXdExcwNMo4HmV5cGRXm9R/h1YG4JVS45VS8UqpRKXUE42sD1dKrVBKbVdK7VJKTTjpnlUWSwa8EEIIAUag/af7wSsM7lkNA28Fc3nNhOW1HViGR2kqf7hdTo8gj0Z35+3qyLy7h7P4kdEN6vk5OdgT3aHx7YRoUsQIozRSxg7jua/UgD9bSQa8EEIIIU7G+gNHj6sW+Smx+3t4byBUFFFYXsWDc7ZzMLu4QbNtKXk8vygWa/3zn8pSSNtklDaplZzw5uJ4yqosTBsSTuzhQg7lNPJ9rQ0ticskyt+NzgHuLbbtH+YNwPZm3nutNY9/v5PD+WW8f33/umVt0rfBwgeNeujjX4PuE2H/YqhqOqB//ZAIbhkRyUPjurb+RVXL3ovFzkSy7oijgx3dAz1h2D3w9wSY8jl0mwD7f4OUDdjZKT6+aSBf3zEUb1dHI1nNtxNEj6vZX/SFcMV7cOhPWPZ8o4cM9napeZK8DrS1Teq/QysC8Eope2AmcCnQE5imlOpZr9nTwLda6/7AdcAHJ92zymJwdK8JwLfiFgkhhBDirFRVCuYy6HU1dIwx5kcBKD3aoGnZ2o/I1l74DprS7Ez1AyN8CPJyaXK9EMclfDigYecc47lkwJ+1pAa8EEIIIU5UaaWZu7/cwiPzdrSqFMrJ2pqch9nSyN17aVvgaCJsncWX65NZuPMwb9sy16tprXnx5zg+X5vEovrZ7JmxRnC2LA8ydwOwO62AeVtSuWVEJA9cEA3AL8eRBb/vSCGZheWtbl9QWsXmA5lM6tq6ebu6BXrgbLJj/4FD8PkEyD3YoM13W9JYHJvJP8Z3Z2BEvTuiF9wD7h1h6izjDtiYK43YbeKyJo95Xhd/nrsiBvtGyqK2KGsfVt8uWLCnV7Anjg62ELZ7B+h1FYx71nheYkwGOzLan0h/N9uynJrvzLX1ux763wSb/gv5qc0f/9BqcHCG0EHH3/dGtCYDfgiQqLU+qLWuBOYCk+q10YCn7Wcv4OTvs6hdA95aZdQUFUIIIc5FZbYsBWdjIhtcbbfF1Q/AW604Jq/kJ8sIJvaXGu7iNAodDHYOcGAFOHnW/I2Ks45kwAshhBDiuOUeAq35cfthCsvNHMopaTYTuy3szyzi6g/X8dnaQw1XFhlhS+v6mXyxej8me8UvuzNIy6uZVHRrch7bU/JxtLfj//7YT1XtQH71XZ9Ayb7laK15flEsfm6OPDiuC8HeLgyM8OHnXRmt6uue9AImvb+W6f/b1Oi5VmMXK1bEZ3G7WsT9u6dAUWaLxzDZ29E7xAuHQ8sgea0RYK7n590ZRAW4cceoenez5h6EnHgY+WBNYDtyFLj4tFiG5piKItg6C5a/BAsfgLk3NL9tdjymwJ6M69GBiX2CG653tfXDFoCvoyS7Zn19Y22FXf58rfn+Jq02Stc4tO4CR0taE4APAWpfFkizLavtOeBGpVQa8CvwQGM7UkrdpZTaopTakp3dyBtU27Ea8La4vpShEUIIca4qLzAeXYzbBpsKwOcfPYK9NoN3OGG+Jz9RjBCt5ugKwf0x6r93gmbuvhB/bZIBL4QQQojjUpgB7w1Eb/yYWesO0bWjO84mO37Yltag6f7MosYz1k/AlqQ8AGavT24Y1C46Ak5e2BVlMKZiBW9d0w8FfL426ViTj1cdxNvVxJvX9CXpaCnzt9b0N//gZnK1BwesQWxavoA+zy9hS3IeMy7phqezCYCJfYLYd6SIxKzm45m5JZXc/eVW7O0U8ZlFLNyZXmf9rrR8Rr62nHWJOXWWL4k7wjDHA9hXFcPqt1r1nvQP96FjwU7jSV5SnXVVFitbknIZ2dm/4Z3UB5Ybj1Hn1yyzNxllaOJ/MyakbYrVYgTe3x0Aix4y+hr/u3EXwve3QdKahttUFENBCnTozqc3D+a28xopb2lyNmLGJTkN15XkgFsTNee9Qo0a9ju+gZyExtuU5EDmnjYrPwNtNwnrNGCW1joUmAB8qZRqsG+t9X+11oO01oMCAloovn+sBnx1AF4mYhVCCHGOKm85A35pXCZ3f/w7AIN7dT+dvRPCED7cePSR+u9nM0sbfSkWQgghxDki9wBoC+VrPyAhs5A7R0VxSUwgi3ZmUGG2HGu2NjGHi/9vFT/taEVRjWbqjlfbnmIE4NPyyli+L6vuyqIMLF0uJl514iHn37iidyCX9w1m7qYUCsqqOJBdzNK9mUwfFsHlfYLoF+bNO8sSKK+ycLS4guz4jcTbdcbU5XzOc9zPpF4B3Dw8gikDw44dYkLvIJSC/cu+aDzIDJgtVh6Ys43s4grm3DmMmGBP3lqyn0qzcb5VXmXh0W93crignH8vjj+WCV9eZWFlfDYxJtt7tfXzlkuqAP3CvOlHvPGkXgB+V1o+pZUWhndu5E7WAyvAKxz8Otdd3vNKqCyCgysaP2DGLvjoPCPw7tsJblsCz+TAjAS4f5NRtnLeTcYdErVl2/oY0KP5F+Tm3zAD3lwJFQVNB+ABznsUHFxgxcuNr6/+fbXRBKzQugB8OhBW63mobVlttwPfAmit1wPOQBO5/q1UuwY8SABeCCHEuas6A965XgZ8SQ6VZit//24nd8zeQqSTMclP325dzkAnxTkvYoTxKPXfz2qSAS+EEEKcJXZ8Y0xGeqoVGJnjLkXJXOYax+V9g7l6QCgFZVUs32sExs0WKy8sigNge2pe8/srzoLXO0HC0mab7UjNZ3TXAIK8nJm9PqlmhdZQdIT9pe7MrLiMUEsq7P+NO0Z1oqTSwpxNKXy6+hAmeztuGh6JUorHL+lGRkE5n69N4oEv1xNpTSWqz0jCB16KyVLGS0MqeX5Srzq1zjt6OjM4woeR+19Dr3uv0T6+sSSetYlHeenKXvQN8+bx8d1JyytjzqYUAP7zx34Ss4qZ1C+YHan5rLFlwa9NzEFVFuNbmQEDbzV29ufrzb9vwIAOim7KlslfLwC//oCR3DUsql4A3mKGQ6ug89iGd7lGjTG+o8b+2PBg5kqYf7uRNDb1C7htMYQPBTt7Y72zF0yba9TSn3t93con2XuNxw4tBeADGgbgS20Z8W7NlMR0D4Dh90LsAsjY2XB90mowudnu8G0brQnAbwa6KKU6KaUcMSZZXVivTQpwIYBSqgdGAL6FGjMtqCwBp9oBeClBI4QQ4hxVvwa8s5dRb7v0KEv3ZvL91jTuHh3FyxcHGuubu9ovxKkSMQK8IyBy5JnuiTiFpAa8EEII0Q5YLY1Oonlclr8E62e2TX+aYwvA52hPHvX8E2eTPSOj/eng4cT8bUZ+79zNqcRnFuHh7MCe9BYScLPiwFwGaZubbFJYXkVidjGDIny4cVgEqxNySMwqNlaW5YGlkj/S7EgOvBjtHQ5r3iYmyJOR0X58uvoQ87elcfWAUAI8jPrfI6L9GdHZj9d/30dxyi5MykLHbkMh8jxAwcE/G+3Htd3s8dJFlBXmNli3MzWfj/88yA1Dw7lmkJH3PLqLP8OifHlveQIr47P4ZLWx/t9T+hDo6cx7yxIBWBx7hH5OtvryXS6GQbcZF1SOHjCWlebCshch4Y86xwws3IOd0mQ6hjUMwB88SvdAD3zdHOt29PA2Iym68wUNX2DtMjT15+7c+CHk7Icr3jMmbG2sRKVfZ2NS1+x4mH+HEewHyNoL9k7gE9lwm9rcAhqWoKkOyLf0nXj4/cbFg+UvNVx3aDVEDDdeXxtpMQCvtTYD9wOLgb3At1rrWKXUC0qpK2zNHgPuVErtBOYAt+iTmc7YYgZzeb0MeAnACyHEX0JxyxPAiON0rAa8j/GolJEFX5pD/JEilIJHLuqKQ5nt5MO9w5nppzi3OXvBw7sgetyZ7ok4hSQDXgghhGgH1s+E94dAydGW2zamMAMK06Go5UlCf99zhOX7TuI7XkEaJQ7efG29iE756+DoAeztFJP7h7AyPotDOSX854/9DO3kyzWDwtibUdh8HfjqwPHRxCab7EotQGvoH+7NtYPDcLS3O5YFby0wyrbsK3bjbxd0RQ1/ANI2wYK7eSo8DnNxDlUWa4OJSGdc0g2TveKhnraJWoP6gqsvBPUxMsQbMc7HyPAvLWhYp/yL9Um4OdrzxKU15UOVUjw+vjs5xZXc8cUWQn1c+OeEHjg52HP3mCg2JeWyLjGHpXuzmBhoS9Lq0ANGPWZMFrrsBeNv491+sPpNWPxPI+O/WuoGLNjxqx4BZbnHvmdWmC1sScprovzMckBBpzGNv9kxVxolX1a+WnOswsOw8nXoeil0vaTx7ap1Ph8ufR32/26UqtEasveBf9eabPmmuAVASb3yQtUB+ZYC8C7eMOxeSFhS92JEUaYx4Wwb1n+HVtaA11r/qrXuqrXurLV+2bbsX1rrhbaf47TWI7XWfbXW/bTWS06qV5W2YLsE4IUQ4q9HAvBtr7oGfPW8KGALwOeSmF1MmI8rziZ742q/sq8pVSOEEG1MMuCFEEKIdmDnHLBW1ZTqOF7pW43H4syarONGlFSYmfHdTh6eu4OCsqoTOpQlP5Vksw9ZXa5D2dnDls8AuGpAKGar5sZPN5JXWskzE3vSO8SLCrOVA9klTe+wFQH46vrvfUK98Xd3YmKfIOZvTSMxq5g35xvZ6uGRnbm4ZyAMuAn63wj7F9Nz3SNsc76HmcGL6RzgXmef/cN92PbMRVzoddj4vuUdYazoNMYI4FeWNuiHV4Hx+7GW5VFUXvP+5ZZU8vOuDCYPCMHDuW6W9YBwHy7u2RGL1rw5pS9uTg4ATBsSjr+7E499t5PckkqGuWcZZVK8I4wErKF3Q9yPRtA9ZBCMfNjIQM/YUbPzlA0c9ejGphLbndN5yQDsSMmnwmxleP3yM2DUfw/ub1xsaEznC2HAdFjzf/D7E2C1wpKnwWqG8a82vk19Q+6EMf+AHV8Z22btgw6tmNfMLcAocWOtmUvgWADetRWV0ftNMx53fVuzLGm18djpDATgT7tK23+0OpOwSgBeCCH+Ek7iBijRhPICcPQAe4eaZa5+UHqUA1nFRHewnRwWZxknIXbt8+NdCHFqKaUeUUrFKqX2KKXmKKWcbWUkNyqlEpVS82wlJVFKOdmeJ9rWR7bmGJIBL4QQQpxhmbFGGRaomayyCb/symDoK0vJL62suyJ9i/Gorc0mUC3Ynk5RhZnCcjOfr607UabVqvlpRzoHsoub7UNZTjKpFj8uHtYfek6C7V9CZQndAj3oFeJJen4Z1w4Ko1eIF71CjBjgnvSCpnd4LAB/oMnvnttT84nu4I6XixHcnj4ikpJKC5e8vYr8LKO++j+mjMHOToHJBSbNhMcPwu1LIXocl+bPNb5b1ePhbDJqhgf1rSmp0mkM/D979x3f1l3vf/z1leS9t+MZZ+/ppll0b9p00XRA21sKpUChwGX+LnDvhVvgMm9LKbOlUCgddAKlg+6kSdMkzV5N7MSJk3jJS/KQLX1/f3yPLNmWZxRLiT/PxyMPWUdHR1+Z0Eif8znvj9cDVev6L+T4NvM87eb3aw/2bH5i42E83T5uWTYx5Pp/ev0Cnv3MCs4MKojHx9i546wyjjV3EOuwUdxdaYrU/u9+K74Ai/8NPvoU3Pw0rPwi2GMDxWVvF1Rvwle4hCqd2+t3ua6iAaXgzLI+BfiOZhP1Eyp+xs9mgyvug6WfhXd/BX+6GnY8BR/6khm8OlznfAOW3AHr7oeWI5AzzAK89plYIZBXkIQAACAASURBVL+eCJphFODTS0yn+9a/BP4uHXzb1KLz5w9/7cMQnd/QO63/88bJEFYhhDj1SHEm7NqbAvnvfolZaHc9FfXuQAHeXWcGygghxh2lVCHweaBcaz0HsGNmN/0v8DOt9RSgEbjdesrtQKO1/WfWfoO/BuD1DXJJuBBCCCECtIaudvNZ3lULHWGqa21/Eq3s+OzxUP/BoLs+uekwNS2d/GN7n6iZIxsx/7IzYAyN1ppH1h1idkEqF83K48E1lb264H+3poK7H9vC+T95k9t+v4E1H9TTL41aa2Jaq6lR2ZxZlmkKrB3NsOlh0Jpblk0kLzWOf79oOgBl2ckkxNjZcXQYBXhPa8giudaaLYebWFgcuCp4QXE650zPYWFxOl9eZor8KmVC7yfa7FB8BrZLvo/yemDDb/q/drfHnPwoWBDYVrrMzOcKFUNzfDsACcrDH97eQ3NbF16f5s/vHuLMskym5aWEfIvJcQ7mF/e/qvmjZ5aSlRTLWVNzcNTv6T2kNCEdrrgXpl4QuD/tYjNo19tt1tLVRu7ss0kvnApAe63JjF93oIHZBamkJfbJPD+4BrTXxMQMRim4+B5TRK94w3Tlr7h78OeEOsYl/wtzV5v7ebOHfo6/yB48iLWtHmwx/b8/D2TBTWaewuEN5n7l22a2VXDzWxhEZwG+pwM+2WQY2WKkA14IIU4VWksXfLh1NNNmT6bBFTTYJikbn7seT7ePKTlBBXgZwCrEeOYAEpRSDiAROAacB/zVevwPwFXWz1da97EeP1+pUNOxepMOeCGEEGKYnv8c3JMP/1sKP54KP5luivEnwueD7U9RnbWMnV357Nu5EVdn6AiZ1o4u3tlvMuKfsYadmmN44ej7UHymud9SHeLZsKHSyd6aVm5ZVsoXLphGa0c3D60xXfCbqxr54Yt7uXBWHl+8YBrbq5v52IPv8s1nd/Q+SEczcb42HBnFJjKz+EwoOsPEpDywlNUdT7H+rtk9w07tNsWsglR2DjaItfEgZE4yPzf0PwFR5WzD6fawoKR3Afvh25bw108vJ9PnhIRMiIkPffzsqTD9Mnjvd4H6pF/dbtPtPiGoOzo2ybynyj6DWDuazVpTiwCwdTTz27creHNfLYed7dy8rHTg9ziApDgHz352BT+6dIL57pc7a/AnzLveZKRXvgGH3zXrKFnKf1yzjCadxNbtW+jo8vJ+VdMA8TOvmZiboiVDL04pOOfrcP2f4abHzZUFI2WzwVUPmGNMuXDo/f3ffYML8O46U5gf+mOtMfMKiEmErY9CczU4D4Q9/x2itgAflAGvlOmClwK8EEKcOrR0SIaTbm9kh1Pxs3/tC2xMzMLW0YQNH5N7ImjqIEkGsAoxHmmtq4EfA1WYwnszsAlo0lr7v5kfAQqtnwuBw9Zzu639Q3zzClBK4fVKAV4IIYQYloo3TXb2RfdA+cehqw0aK4d+3mCObIDmKl5SKzmoikhqreTSe9/ivYPOfru+ua8Oj9fHudNz2HiokaoGK6O8bi94XOxOt4ZqtvTpgNca3rmfZ9e8T1pCDKvmFzKrIJVLZufz0JpKqhra+Nyj7zMhPZ4fXzefuy+Yytqvn8dVCwp4ctORXlnnDUdNh3VO0WSzQSm4+RnTqR2fBv/6T9S98+C1e8zVAsCcglR2Hm3GF+qkf3uTiRuZYnV5N+ynqqGN2paOnl22HDYnORYWZ4T+HbYcg77d732t+Lx5nff/1Hv7UStPfcKC3tsnnWMecwUVgo9bJyOsLPFV0xL5/dpKHnj9ALkpcVw8O9/8ro9uMe//Vyvh71/snWceQnFmIhkuK/9+qAL81IvM73nbE1C1HtKKIc3879meVExnbQW/fasCj9c3wADW12HiSnDEDv46wWZe3rszf6TsMeYYw+lAD1mArx9e/IxfXArMXAU7noH9r5htYc5/h6gtwAdlwIMU4IUQ4lTjHd2AHhFal7uJJl8iGw8GZdslZqPQpGNlwGsdONsvhBh3lFIZmK72MqAASAIuCcNx71BKbVRKbdRaSwe8EEIIMYjmti7erWjgsTW7obmKXakfguV3waJbrR2ODPr8LYebWP3rdfx10xG6vSGamrY/iXYk8PuG2Thyp1Oo6knUHaz+9Tpe3nm8166v7TjMXQkvc3/8L4mli2fetzrdrfz3L2zOo1M72LV3T+/XqN8HL/8HUz54kOsWF5EQawfg7gum0trZzapfrKG2tYP7b1zUk7Ee57Bz87KJeLp9vLwzkCm/xzr25ClBed5xKSar/PaX4a5NJhf+rR/CL86EfS8xuyAVt8fLwYYQg1ibzNBQSleAPQ730b1ccf8arvrFWpxuk3P/flUTibF2puUl938+mMidlPzQj/mVLDVd3+vu7z2k9thWkw+e0SfbfMaHAQ17Xwhss+JnKDsLgNsWp9He5WXjoUZuXFJCTGcT3LcQfnM2vP1jk+S68SF49jNDFuGptYbvDlWAd8TB7Kth99/g0NrAVQ9ATsl0yhx1/OSVfdhtijMm9hmyWvGm6QYfLP890noK8PWBbe76kV8VPv8G6GyGN35gBuzmzQ3fGi3RWYDvyYC3spDiUqUAL4QQpxJf6Msgxej42htpIYl9Na2BjhJrCv2U5A7zwdfjgu52SJYOeCHGqQuASq11nda6C3gaWAGkW5E0AEWA/zrzaqAYwHo8DWjoe1Ct9W+01uVa63KbTeGVArwQQggRUktHF8t/8CrX/2Y9j/7DdNLevyOGynq36TyGIQvwL+08zoZKJ19+civn/eRNHn+vKvBvr7cLdj6Da+KFHGmzk1JkMrKfuT6XKTnJ/O+Le8y+Ph/dW5/kS/s+xpf1wyTtfZov5G/j2S3VJqP9yEbctmQO2ybQ7Mhi3/69PPHe4cAinBUAXGZbz8fOLO7ZPHNCKpfOyaeprYuvXzqzX0b5opJ0ijISeH7r0Z5tRw+ZiJiJk6aHfsPZU+Da38GtfzPF4kdXc+0rK/lLzP+gX/yGKXgH8+e/Z05CZ01m36736ejyUu/y8IXHt+Dzad4/3MTcwjQc9gFKnq3Hh+6AB9MF31QFu58PbDu2xcTP2PocO28OZEw0hW6/49tNITjHvPeieA9XLSgkxq64cUmJOdHRWAlnfRW+vB8+vQbO/Q/Y9hg8++nBi/C1O02MznC++81dba6+cNeZEwsWR1YZRaoeGz7mFKaZAbN+Gx+CP10DWVNh7keGfo1IScgAZe89C8BdB4kjbEorOwtSC83JmYkr+//vGwbRWYD3WAX4WOtsVXxq+IZVCCGEOPl80gEfTrbOFpp1Ej4NWw9bA4kSzSWCs9Oskx09096lAC/EOFUFLFVKJVpZ7ucDu4DXAf83p1uB56yfn7fuYz3+mu43Oa03hWTACyGEEAPZcaQZt8fLNz88kz9cbhpKq+zF/NfzO9EJGeBIGLIA/0FNK1Nzk/ndLeWkJcTwtae286f1Vtd3xRvQ1sCWdBO/Ujx9IQCJLRV88cJpHKhz87etR+Hvd+N45hO0+BLYfNaDkDuLG33/oLLexZbDTbgr32VTVxmfOXcq2QVlTE908bWnt/Gbtw7wxMbDvL7eZIVPUE4mtm3vtb7vXjWHn66ez8dXTOy3dqUUV8wvYM3+ehpcnWitcdUepBsHtqE6zsvOgjvXwpUPwJyrSVAeSiv+Ai98tfd+TivCJ6OUalsBKe5DfP3SGfznqlm8ta+Ox556nDtr/pvlA9XXfV5w1QzdAQ8mBz5zMrz2P/DmD2HzH6FmZ+/898CbN1niFW+Y7HeA41shf57pqAZob+K7V83hb59bSX5afKBre8aHIcmKfzn7q3DeN2Hb4/DcXQOvrXa3GVI6nJzzkmWBE0BBHfBkTMTm6+LrK9L49NlWpr63C/7+JROFM+lc+OSr0X2Ftc1m1tcvgmaEHfA2u8nLh54rFsItygvwwRE0UoAXQohTxlCXzInh83YT63XTZktCKdh0yMTQaKsAPy3FGszqzxuUIaxCjEta63cxw1Q3A9sxn/N/A3wN+JJSaj8m4/1B6ykPAlnW9i8BXx/qNZRSeH0y40MIIcTpae2+46zZE3og6XDsOGoKr9csKiLDXQH2OK45fyVv7qvj5d21kFYEzYcHPca+GhfT8lO4YFYez9+1glkTUnlui7WmbU9AfDp/d88mMymWkimzTfdv3V4umZ3PjPwUHvjXLvT2p9iacRHX6R8w60NXw5l3ktGyh5WOvTy/YR/xjfs4EDuDT3xoErbUAqYntrJicjbfe2EPX/3rNg7v30Eb8fjs8bDjqV7ry06O45pFRQw0t33V/AK8Ps0LO46z53grGd01dCTmD6+j2BELCz+KfdW9fDv3Xl5OusJ0nAfHmzYehIRMjrTH8OKxZCbaarn1zCJuWlLC1QsLSd32ey61vcvHD3we3P0u7DPFWe0dXgHeZoeL7zGJHK/fY4bqdndA8QADSWeuMo1o+16Gbg/U7oH8uaZLG6CjiaQ4BzPyU839NqsA37fAfdZXYMXdZiho3d7+r+PzmQL8cHPWbTYov80Mg82bHdieMRGAO+bauGSOdcbizR/CxgfN69/0uMmPj3ZJOYGTGZ426HKP7qRB+W1m+OqMy8O7PkuUFuDdgDJTaEEy4IUQ4lQjGfDhY52AjkvJYlpuCpurTAG+wWe6akoTzLAi3NZld8lSgBdivNJa/6fWeobWeo7W+matdafWukJrvURrPUVrfZ3WutPat8O6P8V6vGI4ryEd8EIIIU5lHV1eXtp5nM7u3g1Dj6w7SN0jt8Gj17P72OgaQLdXt1CYnkBmUizU7YHsqdyyYjLT81L4zt924U0tRDcd4ZH1hzjnR6/z1r66Xs9v93g53NjGtFzzOV8pxYfnTWBzVRPH6htMvvisVbxb1Up5aQbKEQeZZVC/D5tN8YULppLq3IbqcvNo6wJWTsslPsYO81ZDQiZfTnuNXZvfxo6POWeebx5LLcDWeoyHbi3n6c8s562vnMvHpvlInDAd24xLYddzvTPQhzAjP4Wpuck8v6WaNR/UU6AaiMksHvqJfcwuSOMNV4kpeNfsCDzQeBCdMZGvPbWNSp2Pg25sLYdRSnHPlTM5y7GDrb5JJLuq4JEroa3PcNpWa+DscCJoAKZfCl/5AL5ZC3dvgzvXwIwrQu9bWA7J+Saypn6vKcbnzw0Usdsbe+/vLxqHiktZ+llQNtj+ZP/Hmg+bxuWRDDpd+SW4e4s5qeBnFeB7Yn20hh1/NZnvF36n977RLLgDfqCTGsORXgL/9ndIKwzf2oJEZwG+02W63/1nyKQAL4QQpxbJgA+fjiYAElIyWVSazuaqRnw+zQcuM4m+ILbN7OeWDnghxMmlQDLghRBCnNJ+v/Ygn3pkExf97C1e2nkcrTU/fXkv33puJ0vjDrHStp3vPvoqHV0jv6J3R3Uzswus7ua6PZAzA4fdxneunE11UzsbnIk4j1XwrWd3cLChjZf6DE3dX+tCa3oND710junU3v3WU+Bx0TjpCg42tAUGZmZPM1niwEWz8rkybT9eFP90TeGiWVaXd0wClH+c+e61XGF7B4Dy5SbGhtQC6O4gtquZRSUZlGQlYmushMxJMPsa8x3j4NvD/h0opbhyQQHvHWzkqc1HKLU7icssHemvkjmFqaztsAadHtnYs103HmRnewZr9zdwzvLlZmPDAQAS67eTql10nvFp1I1/Nt3jj1zVO9K61fqdD7cA7+eIg4xSU1AfqJvfZoOZl8P+f8FhE+ND/jxTyI5Lg/am3vu3NZjo7Zj4/sdKyYOys00Bvm9CYM8A1tn9nzcQpcAe03tbWpG5gsJfgK/dZfL/Z64a/nGjQVJO4LtwFH8njs4CvKc1kP8OUoAXQohTjWTAh43HZbo2UjKyWVSSQWtHNwfqXHzg7KJVJ5Bjs/59lAgaIcRJppR0wAshhDi1/Wt3DSWZicTabXzqkU2c/aM3uO+1/dy4eAJ5vhoAJjvf4Af/3DPwQV78f/CXm3p1hrd0dFFZ72ZuYZppKm2qgpwZAJw5KYsrFxSwrj6RLN3Ib26ay/LJWWw9YhVkn/ssPPdZ9tWYz/VT81J6jjspJ5kZ+Skk7n0WknJZ55sFQPlEK9Yke5opQHu7sdkUlyfvY4evDLcthfNmBM2GOuMTYLPzMcerdKWWoPxXzfoL0S3W4FRvt1l7RhlMvRBiU/rF0AzlivkFAHxwvIkc7RxVR/GcgjSqyaYzLjtQgPd242uq4s26JG5fWcYFK/0F+P3m9sBrgGLJedfClAtg9SNmiOvWxwIH7umAH0YEzWjMvMIMPH3nfpPqkTXZbE9I62ms6uGu75nrFdK81aY4fuS93ttrd5rb3BkntlZ7jCnC+wvwu/8GKJNJfyoJjqDxxw5F4XfiKC3AuwP572AK8N5O6O6M3JqEEEIMn2TAh01dnYmWyczKYVGp+aC96VAj+2tdNJFKYrc15MddZ/IF+3Y2CCFEmCgUXq8U4IUQQpya6l2dbK5q5JpFhfzz7g/x3Stn0+X18bnzpvC98zNQ1lW8t2Vu5+F3DvLG3lq01jS6PeyracXdaRXc978Ce/8Br/5Xz7F3Vpsu6zlFaT0d6cEF0u9eNYfzli4G4KKibuYXp7PnWKvptD/wOlStZ19tK7F2GxOzEnut+6qZKSzo2EDb1Mt571Az8TE2ZhdYsSbZ00zzU+NB6HSR7tzKgeTFfGhqNhlJsYGDpE5Azb4GgJiSM4K2W8Vxf2G6+bC5mjlzkumcn/FhE6nS7Rn277k0K4n5xenk0oQNrynyjtD0/BTsNhuV8TPxWQX4v615D7v2klU0jf+4bCYqKdvEu/gL8PtfhYIFgYGm0y42769qXeDArccBBcl5I17TsJSuMN/JGitN3ro/xiUho38ETVv94FEpMy4HR7zJ/g9Wu9vkuYcjnz2jtHcBvmQZJOcO+pSok5RtGrm72oM64KNvcKwj0gsIqdMFccEd8KmB7Y64yKxJCCHE8EkGfNjU19dSCOTm5DMpO4n0xBg2VzVypLGd9pg0VJt1lt9dG5Vn+oUQpxHpgBdCCHEKe31PLVrDBTPzcNht3LxsIjcvm2gePPCauS1ZRtnhDZTn+PjUI5sA6Ow2A8jtNsWcCck85TyIik3F/s7PoWAhzLmWndYA1rmFaSaCBHo64AFS42NYMGcObAKajzC/aBrdPs3eQ9XMb6mGmEQ+ON7KpJwkHPbevbJXJW4lXnXxz9iz2HigkYXFGcQ6rH1yppvb+r3QWInydXH5lTdw2aTF/X8BSz8N25+A4qWBban+Dnhr0KvTGgmTacW/zLkWtj1mfj/TLxn27/qjZ5bwz4bN4APSRp4BHx9jZ9aEVJ4/XsBXY97m4u89S7ZrH1fEwrXnfwibzRoCmzXFFOA7mk2n+MovBg6ilCkoH1prYlyUMicaknPBfpLKofYYmH4ZbPmziavpeUPp/SNo3PWDR+HEp8K0S2DnM3DJ982xO1qgaj3kzQrPejMmwt5/mqsoanbAxd8Pz3HHUpJ1wsBdFyjAh8rVj7Ao7oAPXHJDnPVz5+gGYQghhBhjkgEfNs1O8yGiID8fpRSLSjJ6OuC74zMDg2ZcdYEPH0IIcRKYDHhfpJchhBBCAPClx7fw8YffG3pHy6u7a8lPjQ/ktAdzVprbFXejtJdfltdwzaIibl0+kW9fPot7b1jAZ86ZTKG9CYf2cK/venTRmfDcXVCzi+3VzUxIiyc7Oc7kv9tjTYxLMH8nePMR5heb7uXD+7aYbV1tHKs51it+xi//8D+pVdn86kAWu461cIY/fgYge6q5rd8HFW+APZbYsuVmwGpfhYvgk6/D4lsD25LzAQUtVgd8o/V7yJxkbiedY7q3dz4d6lc6oNXlxTx09YTe73uEfn3zYpafbYr+1+bVcE2Z+Y4Zkx30e82aYorHlW+B9poBosFKlpqie9Mhc7/1+MmLn/GbaQ1pDS7AJ6T3j6Bpaxi6U3veavN9r+IN0+T2xC0mLmjZZ8Oz1oyJpmi97XFr7ZeH57hjyd+E5q4zvytHQu9UlSgRnR3wnlbrPwKWngK85MALIcQpQTLgw8bdbDrcUzPMh7PFpRm8tsfE0tgyc6DN+tDurjOXOQohxEkiGfBCCCGixcaDTp5+33Rtb6h0sqQsc9D9O7q8vPVBHVcvLEQp1X8HZwXY42DqxZBeQs6Rl/j+Rz/Rf7+ptfAH2NSWzeal/8fiF6+Cxz/KXs8PmFNoRZ/U7oGsqf27rP1xL81HyE+NJzcljtbDGwKPN1czLbdPZ3ObE/a/StWE69laYWpi5ROD3mt8mqmf1e2Dmu1QfCbE9o6w6aVwUe/7jlhTwGy1MuCdlSb2xF+Tc8TC5POh4s1AF/kwqeYjvd/3CBWkJ1Bw9oXwjuKOSU5TgD7m6H28rCmmeLznBTNLsnhJ74OUWjnxh9aZYnPr0VGvZ9imXAiX/gjmXhfY1rcDXuuhM+D9x4pPN+9xx9NQ8Tpc+QtzYiQcMiaa2/d+BxMWQHpJeI47lnoK8PXmT1LOiP6ejpXo7YCP6zOEFaQAL4QQpwrJgA+bTpeTbuxmiA+wsCS957H4tOCBM7WnXl6fEOKUYjrgpQAvhBAisrTWfO+F3eSmxJGZFMsDb+wf8jnrKxpo83i5YOYA2d+NB03sis0GM1eZjuOO5hD7mQ7xOkcBj+/pgqseAGcF8xr/ZeJnwHTAhxqQ6Ygz2ePNh1FKMa8oHVv93p6H85Wzfwf8nr+Dr4vsZTcBYFP0zIXqkTMNDq+H49uh7Owhfxf9pBYEhrA6K0znvi2oXFi6HFzHA93xw9V8xJwgiA9xxcFwxaVA7iyo3mj+N0or7n1iwz/kdMdTUHZW/3lYOTPNGvw58GPRAW93wJl3BGqZEMiA19bnKI/LzLocqgPeEQuzroTtT8LWR+Gcb8DCj4Vvrf4CfFsDzFoVvuOOJf/v0B9BkzTESY0Iic4CfKerzxBWfwa8FOCFEOKUIBnwYeNra6LdntJzFn9+UTp2K/MwNSsfuttNN0VHs0TQCCFOMiUd8EIIISLupZ01bK5q4ksXTuPjKybyxt66ngz2gby6u5aEGDvLJg9QnPMXnsFEiHg98MErofezOVgwZzYvbD9OR8k5tKVP41b7S8wpSDENpU2HeuW/95JWZArTwPyiNHI6DuKz8qoLVANT85J777/jKcicROnsZUzOSWJ2QRrJcX0667OnBbLbJ422AG9F0DgrA/EzfqUrzO2hd0Z23OYjo8p/76doMRzZaE4AZPaJ9cmaYm69nf3jZ8CcSCheagrw3i5ToB0sd/1kSUg3V4l3tZn7/iaq4WSVz7/R3C78GJz9tfCuKzgmaebpUICvj9q5aNFZgO+XAW8K8FsPVKG1fOgXQoioJxnwYdHu8eLoaqE7NtA1khTnYEZ+CrF2G2lZVvdGndU5E4XT3oUQpw+lpANeCCFEZHV5ffzwxT1MzU3mI4uLuHnZRJLjHPzyjQMDPkdrzau7a1g5NTt0NrrWvQvPRUtMp/qu5/rv66yE9BKuWlyKq7Obl3fXsjl/NbNth1jIXpPFDsMrwBenM1VV48xZgg87hbZGSjOD4mPam0y2+exrUDYbv765nP+7YUH/Y2Zbg1hjU6BgUf/Hh5IywQxh9flCF7lzpkNC5igL8KPLf++l6AyTn35sW6Bj2y9zcuDnUAV4MDnw9fugZqe5f7I74EOJt65i9sfQtJmY0WF9fytdBp/dAFfcF/5olYQMU3PNmRGYJ3CqiU2CmKTeETRRKPoK8FqbSzF6dcCbYvyTa3fxxt66CC1MCCHEsEkGfFgcbHCThjvwgc3ykcVFrFpQgD3Z+sBWt9vcSgSNEOIkUkgGvBBCiDHWchRe/Q50dwLw2HuHqah387VLZuCw20hLiOGjS0t4YfsxKuvdIQ+x61gLR5s7+EhhM/z143DfIhMH4td63FxV6i8822ww43LY/y/oau99sEZTqF9alkVBWjxPbz7C3/RKWkgiY8fvTf47DFKALzaFaa2Zl+ug2FbHAVVKoz2TqfHNOOxBZbqG/aB9UFQOwJTcZCbnJPc/pr9wOnFF/9z54UgtMAXuxkro7uhfgFfKxNCMuAB/ODwF+ELz/tHe/gX4uGRIKTDbsyb3fabhz4Hf9ay5jVQHPAT+3o2kAx7MSRBbiJNHJ0opWHG3ibY5lSVlg6vWdMEPlasfIdFXgPe4AR0yAz6Zdv78blVk1iWEEKchpdQlSqm9Sqn9Sqmvh3i8VCn1qlJqm1LqDaXU8D5BSQZ8WBysd5Oq2nAk9i7A37aijB9fNz/wgc3/QT9Kz/YLIU4TCrw+X6RXIYQQYjzZ9DC8/RNY/0ve2lfHT1/ey5KyTM6fGWg8uX1lGQ67jV+/2b8L3ufTvLV+Pb+O+SkXv30N7HoenAfg4JrATv5s8+DC85QLTFzI0S2BbVqD8yBklGGzKa5eVMhb++p4o9LNutTLzLEPvAa2mP4xLn5pRea47Y2ktx0E4P32PI76MimNaey9b4OVbZ85QGHZL2822BxmzaORWmBuD601txll/fcpXW5+T/6s+KF0ukxRPxwDT3OmB1Iy+hbgAZbfBWd9deDnFyw0A3Z3PmPuR6IDPsHK7e/wd8BbBfhoyCs/68sw+6pIr+LEJOWYv5/ezqj9ThylBXh6d8DHJODFTrJq57U9NRxrbg/9XCGEEMOmlLIDvwAuBWYBNyqlZvXZ7cfAH7XW84DvAN8f1sElAz4sKhvcpOImPnWAD2b+s/v+Dvgo/bAhhDg9KBTdXumAF0IIMYYOvA5A+2s/5IsPvUJGUizfu3oOKiiKIzclntXlRTy56Qirf72OH/xzD3/fdpTv/n0Xy3/wGou2fJuzHLtMfvYXd5hi7KF1gddwWgX44MJz4WJze3RzYFt7I3Q29xTqr15YhE9DTUsn1dM+arrVtz9hOtIH6kT3d4Q3H+6JkXy5LoND3Rnk6obeoe1BlQAAIABJREFU+zYcAGULXXQOlpxrIkrKPz74fgPxF+APWgX4UCcP/F3kw+2Cb6k2t+HIgLfZoXCh+TnU72LZZ2HhRwd+viMOCheZIa4QmQ74vhE0I+2AF4NLzo36prQoLMC7zG1wBrxStKsE8uO60MBjGw5HZGlCCHGaWQLs11pXaK09wGPAlX32mQW8Zv38eojHQ5MImrCorHOTbmsjpk8HfA9/x4T/w4ZE0AghTiLJgBdCiLGnlHpIKVWrlNoRtO2/lFLVSqkt1p/LrO0TlVLtQdt/FbmVh0FHM7p6E6+wFIe3g4dKX+GFz3+IKbkp/Xb9ykUzuG35RDq7fTy4poK7Hn2fR9YdYmFBAuWOAziWfBzO/X+m+7moHKqCCsnOClB2SC8JbEvJM93b1Zt77wc9hfopucnMLzaf00snz4Lpl5rHB4qfgaAC/BGo24NXOdjWlsFRnUVKZ43psu95vQNmTY7YoX9XWZNHH1GSEtQBb3OELprnzYXY5OEX4Jutul04ImgAis8c3smIgZQsM7c2R2SK3v4ImuAOeEd87+ZjMXpJ2eBpDfwchUYRDnWS+Qvwcb1zrVwkUJzUzdlFOTz2XhWfO29K72wsIYQQI1UIBJ/RPAKc2WefrcA1wL3A1UCKUipL697tGUqpO4A7ABZPsEkETZgcrHdZGfBpoXeISzNfFlzHISZRPsAJIU46KcALIcSYexi4H/hjn+0/01r/OMT+B7TWISZ1nnq8B97Crr085biMxTNmMn/nH8D5Fcjre9EupCXG8M3LzfaOLi/7alqZmJ1Eas1G+H0X9rLlgZ1Ll8PbP4XOVhN53FgJ6cVgj+l90IKFvTvgnf2jam5aUsyeYy3MK0qH2Dtg7wtDFOCt4nbzEajbiydtEt3tDo7rTOy+TtNln5hp9mk4MHT8TDikWh3hzYdN93uo7n27wxTBq9b1fyyUpjAX4Jd/DiadO/D3oqH4C/DJ+Sbjf6z5I2h6MuAbzImAcA9VHa+Cu96jtAAffRXsTn8HfKCI4PNpmnwJpNs7uWlJCTUtnby2pzZCCxRCiHHly8DZSqn3gbOBaqBfdV1r/RutdbnW2kzIkQiasDha34SD7kDHRF82WyCGJkovtRNCnD6UkiGsQggx1rTWbwHOSK8jEva+8zxuHcfVq64i87Jvm2L5y98c8nnxMXbmFaWTGh8TKBgXB/UZlSwzAz0PbzD3nRWhc88LF5nH/EVTf1Z8UBf26vJi1n/jfHJS4mDSOfDhn8CiWwZeXGKW6XxuPgx1e4jJn4ndpqizWUXD5iPmVmtTgB9osGg4xaVAXKr5eaDsejAnLmp3Qdsw/jrufMYUu/3xNicqPs0MmR2t4iWAikz+O5iUD2ULRNC01UdH/vvpolcBPjq/F0dfAb4nAz5wSVG9u5NWHU+qaue8Gbnkp8b3Hsba1QGetjFeqBBCnPKqgeDrC4usbT201ke11tdorRcC/2FtaxryyL7uMC5zfGrt6KLbbX24HazTQwrwQogxolDSAS+EENHjLqXUNiuiJiNoe5lS6n2l1JtKqQ9FbHUj1O7x4gv6N6a6qZ3EI29zIHE+F80tMV3hZ38NDrwK+18d/oGr1kP2tN5dscVLTDHUX5x3VoYuPBcsMrdH3w/sl1IAMQk9uyilyEiK9d+BMz4R6CgPRSnTFV6/HxoP4sibyfS8FGIyrE5x/5BTd52J1BiLDngI5KKHOhHh58+B9//eavfAnz4SOJHhd/R9qHwTln569LE44ZaQDiVLIXdmZF7fZjPf6TqCMuAl/z18gr8LR+nvNQoL8FZmT1AH/NGmDlw6gUTdhsNu44Ylxbz1QR1/fm0Tb/zy87R+bzKVP1zJH9ZWUtvaEaGFCyHEKec9YKpSqkwpFQvcADwfvINSKlsp5f+34hvAQ8M6smTAn7CD9W2kKuvkcvwAHfAQKMBL/rsQ4iRTQLfPF+llCCGEgF8Ck4EFwDHgJ9b2Y0CJ1TzzJeBRpVRqqAMope5QSm1USm2sq6sbizUPqKPLy7IfvMql977NyzuPo7Xm50+9ykR1jOLyDwcGrp7xSXAkDL8A7/PB4fWm8BosLgUmzDeDWNucpiiaGaLwXGAN/qzeZG4bK0PvN1JpRXBwjRnamjOd718zl9svs86VtFgd8A0HzG3WlBN/veHwd6oP1gFfsMgaYPsO7HsZfncB7H8FnrnTNMb6rb3XdNSX33Zy1zxSNz8DH/5p5F4/Pj1wNUVbfdRGpZyS/L/L2BSIiY/sWgYQhQV4qwM+KAO+urEdFwnE+0wh4obyIv7D8WeuffNizjr+R2odEyjrPsDr/3iUpd97lVse2kC9q7PfoQ8726isd4/J2xBCiGinte4G7gJeAnYDT2itdyqlvqOUWmXtdg6wVym1D8gD7hnWwSUD/oRVN7WRhhXLNlgHfJJ0wAshxogMYRVCiKigta7RWnu11j7gt8ASa3unf1aT1noTcACYNsAxeiIkc3Ii+zlyf62LprYujja3c8cjm7jwZ29BxesAZMy9OLCjIxZypkHd7uEduH4vdDQH8r+DlSyH6o1Qt9fcD1V4Tkg3BfDqoA74wTrEhyutKNB8mjOD+cXpzJ42xQwIbbYuSG7Yb26zBimIh1NPAX6Q9xcTbwbYbvkz/OV6yJwIV/7CDItdYxW2nRWw6zlTfB9tXvvJEpMwvIG2J0tCRiCCxp8BL8LD/104ik9qRF8BPkQG/NGmdlp1IjHd5rH83Q/zCfs/6J6xCj77LpO/vg5SCnigbC13nTuFDZUN3PrQBlo6Ah2Ymw41ctm9b/OJP7w3pm9HCCGimdb6Ba31NK31ZK31Pda2b2utn7d+/qvWeqq1zye01v3PboYiGfAnrN7lCXTAD5QBDxJBI4QYM6YDXgrwQggRaUqp4IyTq4Ed1vYcpZTd+nkSMBWoGPsVjswHtaYY/cSnlvG/187F3dnNh5P2oFMm9B9omjPTRJ/01XQYXv5W705sf1RK3w54gNJl0N0BO5829wcqrBcsMoNYPW5wHTdF5xPlH8Sq7IGMd5vdxMD4I2icB0xBPq3kxF9vOIbTAQ9QusJ0cc/4MHz8JVj4MZi72gy1rdsH635h1n3mp0/+mk81CenmaouuduhySwZ8OCVZV4NLAX4EejLggzrgm9rptCehOluhZhf8679g+mUk3/AgttzpZlL1mZ8isXotX5rbyS8/tpi9x1v5xB820tHlZUOlk1sefJe2Li8H6twhu+OFEEKEkWTAnzCn20Ma1r+Jg0bQWB8yJIJGCHGSKaXweqUAL4QQY0kp9RdgHTBdKXVEKXU78EOl1Hal1DbgXOCL1u5nAduUUluAvwJ3aq2jfoDrvhoXMXbFlNxkrj+jhDVfOZuV9p2oSeeazPRgOdOh9ajpbA+2/Ql45z7Y/IfAtqr1pjAXqrju74rf/qS5DRqs2kvhImg9Fijmh6sDHkyx2xEX2J5aCC3+DvgDZk12x4m/3nBMPs/8Ger9rfg83PAoXPfHQOPsxd8zPz97J7z/J5h3/eA5+ONVfLrpgHfXm/vSAR8+iZmAiuqmtCgswLeaTCl7TM+m6qZ2bPEpqO52ePqTJq/rivt6/4d48b+Zov26+zl3ei4/WT2f9w46ufnBd7n1oQ3kp8Xz8xtNftemQ41j/KaEEGKckQz4E+Z0e8iNtTp4hpMBH8Vn+4UQpw/pgBdCiLGltb5Raz1Bax2jtS7SWj+otb5Zaz1Xaz1Pa71Ka33M2vcprfVsrfUCrfUirfXfIr3+4figppWy7CRi7KZEZa/ZhmpvhMnn9t/ZP0TTHx3jd3SLuX37J+CxriKtWme63/sW8cF8ds6ebrq5UyZAbGLoxfkHse54xtyGKwMezMmEYKkFgQK8s2Ls8t/BDFi9+ZmhI1riUkz3uy2onJicAxd+x2Tld3fCirtP7lpPVQlWBnybVYCX72/hY7ObE1iphZFeyYAiX4A/ugXuXxK4zKbT1Sv/HUwEjSPRyo6q2QFX3m/+Dx4sIR0W3gw7noLmaq5cUMh3Vs3mvYONFGcm8NgdyzhvRi6xdpsU4IUQ4mSTDPgTVu/qJC/GX4APOTvL8H9wS5IOeCHEyaUkA14IIQSYz/q/XAEPXQpb/hIoeI/SB7UupuamBDZY+e9MOqf/zv5Imto+OfDHtpqOclcNbHzQZKk3VYXOf/crtR4bLHZlwjwTFbP7b0PvO1z+CJq+8TpphaY25vOZDvjMySf+WmNl4c0w43LTHJs9NdKriU4JGebKDemAPzlufgbO+UakVzGgyBfg6/aYwRjv/8nc97h7xc+AKcDHJ1vdf4tuhemXhj7W0jvNFOkNvwbg5mUTefLOZTx553JyUuKIj7EzpzBVCvBCCHGySQb8CXO6PeTEdJh/E4OuCuunZBlMv8x8ORBCiJPIZMD7Ir0MIYQQkdbeZJojj20xsSM/mQGv/c+omnDaPV6qnG1MzbPqQJ42Ux/Knxc6YjG9FGISTS3Jr80JTYdg0S0w6VxY8zM48Kp5LFT+u1/pCnM7WOxKTALkzYLOZnNVakLGyN5gKOmlpmA955re21MLTS59zQ7obh+7AazhYLPBDX+Gy38W6ZVEr/h00F5oPGjuSwd8eOVMi+pc/cgX4P1Fms2PmLN8HlevAnybp5vGti5aC8+CZXeZbKmBZEyEmatg48M9eWBnTMwkLSFQuCifmMn2I810dEl3phBCnBRKSQZ8GDjdHjJtbRCfNviOaYVw41+G3k8IIcLAp8EnXfBCCDG+tVux8lfcC//2Akw5D976ETx6PXS0DPi0fTWtbDvS1GvbgToXWsO0PKsD/vV7TPzKRf8T+iA2G2RP690Bf2yruZ2wAM77JrQ1mIGsMUmmkD+Q0uXmNnuIqBd/DE044mfA5LpfeT/kze693R+fUfmWuR3LCJpwCRX3I4wEq7G4Yb+5TYzeYrEIv8gX4P05wc1VUPmGKcAHRdAcbWoHID2vFC6+p188TT8rv2jOTK69N+TDi0sz8Hh97KhuDvm4EEKIE6UkAz4M6l0e0m1tg+e/CyHEGFLWl2qvlgK8EEKMa20N5jYxEyaugOsehsv/z0THPHihiU85vh02/Bae/QysvY/GYwe56bfrufORTeigf0f21bQCMC0v2QxNXfcLKL8dJp098OvnzuzdAd9TgJ8PReUw9WLoaDI/DzbENK0IbnkOyj8++PsttArw4RjAOpi0PgX4UymCRgzNf/VEw36wxUgD1TgT+QK81+qSdMTD5j+aDHj/JGWgusnk3xZmJAzveAULYO515j/azdX9Hl5UYv7CSwyNEEKcLEoy4E+Qz6dpbPOQgls+mAkhooa/p01y4IUQYpxrszrggzt4y2+Djz0Nrcfh54vgVyvhhS/D3hfglW+R9usF3Nf5bUpbN3Ggzt3ztA9qXcTYFaWpyhTr04rhwv8e/PVzpkPrMROFAyYKJ73EnBAAOPf/mVt/xMxgJp0z9OftcHfAD8TfAX/oHVMji+KBkmIU4oM64BOz5GqBcSbyBXh/l+Sca2H336H5SK8ImupG0wFfkD7MAjzAed8yWfCv9b9kKScljolZiWyUArwQQpwcSkkG/Alq6ejC69Mk+VyBSxWFECLC/N8Tu6UAL4QQ41tPB3yfCI1JZ8MnX4MP/Ttc/Ru4ext8tZIXz32B+7qvZlZ8Aw/E3MvavUd7nvJBTStl2UnEvPl9cB4w0SxxKQwqZ6a5rdtrbo9uMfEzfgUL4BOvwrLPnuAbteTOMvnys64Mz/EGkpQLNgd4Wk23vS3yJTsRRv7vdU1Vkv8+DkX+/83+Ik357aYY7zreqwB/tKkdu02RlxI3/GNmlMKZn4KtfzGXPfl1d0JXB4tLM9l8qLHXZU9CCCHCRTLgT1S9ywNAgtclHfBCiKjj9cpnaCGEGNf8BfiEzP6PZU2G878N86+HjFKONLXz5ddcvFN0BynX3keGctG0/cWe3ffVuDgz0w3rH4DFtw0ePeOXO8Pc1u02XfCNlSZ+JlhR+dARxsNld8Cqn/d/jXCz2SClwPycJfEzpx1/B7z2Sf77OBQ9BfgJ86Cw3PzcJwM+PzUeh32ES/3Qv5uixcvfMmeXXv4W/HgaPHwZi0vSaXB7ONjQFqY3IYQQwq9ba8mAP0FOtynAx3a3SAa8ECJq+DPgu32+CK9ECCFERLU7wR7XKz44FJ9P8+Unt6K15ier52Ofej5uezrTa/6Bp9tHu8fL4cY2VnW9BCg46yvDe/20EohJhNo9cHyb2VawYPDnnCr8OfBSgD/9+DPgQTrgx6HIF+D9RRqbw1zSA30y4NspSI8f+XETMuDsr5khIP83z2TCp5dA9SZWJlQCsPGg80RXL4QQog+fRjLgT5DT3YkNH44u6YAXQkQPyYAXQggBmA74xMwhM6wfe+8w6yucfOvyWRRnJoI9hoayyzmXTWzdf4gDdS5idBfzap+D6ZcGis9DsdlMDnzdbhM/A70jaE5l/tx3GcB6+olNMrVPgEQpwI83kS/Ae7vMX0ClYM415nKb7Ok9D5sC/Ajy34Od8QmYfQ2suBvu3gq3vQCxyRRXPE5qvEMGsQohxEmgUfgkA/6E1Ls8pGBdpSUZ8EKIKOEvs0gGvBBCjHNtziEjNGpbOvj+P3ezdFIm159R3LM9a/ktxKku6t97kn01rVxqe5c4TyOccfvI1pAzw2TAH9sKqUWnT0dxqkTQnLaUClzdfLr8fRXD5oj0AvB1gT3W/ByXAl/c2TNowuvTHG/uoHC0BXhHLFz3+97b5n4EtfVxVhZfJ4NYhRDipFB4u7ui4Azvqcvp9pCq3OaOdMALIaKFVYGXDnghhBjn2pymA34Q//33XXR2+/je1XN7IswAksqWcNReSGHV33ghexW3OP6FzpyMKjtnZGvImWHm/h1cY/LeTxfZU02TalBjqjiNJKRDW71kwI9Dka+PeLvBFhO4HzTlua61k26fHn0HfCiLb4Pudm6IXcv+WheNVs6uEEKI8NCAr1v+23oinG4PBXGd5o5kwAshooTCnwEvBXghhBjX2hpCD2C1vLanhn9sO8Zd505hUk6fQahKcajwcuZ4tuPb+08W2/ahzri9Vy1oWHJnmlvX8ZM/HHUszb8RPr0OknMivRJxMvhz4KUDftyJfAHe12UmSodQ3WQuvx91B3woBQugYCHlDc8Dmg2SAy+EEGGlrQ54MXr1rk4K4/0FeOmAF0JEB9XTAS9DWIUQYlxrHziCxt3Zzbee3cmU3GTuPDt0jErKkpuwKc1nGn+ER8XBgptGvoacGYGfT5f8dwB7DORMi/QqxMnib66SDPhxJ/IFeK+ndwd8kOqmDoDwdsADLL6NxKZ9LHXsZ31FQ3iPLYQQ45zpgJcC/Ilwuj1MiLOuIpACvBAiykgHvBBCjGM+L7Q3DliAf3HHcaqb2vnOlbOJdYQuOc2YOY8tTCNdufkg9+JAV/BIpBVDTJL5ueA0KsCL01uCZMCPV1FQgO82Z/hCONrUDkBBenx4X3POtRCbwmdS32bdASnACyFEeCl83u5IL+KU5nR7yInxd8CnRnYxQghh6RnC6pUCvBBCjFsdzaB9A2bA769zEWNXLJk4cESNw25jR87l+LSiafYto1uHzQY50yFlAiTnju4YQow1/8km6YAfd6JjCKstsIzfvlVBvbuT7KQ41nxQT2q8g5T40AX6UYtLhnmrWb75Txxx3oDT7SEzKTa8ryGEEOOURqG90gF/IupdHrJzzFVgxEkBXggRHZQMYRVCCNFmxfgO0AFfUeeiJDMRh33wfs/YM/6NC54t5Pezl49+LWd9GTpbR/98IcZaxkRThB/NVR/ilBb5Ary3C+ym+H20qZ17XtiNUqCtz/WLS0/SX8rJ5+LY+CClqpYNlQ1cMmfCyXkdIYQYZzTgkwL8qPl8msY2Dxl2qwM+LiWyCxJCiB4yhFUIIca9NitFYIAhrBV1bsqyk0M+Fuwj5SXMLV5NaVbS6Ncy48Ojf64QkXDGJ2Hu6pEPHRanvMgX4H2BCJr3q5oAePYzKyjLSaLBdRI705PzASh0tLDugBTghRAiXDTKxIuJUWnp6MLr06TZ2iE2GWz2SC9JCCEA6YAXQghBoAAfIoLG69McamjjvBlDR8LYbIqZE+RKTzHOOGIhOSfSqxAREPkCvDcQQbO5qpE4h42ZE1KJddhIDXf0TDArI6w8u4unKpwn73WEEGKc0YD2SQF+tOpdZvhqCm0SPyOEiCo9GfA+X0TXIYQQIoLaB46gqW5sx+P1MSnnBLrahRDiNBT5ax58XUEd8I3MLUwbcFJ2WFkF+HlpHeytaaXB1XnyX1MIIcYFZU6uilFxuk0BPkm7ZQCrECKqSAe8EEKIwTrgK+pdAMOKoBFCiPFkWJVupdQlSqm9Sqn9SqmvD7DPaqXULqXUTqXUo8NegbcLbDF0dnvZUd3CopOV+d5XTALEpTEp0Q3AeumCF0KIsNAoEy8mRsXpNieE431uyX8XQkQZyYAXQohxr81p5vjF9i+yV9SZ+op0wAshRG9DFuCVUnbgF8ClwCzgRqXUrD77TAW+AazQWs8GvjDsFXi7wO5g59EWPF4fi0rSR7L+E5OSR7ZuIinWzvqKhrF7XSGEOI1pQEkBftT8ETSxXrdE0Aghooo/gsbrlQK8EEKMW20NkJBJnctDt7d3JFlFvYuUeAdZJ2uWnxBCnKKG0wG/BNivta7QWnuAx4Ar++zzSeAXWutGAK117bBX4DMd8P4BrAtLxqgDHiA5D5u7lvKJmayTArwQQoSJQmkpwI+WP4ImpsslETRCiKjij6CRDnghhBjH2pz4EjM578dv8Ls1lb0eqqx3MyknGeX/B0MIIQQwvAJ8IXA46P4Ra1uwacA0pdRapdR6pdQloQ6klLpDKbVRKbWxrq7ObPR2gT2WzVWNFKYnkJcaP/J3MVrJueCqYdnkLPbXuqht7Ri71xZCiNOVUtIBfwKcbg8p8Q5UZ4t0wAshoowpqEgGvBBCjGPtTtod6bR2dvPant69lxV1biZnS/yMEEL0Fa5ppw5gKnAOcCPwW6VUvywZrfVvtNblWuvynJwcs9HXDXYH7x9qZOFYxs8AJOeZAvwkM71bcuCFECIcFEp7I72IU1a9q9NcttvRIh3wQoioEuiA9w2+oxBCiNNXWwMtyswper+qkXaP+dzf5unmWHMHZVKAF0KIfoZTgK8GioPuF1nbgh0Bntdad2mtK4F9mIL80LxdtHttHG3uGNv4GTAd8B4Xs7NtJMba2XyocWxfXwghTkdKYZcImlFzuj3kJtmhu1064IUQUaUnA1464IUQYvxqa6Deawawdnk1Gw+ZRsbKev8A1v7DWYUQYrwbTgH+PWCqUqpMKRUL3AA832efZzHd7yilsjGRNBXDWoHXg7PDfIgf0wGsAMn5ADja65lTkMa2I01j+/pCCHEaUtIBf0Kcbg8FCV3mjhTghRDRRDLghRBifPP5oL2RY11JZCfH4rAp3jlg5ulV1PkL8NIBL4QQfQ1ZgNdadwN3AS8Bu4EntNY7lVLfUUqtsnZ7CWhQSu0CXge+orUe3lRTXzf1bV5iHTZmF6SN6k2MWnKuuXXVMq8ojZ1HW+jyyiW1QghxQqQD/oTUuzwUxFsFeImgEUJEESUZ8EIIMb51NIH2UdURx8wJqSwoTu8pwPs74CdmSQFeCCH6cgxnJ631C8ALfbZ9O+hnDXzJ+jMy3i5q3D7mFKQS6whXJP0wJeeZW1cN84pL6FxTyd7jrcwpHOMTAUIIcVpR2JEC/Gj4fJrGNg+5sdIBL4SIPko64IUQYnxrM3EzH7TGMXl6MqnxDu5/fT8tHV1U1LkoTE8gIdYe4UUKIUT0GeOKd3/aZwrwi8Y6/x0CBfjWGuYXmaL7tiPNY78OIYQ4jSilcOAFLQWakWrp6MLr0+TEdJoN0gEvhBgBpVS6UuqvSqk9SqndSqllSqlMpdQrSqkPrNsMa1+llLpPKbVfKbVNKbVoyONbt165YlQIIcandlOAr+lOZHJOEssmZ+PT8F6lk4p6twxgFUKIAUS8AO/r7qLTZxv7AawAiVmg7OCqoSQzkfTEGMmBF0KIE+VvkfRJDvxI1bs8AGQ5OsyGuJQIrkYIcQq6F3hRaz0DmI+Jj/w68KrWeirwqnUf4FJgqvXnDuCXQx5dOuCFEGJ8azNxM06dwuScZBaWpBPrsLF2fwOVdW7JfxdCiAFEvACPt4tuHMwqiECXn81mcuBdNSilmFuYxlbpgBdCiBOiegrwXZFdyCnI6TYF+HS7vwAvHfBCiOFRSqUBZwEPAmitPVrrJuBK4A/Wbn8ArrJ+vhL4ozbWA+lKqQmDvoZ1KxnwQggxTlkRNE5SmJSTTHyMnfLSDF7YfozWzm4mSQe8EEKEFPECvNLddGEnMyk2MgtIzgVXLQDzi9LZV9NKu0e6NoUQYvRMicbXLQX4kXK6TfRMmmo3G+JlJokQYtjKgDrg90qp95VSv1NKJQF5Wutj1j7HASuDkULgcNDzj1jbelFK3aGU2qiU2thQXw9IB7wQQoxbVge8JyaNvNQ4AJZPzuJ4i2keKctJjtjShBAimkW2AO/zYdNevNhJiRvWPNjwS84DVw0A84rS8Po0O49KF7wQQoyWspkCfIenM8IrOfX4I2iSaTMbpANeCDF8DmAR8Eut9ULATSBuBgCttQZGVD3XWv9Ga12utS7PyckBpANeCCHGrbYGunGQn5PTc9XrssnZPQ9LB7wQQoQW4QK86Y60x8Ris6khdj5JrAgagPnF6QASQyOEECdAWR3w7R1SgB8pfwRNgnaDIx4cEbo6TAhxKjoCHNFav2vd/yumIF/jj5axbmutx6uB4qDnF1nbhiQd8EIIMU61O2kihcm5gTlF84rSSIq1E+uwUZCeEMHFCSFE9IpsAd5rCvCxMREsMCTnmQgan4+81HjyUuNkEKsQQpwAfzdMZ6cU4Efhx4GaAAAgAElEQVTK6faQEu/A4WmVAaxCiBHRWh8HDiulplubzgd2Ac8Dt1rbbgWes35+HrhFGUuB5qComgE5bAqvzxfexQshhDgldLvqqfcl9+p0j7HbWDk1m1kTUrFHqrFSCCGiXIRyXyxWB7wjNi5ya0jOB+2FdickZTOvKJ1t0gEvhBCjZxXg2zs9EV7IqedAnYvC9ATobJX4GSHEaHwO+LNSKhaoAG7DNNw8oZS6HTgErLb2fQG4DNgPtFn7DsluU9IBL4QQ41Rncx2NOoXJub2z3n903Xy6vfJvgxBCDCSyBXhvNwAxES3A55pbVw0kZbOgOJ1XdtXQ3N5FWkJM5NYlhBCnKJtVgO/o7IjwSk4tXV4fGw82srq8CFpbIF4K8EKIkdFabwHKQzx0foh9NfDZkb6Gw6bwSpFFCCHGJV+bk0aymNxn2GpqvNROhBBiMFGRAR8X0QJ8nrltPQ6Y/DKA7dIFL4QQo6Js5p8Wj3TAj8i2I820d3lZOikLOlukA14IEZWkA14IIU5jWsM/vwaH3wv5sL3DZMCXZiWO8cKEEOLUFuEMeFOciYuLhg54M49qXqF/EKvkwAshxGiong54yYAfifUVDQAsKcuEDumAF0JEJ4fdhlcK8EIIMaaUUg8ppWqVUjuCtv2XUqpaKbXF+nNZ0GPfUErtV0rtVUpdPOwX8nbBu7+CHU/1f8znI66rme64TOJj7Cf4joQQYnyJaAG+q8sU4OPj4iO3CH8HvKsGgLTEGCZmJbL1sBTghRCnP6XUJdYH8/1Kqa+HeLxEKfW6Uup9pdS24A/2gxwTAE+XFOBDqt8Pv1wJzUd6bV5f0cD0vBSykuOkA14IEbWkA14IISLiYeCSENt/prVeYP15AUApNQu4AZhtPecBpdTwKuZe6/N7UxVg5hNtPOg02zqbseMjJiXrBN6GEEKMTxEtwLvb2gGIj49gB3xcMsQk9XTAA8wrSmd7tUTQCCFOb9YH8V8AlwKzgButD+zBvgk8obVeiPkg/8AwjguAxyMRNCHtexFqtsPuv/ds8nSb/Pdlk60vNDKEVQgRpRw2hdfni/QyhBBiXNFavwU4h7n7lcBjWutOrXUlZtj2kmE902tigv0F+B+9uJfrfr2On7/6AV6XuVozMT13JEsXQghBhAvwLVYBPjE+gh3wACl5PR3wAHML0zjW3EG9S7o3hRCntSXAfq11hdbaAzyG+cAeTAP+SnAacHSog9qUlQHvkf+GhnT0/7N333Fy3fW9/1/fKTuzvRd1rXq3VdwxrjjCgA24XwwxwfYvBOKbUG4cuBcDgfsAQoBwAYNtwAnBdhzTHGJs4oYxbipusmRbdaXd1VZpe5n2/f1xzmyRVtqVNLNndvb9fDz0mJlzzpzz3cV8d/czn/P+bnUedz0+tOn1hg43/70MEgmnAK8IGhHJQOqAFxHJKJ9071L9iTGm1N02Czgw4ph6d9soxphbjTGbjTGbW1tbnY1uTHCyAH+oN4LfGP7pv9/mSw/+EYCi8pr0fCUiIlksIzrgc3NzvRyGE0MzsgCfXIhVXfAikt0m8sv5F4EbjTH1wCPAX491opG/wHd1dwEQiURTPuBsYBu2OI/7noXoAAAv7HEams6sLYdIN2DVAS8iGcnpgFcBXkQkA9wJLAROBw4C/3Qib7bW3mWt3WCt3VBZWelsjLkNNIOd0H+YroEoFy2r4n+/ZzmNjU58YmXVjJR9ASIi04W3Bfh+p/DgeQd8QdWoAvzKmU7R4/V6FeBFZNq7AbjXWjsbuBz4mTHmqJ8dI3+BLylxFrOOTucImr5DcM+lsOXe0dt72zGH9/FCYjkm1g/7nwOc/PdlNYWU5ec4C7CCOuBFJCOpA15EJDNYa5uttXFrbQK4m+GYmQZgzohDZ7vbxhcf0UDTsZ+u/ijFuUFuPn8Bt5/rNCouWrDw1AcvIjLNeNwB7xTg8/O8LsCP7oAvDAdZUJGvDngRyXYT+eX8Y8CDANba54EwUHH80zoZ8MmFtqedeBT+48+hfhO8/G+j9zW+DMCPYu8lYgN0bnt0KP/97AXJ/He3AB8qnMRBi4hMTMDnIx5XAV5ExGvGmJGt6B8AtrnPHwauN8aEjDG1wGLgpQmdND4iQrJjP539UYrCQQAW+Q9CMI9w2ZxjvFlERI7F0wJ8X78TQVPgeQRNFQx0DkUBgBNDow54Eclym4DFxphaY0wOziKrDx9xzH7gEgBjzHKcAnzrcc/qLsIai03DAry18MhnYO8zMHMdNGxxuuFdifrNJDD45p3DFpbT9fqjvFafzH8fsQArKIJGRDKSOuBFRCafMeZ+4HlgqTGm3hjzMeAbxpjXjTGvARcBfwtgrX0Dp4FmO/Ao8AlrbXxCF4oP//4eP1RHbyROca5TgKftbShfBD5Py0giIlOSpzNn/2CmRNC4i4j0tgxtWj2rmKauAVq7tYigiGQna20M+CTwGLADeNBa+4Yx5svGmCvcwz4N3GKMeRW4H7jJWjtO5cUtwEenYQb8iz90Ymfe8SnY+DWwCdjz9NDunj0vsSsxk/eeuZTQssuYE6vjO7909p9VW+YcNBRBUzypQxcRmYiA3xBPJLwehojItGKtvcFaO8NaG7TWzrbW/tha+2Fr7Wpr7Rpr7RXW2oMjjv+qtXahtXaptfZ3E77QiAiaaPs+AIpyA86GtrehYklqviARkWnG2wL8gFPc9gWCXg7DiaAB6B6xEOssp/Cx7cgYmo4Do3PRRESmMGvtI9baJe4v6F91t33BWvuw+3y7tfY8a+1p1trTrbW/H/ekyQ746RZBs/9FeOxzsOy9cPH/gVnrnSL67iec/dYSaHqZ1+xCzl9cydqLrwJgZvtzLKsppDQ/xzluKIJGHfAiknnUAS8iksViww2IicN1AE4ETaTPqYWoAC8iclI8LcAPuB3w+HO8HIYTQQOjF2KdVYwx8NrIGJrBbvjeGbD1XyZ5gCIiU8k0jKBJJODR2507qj7wI+fWXH8AFlwIu55womk6D5AXPURr0UoqCkKYqhXE8mdwkf913rmkcvhcA+7PHS3CKiIZKOAzxFWAFxHJTskImtxSTOcBACeC5tBuwELFYu/GJiIyhXlbgHc74PFnSAd8T9PwplDg6IVY23dDrN95FBGR44pPpwiabb+Axq30v/NzECoY3r7oUug+CC076N27CYCihWc5+4whsOQS/ix3B5+6ZMHwe7QIq4hkMHXAi4hksWQBvnwxwe4DgKUoN+jEz4A64EVETpKnBfjBiFuA9wW8HIbTAR8ugQOjFwZfPauY1xs6hjcccgvv3U2IiMgxuBE08dg0KcBH++GJL9FTupJVvyzhZy/UDe9beInzuOtxmrY/y6ANsPz0c0bt9w12Em55dXjbYDcYPwTzJmf8IiInIODzqQNeRCRbDRXgFxGI9lBEr5MB37YTMFC+0NPhiYhMVZ4W4KORZASNxx3wPj+suBJ2/NbJNnOtmlVMc9cgLd3uONv3OI8qwIuIHFu2FeBb34Jvr4LD+8be/8Kd0HmAnxbcTNz6uOM32/j9G+7PieJZULkMdj+BbXiZt8181syrGn7vggvB+GD3k8PbBrqc+Bn3+ygikknUAS8iksWS691VLAJgjmlzImja3oaSuRDM9XBwIiJTl6cF+EjE/XTV53EBHmD1NRDthbceGdq0ZnYJMGIh1mQHfI8K8CIix+YUjhPZsmD1zv+GzgOw949H7+ttgz9+i9jijdxZN5MPrJ3F6tkl3PbAy2zdf9g5ZtGl2LrnmNm3g/aS1QT8I3705pVBxVJofGV422CXFmAVkYzlZMAnvB6GiIikQ3IR1nKnAD/btDqLsLbtVP67iMgp8Cz7xQLxaASCeN8BDzDvPCicCa8/BKuvBmDlzKKhhVgvXlYNh5Id8M3OgnrqTpRpKBqNUl9fz8DAgNdDyRrhcJjZs2cTDGbAXJgSztxos6UDvnGr89i87eh9z30Xon08M++v6Xu9k2s3zGFxdQFX3fkcH7t3E/907WmcM/dCcp//HnlA3vwNR5+jZhXUPT/8OtkBL+IBzfGplX3zu9sBH1cHvMhUo/k9tbJxfgdGZcADzPW1kRc00L4L5r/Dw4GJyLFofk+tdM3vnhXg4wlLgLjzwusMeACfD1Zf5UQJ9B2CvDLyQwEWVhYMd8C37waM0yk/2K0CiUxL9fX1FBYWMn/+fIw+hDpl1lra29upr6+ntrbW6+GkhvvfRSIRxVo79f87adjiPDa9fvS+fc/C3HO4b3eY6qIBzqwtw+8z/MtHz+TqHz7PX9y7mVxflJdzcggTYeHpFxx9jprV8Pp/DP3sUQe8eElzfOpk5fwOBPxGGfAiU5Dm99TJ1vkdGC7AF1Qx4MtjQbAN09UI0T51wItkKM3vqZPO+d2zCJp4whJMFuD9OV4NY7TV10AiBtt/PbxpVjGvN3TCQCf0tUH1SmdHT7NHgxTx1sDAAOXl5ZrYU8QYQ3l5eZZ9Wu38t+G3cQZjUzymoLfdyX7350DTNufup6TYIDS9zmD16fzh7Rbeu2Ymfp/ztc+vyOcPn72Qf/2LM7n5wuVsD51Gv6+A8rkrj75GzWrnMdlhrwK8eEhzfOpk5/wOfi3CKjIlaX5PnWyd34HhArw/h/ZADXN9bU7+O0DFEu/GJSLHpPk9ddI5v3tagA8Qc15kQgQNQM0aJ4v3tf8Y2rTaXYi1ff8OZ8O8c51HLcQq05gm9tTKuu+n+/UESNAXiXs8mFPU+LLzuPx9MNgJHfuH9zVvg3iEzbFaonHLFafNHPXW/FCAdy6p5NOXLWXdLT8k98b7nLutjlTtFuCTHfaKoBGPZd2c5KFs/F4GtAiryJSVjXOSV7L2e5kswAdCtPirmEWrk/8OKsCLZLCsnZM8kK7vpYcF+AQBE8diwOf3ahijGeN0we9/DjoOALB+XikA+3a6hREV4EVExmUxBEyMvkjM66GcmoYtgIG1NzqvR8bQNDjZ8A82VjKvPI81s4uPfZ6KRbBgjPgZgIJKKKgZPrc64EUkg/l9iqAREclaMbcA7wvSQBXViRZoewvCxZBf6e3YRESmMM8K8LGEJYcY+DKk+z1p9VXO47ZfAM5CrAWhAIcPvOlsn3uO89ijAryIVzo6OvjBD35wwu+7/PLL6ejoOO4xX/jCF3j88cdPdmjiSvgCBEjQP9U74Bu2QOVSmHMWYEYvxNqwlUReBf9Z5+fK02ae2iflNauHI27UAS/TmOb3zOd0wE/xeDERmXSa36eIeMSp0fh87E9UkGv74MAmp/tdHbYiMgbN7xPjcQRNPHPiZ5LKFsCsDfDGLwEI+H2sn1eKbd8NRbOgoBoCueqAF/HQsSb4WOz43daPPPIIJSUlxz3my1/+MpdeeukpjU8AEyBAfGpH0FgLjVth5jrIyYfyRaM74Bu30pC3nIQ1XHH6zGOfZyJqVkHrmzDQATYOocJTO5/IFKX5PfOpA15ETobm9ykiHhlao29vtMzZ1vy64mdE5Jg0v0+M94uw+gNeDeHYlm6Eg69CTysAZy0oo3zwANHiWudT38JqFeBFPHT77beze/duTj/9dM444wzOP/98rrjiClasWAHA+9//ftavX8/KlSu56667ht43f/582tra2LdvH8uXL+eWW25h5cqVXHbZZfT39wNw00038dBDDw0df8cdd7Bu3TpWr17Nm286d8K0trbyrne9i5UrV3LzzTczb9482traJvm7kNmsLwsK8J0HoLcVZq1zXteshqbXnOeD3dD6Fs8PzGdpdSGLqk6xYF6zGhJRqN/ivFYEjUxTmt8znzLgReRkaH6fIuIR8Aex1rJzsHx4e8Vi78YkIhlN8/vEeFb9jics+YEExv10NaMsvBie/ArseRrWXMNZteXMM80c9K9lLjhZvT3NHg9SxHtf+s832N7YldJzrphZxB3vW3ncY772ta+xbds2XnnlFZ5++mne8573sG3bNmprawH4yU9+QllZGf39/ZxxxhlcddVVlJeXjzrHzp07uf/++7n77ru59tpr+cUvfsGNN9541LUqKirYunUrP/jBD/jmN7/JPffcw5e+9CUuvvhi/v7v/55HH32UH//4x6n7BmQLtwDfH53CGfBuxjuz1juPNaucu6P6O9xOeMujh2dy2QXVp36t5EKsdc86j+Hj5MmLTBIv5njN75nP7/MRj6sALzKVaX7X/H5M8QgEQgzGEuyNl0MysKBcBXiRqUDze+bO755mwOcFbOZlwAPMOB1yS2H3kwCsqYBy082b0Spnf2GNOuBFMsiZZ545NLkDfPe73+W0007j7LPP5sCBA+zcufOo99TW1nL66acDsH79evbt2zfmuT/4wQ8edcyzzz7L9ddfD8DGjRspLS1N4VeTJfzBqd8B37DFuQW3epXzumaN89j8hrs4K7wcr+XS5SkowJcvdOLN6p5zXqsDXgTQ/J6JAn51wIvIqdP8nqFiTgRNZ3+UTvKJ+POd7YqgEZEJ0vw+Nk874HP9NjMjaHx+WHChU4C3lmDHHgCeP1zMZeAU4Hc94eEARTLDeJ3qkyU/P3/o+dNPP83jjz/O888/T15eHhdeeCEDAwNHvScUCg099/v9Q7c4Hes4v98/boaZjJANETQNW51omIB7p1aN26Xe9Do0bqU1OJNAsJLVs1LQre7zQ/XK4a57ZcBLBsiEOV7ze+ZRBrzI1Kf5XY7JzYDv6o8ChoH8WeT07IGy2nHfKiLe0/yeuTzNgM/zxzOzAx5g4SXQ0wQtO+DQXgD+1FFCR1/EWYg10g2RXo8HKTI9FRYW0t3dPea+zs5OSktLycvL48033+SFF15I+fXPO+88HnzwQQB+//vfc/jw4ZRfY6oz/gB+E6d/qhbgE3E4+IqzAGtSQTXkVUDz69j6LWyOzOfS5VX4fCY116xZ5eTAA4TVAS/Tk+b3zOdkwCe8HoaITDGa36eI+KBTgB9wficdKFkIlcvAn6F1GxHxnOb3icmADvgMncgXXuQ87n5yqNBel6jipb2HuKywxtnX3eTEBojIpCovL+e8885j1apV5ObmUl09HAGyceNGfvjDH7J8+XKWLl3K2WefnfLr33HHHdxwww387Gc/45xzzqGmpobCQnUsj2T8QYJTuQO+7W2I9Aznv4OzCHfNatjzB0xXPZtjF6QmfiYp2WEPiqCRaUvze+bz+wwJC4mETd0HkCKS9TS/TxHxKPiDdPY7Bfjm875CVXVonDeJyHSm+X1iPCvAxxKWkC+DO+CLZ0PFUqcAn1+BLZqFjYV5Yc8hLlvhFOD/9Mo2OqvyuHz1DI8HKzL93HfffWNuD4VC/O53vxtzXzIjrKKigm3btg1t/8xnPjP0/N577z3qeIANGzbw9NNPA1BcXMxjjz1GIBDg+eefZ9OmTaNumRKnAB8gTn9kat0WNuTIBViTalbDnqcAeNO3iM8uqkjdNZMZ86AOeJnWNL9ntoBbdI9biw8V4EVk4jS/TwHuIqxd/c7v8PllNVBS4PGgRCTTaX4fn2cF+IS1hHyJzMyAT1p4MWz5KZQvxpQvZG1+CS/ubYcznQL8/U9s4tlwLn+2sga/OoBEpo39+/dz7bXXkkgkyMnJ4e677/Z6SBnH+AKEfImp2wFf9yenC718EU+92cJ3n9zJ5y9fzga3Sz2Oj5KFZxAO+lN3zaoVgAEs5GTeJ/Yi04Hm9/H5fU6CZTxhSeUUKCKSTprfJ2jEIqwARbkZ2jApIuKaKvO7p9XvkImDP8fLIRzfwovhxTuh+XVY/1HOCpfz/57cydf/5OfvgFVF/fy2I8rW/Yc5Y36Z16MVkUmyePFiXn75Za+Hkdl8AXJ8CfqiU7AA398Bb/wKVl3Fvc/X8eXfbgfgIz95ifuunMPpwNuJWVywcn5qrxsqgLIF0NMCPs+WaBGZ1jS/jy/ZAR/TQqwiMoVofp+geARy8t1FWKEorAK8iGS2qTK/e/oXfo5JZG4EDcD884bHV7aAs2vLsBbufOkQMRPko6flEvQbHt/RPPp91sKTX4W2nZM/ZhGRTOALEDKJqbkI6yv3QbSPO/su5ov/uZ2Ll1Xz5KcvZFZJLv/jV+0MmjCv2EVctKwq9deeeTrkl6f+vCIiKZK86zMeVwFeRCTrjFiENTfoJyegphARkVTwdDYNmnhmR9Dk5MNcd4GA8oWsm1fKsppCPnHRIvxFNYT6WzirtpwndrSMelukbS888w2a/njv5I9ZRCQT+IMETYK+TMuAt9a5tfZYEgnspnvYm7uSr78a4pbza/nRh9czvyKfB249m3mVxVw78Dl+X30zlYVpyJW77Ctw3b+l/rwiIikS8Cc74BMej0RERFJuxCKsRbkZXKsREZlivC3AE8vsDniARZc4j+WLCAf9PPo37+Szf7YMU1gD3U1cvKyKXS091LX3Dr3lpZf+BEBr3ZtejFhExHu+ADm+eOZlwD/1f+EHZzl/XIxlz1OYQ7v5TucF/O2lS/j8e1YMdXuWF4S4/5azmLXqHVx34fqx33+qimY6C72KiGSooQ54RdCIiGSfEYuwFiv/XUQkZTwtwAeIgT/DJ/UzboGrfgwVS0ZvL6yBnmYuXV4NwONuF3wiYdnx2iYAwj11kzpUEZGM4Qu4HfAZVoA/+Coc2gNv/teYu9uf+j5ttojE8iu47ZJFR+0vycvhBx9az8ZVM9I9UhGRjKQMeBGRLOYuwto1EFX+u4hICnlagPfbOPgy/LamUAGsvhqMGb29oAa6DzK3PI/FVQU84ebAP/VWC6V9ewGoijYSjev2XBGvFRQUANDY2MjVV1895jEXXnghmzdvPu55vvOd79DX1zf0+vLLL6ejoyN1A80mvgBBMrADvvOA8/jS0SujN+57i9KGJ/l9eCNfu/YMzJHzvohkHM3vk8/vLhKtDngRSSfN7x6JOwV4J4JGBXgRSb3pOr97WoD32Rj4c7wcwskrrIaBToj2c8nyal7ae4iugSg/emYPywMHASg2veyuO+DxQEUkaebMmTz00EMn/f4jJ/hHHnmEkpKSVAwt+7gZ8P2ZlgHfWQ85BVD3LDRvH9o8EI3z7P3fwALnX/9Z8kMZ/uGwiIyi+X3yqANeRCaT5vdJNmIRVkXQiEg6Tbf53bMCvN9nMIlo5kfQHEtBjfPY08wly6uIJSzffXwnL+1tZ7GvgWjhbAD279rm4SBFstPtt9/O97///aHXX/ziF/nKV77CJZdcwrp161i9ejW/+c1vjnrfvn37WLVqFQD9/f1cf/31LF++nA984AP09/cPHffxj3+cDRs2sHLlSu644w4Avvvd79LY2MhFF13ERRddBMD8+fNpa2sD4Fvf+harVq1i1apVfOc73xm63vLly7nllltYuXIll1122ajrZDVfgECmdcD3d8BgF5x5K/hDsOmeoV3f+fdH2TjwCIdmX8qc2iXHOYmIpJPm98w3nAGvuzxFZOI0v08R8ajTAd8XpSishhQRGZ/m94nxbEb1GwPxWOZH0BxLoZv/293EurnzKM0Lcs+ze1kc7iQn3odd9iHYdDeH698E3uPpUEXS5ne3Q9PrqT1nzWp499eOe8h1113H3/zN3/CJT3wCgAcffJDHHnuM2267jaKiItra2jj77LO54oorjhkjcuedd5KXl8eOHTt47bXXWLdu3dC+r371q5SVlRGPx7nkkkt47bXXuO222/jWt77FU089RUVFxahzbdmyhZ/+9Ke8+OKLWGs566yzuOCCCygtLWXnzp3cf//93H333Vx77bX84he/4MYbbzzFb9IU4Bbg+zOpAN9Z7zzOOA1WXQWvPgCX3sGvth7gmrc/TTAnSNEHv+HtGEUyiQdzvOb3zKcOeJEsoPld8/uxxCNYfw7dgzFF0IhMRZrfM3Z+96wDvig36OaLTdFJvdBZfJXuJvw+w0VLqwC4dXkUALN0IwkMsdbdXo1QJGutXbuWlpYWGhsbefXVVyktLaWmpobPfe5zrFmzhksvvZSGhgaam5uPeY5nnnlmaKJds2YNa9asGdr34IMPsm7dOtauXcsbb7zB9u3bj3UaAJ599lk+8IEPkJ+fT0FBAR/84Af54x//CEBtbS2nn346AOvXr2ffvn2n+NVPEf6g0wEfjWNthhRpkvnvJXPgzFsg2kvT0/dQ9ehfMs/XSs6H7oOyBd6OUWSa0/ye+ZId8LF4hsztIjIlaH6fAqyFeIQIfqxFETQiMiGa3ydmQu3nxpiNwD8DfuAea+2YH30YY64CHgLOsNYeNy1/RnEYEl3gm6KT+ogIGoCrN8zm1foONlbvgh3AjNPpyakkt3c/g7E4oYDfu7GKpMs4nerpdM011/DQQw/R1NTEddddx89//nNaW1vZsmULwWCQ+fPnMzAwcMLn3bt3L9/85jfZtGkTpaWl3HTTTSd1nqRQKDT03O/3T59bWH0B/MSJJyyReCIz5sAOtwBfPAcKqojPWEfFC1+hxpeg+8/+mcLad3g7PpFM49Ecr/k9swX8yQgaFeBFpizN78c1Xed34k4zYX/CKRMVhadorUZkOtP8flxezu/jdsAbY/zA94F3AyuAG4wxK8Y4rhD4n8CLE756PDZ1O+Dzyp34nG5nwdVzF1bwxKcvpLBrt7Mvv4Jo8Xzm0sybB7s9HqxI9rnuuut44IEHeOihh7jmmmvo7OykqqqKYDDIU089RV1d3XHf/853vpP77rsPgG3btvHaa68B0NXVRX5+PsXFxTQ3N/O73/1u6D2FhYV0dx/9/+fzzz+fX//61/T19dHb28uvfvUrzj///BR+tVOQL4DfOvEzGRND03nAyX7PrwRg2+zrCZBg/7KbKTznJm/HJiJDNL9nNr/P+fNBETQicqI0v2e4+CAAAwmncUYRNCIyUZrfxzeRDvgzgV3W2j0AxpgHgCuBI3v+/wH4OvDZCV89EZ26GfA+HxRUQ/cRt1C0vgUVSwEIVS1iXstvebShk9PmZNTBdLEAACAASURBVO5KvCJT0cqVK+nu7mbWrFnMmDGDD33oQ7zvfe9j9erVbNiwgWXLlh33/R//+Mf56Ec/yvLly1m+fDnr168H4LTTTmPt2rUsW7aMOXPmcN555w2959Zbb2Xjxo3MnDmTp556amj7unXruOmmmzjzzDMBuPnmm1m7du30uV11LL4APpzCe18kTkmex+MBpwBfPBvc3Lk/hC7ifw9+hX//wP/n8cBEZCTN75kt4FMHvIicHM3vGc7tgO+LOx+0FuVO0VqNiEw6ze/jM+Nl8xpjrgY2Wmtvdl9/GDjLWvvJEcesAz5vrb3KGPM08JmxImiMMbcCtwLMnTt3fd1HO+Gdn4WLP5+yL2hS3XURhIvhI792XlsLX58PKz8A7/sO9o/fwjzxJf7P8kf5h+vO8XSoIqmyY8cOli9f7vUwss5Y31djzBZr7QaPhnTSNmzYYDf/wyVEtt7Hku4f8vinLmBRVYHXw4K7L4FQAXzEWYH9r+9/mZf3H+bZv7vY44GJZA7N8amXVfP7srn2e7/dxPX3bOa+W87i3IUV479JRDKC5vfUy6r5fcMGu/mp38I/LeWNdV/iPc8t5rd//Q5WzSr2emgiMg7N76mXjvn9lBdhNcb4gG8Bnx7vWGvtXdbaDdbaDZWVlYCduhE0AJXLoGErRPqc172tMNDhbAeMu5hf+4G3vBqhiIg3fAF8GRdBU+/kv7t2NnezOBM+GBARmSq6GshJOLmb6oAXEckyMSeCptftgNcirCIiqTORAnwDMGfE69nutqRCYBXwtDFmH3A28LAx5vifCiQ776dqBA3A2hthsBPe+JXzuvVN57HSiaDBLcCbw3sypwAlIjIZfAF8Cfc21kjM48Hg/EHR0zRUgI/FE+xp62VxdaHHAxMRmVqC1inQKANeRCTLuBE0vTFlwIuIpNpECvCbgMXGmFpjTA5wPfBwcqe1ttNaW2GtnW+tnQ+8AFwxVgTNaAnnYSp3wM87FyqWwJafOq9b3U73oQJ8LQBzaWb7wc7xz/frv4KHPpaGgYqk1njRVXJisvL76Qtg3A74tp6Ix4PB6X4HKHEK8AcO9xOJJTIjGkckw2TlnOSRbPxeBt0PV+Px7PvaRLJdNs5JXsnK76W7CGtP1GAMFIamcLOkyDSTlXOSR9L1vRy3AG+tjQGfBB4DdgAPWmvfMMZ82RhzxUlfeagDfgoX4I2B9TdB/SZo2uZ0wIeKoHCGsz9USDyvgrmmmVcPjFOAT8Rhx3/C3j+kfdgipyIcDtPe3q4JPkWstbS3txMOh70eSmr5g24B3lJ3qNfr0QwX4N0O+J3NzmrpiqARGU1zfOpk6/weUAe8yJSk+T11snV+J+40zfTEfBSGAvjcRbdFJLNpfk+ddM7vE/pI01r7CPDIEdu+cIxjL5zYpd3/MPxT/FPV026Ax78EW+51OuArlzqFeZe/fCGL+1v4ecM4BfjmbTDYBYNA3yHIK0vrsEVO1uzZs6mvr6e1tdXroWSNcDjM7NmzvR5GavmcW1er8/zsb+/zeDBA5wHnsdj5Pu9s6QFQBI3IETTHp1Y2zu/BhFOAVwa8yNSi+T21Jnt+N8b8BHgv0GKtXXXEvk8D3wQqrbVtxpgLgd8Ae91Dfmmt/fK4F3EjaDqjPsXPiEwhmt9TK13zu3fV72zogAenUL7iSnjt3508+2WXj95ftoCFBx/nqbda6B2MkX+s27jqnht+3voWzDsnfWMWOQXBYJDa2lqvhyGZzp3b55fnUJcRBfh6wEDRLAB2tfQwszhMgW6tFRlFc/zUZ4zxA5uBBmvte40xtcADQDmwBfiwtTZijAkB/wqsB9qB66y1+8Y7v3+oAz6Rni9ARNJC8/uUdy/wPZx5e4gxZg5wGbD/iOP/aK197wldwV2EtStitACryBSi+X1qmEgGfHokC/D+HM+GkDIbPup0r/cfgoqlo/eVLaA01kp/Xy/3vzT6Z6K1lodfbeTuZ/ZQ9/J/Ew3kOzva3pqkgYuIpIm7wHZtaZj9hzKgAN9xwIkHCzg/c3a2dLNI3e8ikp3+J05sZNLXgW9baxcBh4HkgkMfAw6727/tHjeuYFwd8CIik81a+wxwaIxd3wb+F0MRA6fAjaDpjBiKwirAi4ikkncF+KSpvAhr0txzhgvvlctG7yt1PoV675xB7vnjXgZj8aFdv36lgdvuf5mvPrKdgqaXeHhwLf2EGDy4g+Oqex72v5DKr0BEJLXcuX1eSZDGzv5Rc58nOvcPxc8kEpZdLT3KfxeRrGOMmQ28B7jHfW2Ai4GH3EP+BXi/+/xK9zXu/kvc44/Ln3AKNMqAFxHxljHmSpy7nV4dY/c5xphXjTG/M8asPMb7bzXGbDbGbG5tbR0qwHdEoChXd4mKiKSShx3w7m2rviyY2I2BM28B44eaVaP3lS0A4KPLLU1dA/z65QYAWroG+OLD21k/r5TXPzGfctPNaee9l92JmdS9ufXY12rYCj97Pzx6e7q+GhGZRowxG40xbxljdhljjppYjDHfNsa84v572xjTMaETuxnwc0uCWAsHDvWnduAnqrMeSpwFWOsP9zMQTagALyLZ6Ds4nZDJfJhyoMNaG3Nf1wOz3OezgAMA7v5O9/hRRhZoAPzKgBcR8ZwxJg/4HDDW2nxbgXnW2tOA/wf8eqxzWGvvstZusNZuqKysHO6AH1QEjYhIqnnYAZ+MoMmSif2Mm+G2rVA0c/T2MqcDfmW4jdWzivnhH/YQT1g+/+ttDETjfOPqNRQ2vQjAojMug8ol5HXv4aW9Y9xd1t0ED3wIYgNEDx0Z8SYicmLcnODvA+8GVgA3GGNWjDzGWvu31trTrbWn4/wC/8sJndzNgJ9T4kS+7D/Um7Jxn7BEwinAFzsF+J0t3QAsrlYBXkSyhzEmuTjfllSed2SBBsAfHwDUAS8i4rGFQC3wqjFmHzAb2GqMqbHWdllrewCstY8AQWNMxbhndBdhbR9UBI2ISKp52AGfHEGWTOzGQOn8o7fnlUG4GNOygy8t3s1fd/4j+77xDjZv38lnLlvKwsoCqPsTFNRA2QKWrFrPbNPGl37x4ujIhtgg/PuN2P4OHuVcggPtEPW4o1REprozgV3W2j3W2gjOIn1XHuf4G4D7J3Rm9+6m2UXOHO/pQqy9LU5HjxtBs7OlB4BFlcqAF5Gsch5whVuIeQAneuafgRJjTPKW09lAg/u8AZgD4O4vxlmM9biGOuDjWoRVRMQr1trXrbVV1tr51tr5OHc4rbPWNhljapKRYsaYM3HqPuPO78lFWLtjhkIV4EVEUioDOuCzIIJmPGUL4JWfs+6F27jE/ypz+3fwjZJf8hfvqHUWo617DuafB8aQU+M0n/rad/HDp/cMn+O3n4L6Tfxsxu08Gjnd2dbZMMbFREQmbCh+wDUymmAUY8w8nC6bJ4+xf3SGpHt3U2nYR36O39sCfGe981gyF4CdzT1UFYYoztMfFiKSPay1f2+tne0WYq4HnrTWfgh4CrjaPezPgd+4zx92X+Puf9JaO25bu89dhFUd8CIik8cYcz/wPLDUGFNvjPnYcQ6/GthmjHkV+C5w/UTm92QETcQGCQe9Xy5QRCSbeFf9Ts7//hzPhjBp3vG3zqKpS9/N8121tP7m83x44DfQsBnyy6H7IMw71znWXcz1mnl9/MNTO1kxs4h3VXbAK//GwVV/yRc2L+KisPO9ix/ej79ikVdflYhML9cDD1lrx1xN1Vp7F3AXwIYNG2wyA94kYswtz6eu3cMImg43ssvtgN/V0s2SanW/i8i08XfAA8aYrwAvAz92t/8Y+JkxZhdwCGeeH5cy4EVEJp+19oZx9s8f8fx7wPdO+CJuAT6Kn1BABXgRkVTysP3c/aU9WyJojmfFlc4/YCOQWPp9+P4m+K9POdnxAPPOcx7LasEX5Np5PfwiXsxf/tsWfrXmJdYAn6k7i1kluVy2dgM8D90teylZ7MlXJCLZYSh+wDUymuBI1wOfmPCZk3N7Isa8sjzednPXPdHpNvkXz8Fay86WHq7dMOf47xERmcKstU8DT7vP9+BEjh15zABwzYme2xdTBryISFYaKsAHCAX9Hg9GRCS7eJgBP40iaI7gCxfCxv8LTa/BE1+G3LKhznf8QShfSLhjN/fdfBbnLixncPt/sTe4kD+1hrjjfSuomjWfhDX0t9Z5+4WIyFS3CVhsjKk1xuTgFNkfPvIgY8wyoBTntteJcTPgSUSZV5FH/aF+77olO+shXAzhIho7B+iLxLUAq4jIyTBmKIJGHfAiIlkmGUFDUB3wIiIp5n0G/HTogB/LivfDgguhr82Jn/GN+J+iYgm0vUV+KMCPr65lvW8nv+k/jUuWVfGuFdVUlxbSTCnxwweOdXYRkXFZa2PAJ4HHgB3Ag9baN4wxXzbGXDHi0OuBByaUHZnkT3bAx5lXlk8knuBgp0cLR3ccgGKn431ns9OJv7hKETQiIifM+DDqgBcRyU6xkRE06oAXEUmlDMiAn6YFeGPg8m/Cj94Jiy4dva9yGbz5W4gNkrP3ccCy9tL/wUfOOA1jDNVFYfbbcmZ213sydBHJHtbaR4BHjtj2hSNef/GET+xmwBOPMq88D4D97X3MLs07uYGeis7hAvyulh4AFlepA15E5MQ5BfiAzxBPJLwejIiIpFI8QsIXxOJTB7yISIqpA95LFYvh02/Buj8fvb1yKdgEtO+Gtx6BwhlccMGllOU7C9aW5+dwkApCfY0eDFpEZAJGZMDPLXOK7nWH+rwZS2f90AKsbzV1U1GQQ2n+NFgAXEQk1YyB2CB+n1EHvIhItolHsO7v8KGgCvAiIqmkDHivhYtGx8+AE0EDTkb8ridh6budP3hcPp+hM1hN4WALqPtIRDLRiAz4mSW5BP2GunYPCvCxQRjogMJqAHY0dbF8RtHkj0NEJBsYH8T6nQ74uArwIiJZxe2ABxRBIyKSYt53wPvVhXiUisWAgU33QLQXlrz7qEP6cmcQtBEnQ15EJNOMyID3+wxzSvOoa++d/HH0tDiPBdVE4wnebu5RAV5E5GQZA9EBdcCLiGSjeISEz6nPKIJGRCS1vO+An84RNMcSzIXSeVC/CYJ5UPvOow6JFc5ynnRqIVYRyUAjMuAB5pbnedMBP6IAv6e1l0gswQoV4EVETo7xQWyAgN9HXAV4EZHsEhvRAa8IGhGRlMqADvhpHkFzLJXLnMeFF0MwfPR+d0FBOrUQq4hkoBEZ8ADzyvLYf6gPaye5YNPT7DzmV7LjYBeAOuBFRE6WW4BXB7yISBaKR4gbRdCIiKSDOuAzVTIHfunR8TMAoYp5AAy275+sEYmITNyIDHiAeeX59AzGONQbmdxx9A53wG8/2EVOwMeCyvzJHYOISLYwPoi6GfBah0hEJLvEB4kNFeDVAS8ikkoZ0AGvAvyY5p8PeeWwZOOYu8vKKumxYfpb903uuEREJmJEBjzAvPI8APZNdgxNMoLG7YBfUl1A0K8/KERETooxEBtUB7yISDaKR4kbp4lGBXgRkdRSB3ymWnIZfHY35FeMubu6OJdGW078sDrgRSQDJTvg48kOeKcAv//QJC/E2tMMuWVYf5DtjV3KfxcROSU+iCU74FWAFxHJKrERHfBBRdCIiKSStx3wxg8+fbJ6TMYcc1dNcZhGW4Gvq2ESByQiMkFDETROBvzs0jyMYfIXYu1phoJqWrsHae+NKP9dRORUqANeRCR7xaPEUASNiEg6eNsBr/iZk1ZTFKbRlhPqa/R6KCIiRxuKoHE64MNBP3PL8tjW0DW54+hpgYIq3nAXYFUHvIjIKRjKgPcRj6sALyKSVeIRoiaIz0DAd+xmQBEROXHedsArfuak5eb4aQ9UkRc9DNF+r4cjIjLaUAd8fGjTOxZV8PzuNiKxSVy4r6cZCqrY4Rbgl6kALyJy8owPYgPqgBcRyUbxQaL4CQX8mOPcjS8iIifO4w74gGeXzwZ94RnOk07F0IhIhjkiAx7gwqVV9EbibN53aPLG0dMKBdXsONjN7NJcinP1wa+IyEkzBmIDBHwQT0zih6kiIpJ+8ShRAoSCip8REUk1dcBPYbHCWc6TzgPeDkRE5EhHZMADnLuwnBy/j6ffbp2cMQz2QLQXCqrY3tip/HcRkVNlnD8dwr6YOuBFRLJNbJAIQeW/i4ikgTLgpzBTMtt50lnv7UBERI50RAY8QH4owBm1pTz9VsvkjKGnGYBIuJK9bb0qwIuInCq3AJ9rosRVgBcRyS7xKBEbIBTwez0SEZGs43EHvCJoTkW4bA4Ja0h0qANeRDLMGBnwABcsqeTt5h4aOyZh7Yoep9C/P1pIwmoBVhGRU+dkAueaiDrgRUSyTTzCIAF1wIuIpIHHHfA5nl0+G1SWFtJCCYPtdV4PRURkNGPA+EdlwIOTAw/wh8mIoXE74Hf2hAEV4EVETtlQB3xMHfAiItkmPuh0wCsDXkQk5bztgFcEzSmpKQrTYCuIHdrv9VBERI7mC4zKgAdYXFXAzOLw5MTQuB3wrx4OUxgKMLs0N/3XFBHJZskMeBNVB7yISLaJRxm0fkXQiIikgbcd8IqgOSU1RWEabTm+7gavhyIicjR/8KgCvDGGC5ZW8add7URiifRev7cFjJ+trbBsRiE+n0nv9UREsp1xI2iIEE+keQ4XEZHJFRtkIKEIGhGRdNAirFNYdXGIBltBuO8g6I8gEck0Pv9RBXiAC5dW0jMYY0vd4fRev6cZ8ivZ0dLH0prC9F5LRGQ6cDvgQyZKLK4OeBGRrJJIdsCrAC8ikmoeL8KqAvypKM8P0Uw5/kQE+tq9Ho6IyGi+4FEZ8ADnLaog6Dc8/XaaY2h6WojnV9I9EGNOaV56ryUiMh24HfBhE1EGvIhINrHOnN6fUASNiEg6qAN+CvP7DPFwmfOi/5C3gxEROdIYGfAABaEAG+aV8Ye30rwQa08zAznlAMwoUf67iMgpS2bAE1UBXkQkqzhz+oAWYRURSQstwjrF+fNLnSf9Hd4ORETkSEdmwDe+DF0HAVg1q4h97b3pvX5PC10B50PKWSXh9F5LRGQ6SEbQENEirCIi2cQ6kbYDcUXQiIikg8eLsKoAf6pCBU53JwMqwItIhjkyA/6+6+CZbwBQGA4yEE0Qjadp/QproaeFQ8b5kHJGsTrgRUROnRNBE0IRNCIiWcWNoOlTBI2ISFqoA36Kyy92C/DqgBeRTDMyAz4WcRZF7XFy3wvDAQB6B4+OqEmJ/sOQiNKcKMbvM1QVhtJzHRGR6STZAW8jxBJp+gBVREQ84GbAx33qgBcRSQOPO+ADnl0+WxSUVAIw2N3m8UhERI4wMgO+1817H+gEnBx4gO6BNBXg3UJ/fbSQmqIwAb/+kBAROWVuAT5HHfAiItnF7YDvTSgDXkQkHdQBP8VVVlYB0HlYBXgRyTD+EQX4nmbn0b1bJ9kBn74CvHO9vQP5zChW/ruISEoYN4LGKgNeRCSruBnwg4qgERFJC2XAT3GLZpTQbXPpPtx61L4tdYfZUnfIeVH3HNx9MUT7J3mEIjJtjdkBnyzAO/N/90A0Pdd2O+B39uYxs0T57yIiKTHUAR8lHlcBXkQkezhzepSAImhERNLA4w54RdCcqtqKfLrIZ6C7/ah9X/jNNr78n9udF3v+AA1boOPAJI9QRKatkRnwR3TAJyNoetKVAe9eb0d3LjNK1AEvIpIy/hA5DKoDXkQkm7gRNBEV4EVE0sLbDnh/jmeXzxahgJ9+fyHx3kOjtkfjCXY293Cwc8DZ0FXvPCaLYCIi6eYbI4Im0g3x6FAETToL8NYfoj0eZpY64EVEUicYJmiVAS8iklXsiA74oCJoRERSzdsOeEXQpEQspxjfYOeobXtae4nEE7T2DBKNJ6CzwdmhAryITJZRGfAtw9sHOilwC/Bd6cqA720lmlsJGGYUqwAvIpIygVxybIRYIuH1SEREJGWcAvygDaoDXkQkDTzugFcETSqYvBJC0S6n0O56s6kLcL7Nrd2D0JUswLeMdQoRkdQbqwMeoL+DIjcDvieNi7D25ZQDMFMRNCIiqRMIEUwMkrCQUBe8iEh2cBdhdTLg1QEvIpJq6oDPAqGCcopML3XtfUPbth/sGnre1NkPnYqgEZFJNioDfmQHfAehgI+Az6R1EdZOXykAM9UBLyKSOsFcgjYCQNyqAC8ikh20CKuISDp53AGvAnwq5BeXU0wvu1q6h7a9ebCbcND5n7e9rRmibnFeHfAiMll8AUjEnec9LVA8x3ne34ExhoJwIK0Z8G0Ukxv0U5KnnzUiIikTCBFIDAIoB15EJFuMXIQ1qAK8iEiqeTuzqgM+JYrLKgmbKHsPtg1t23Gwi3MXVgDQ21I3fLA64EVksvgDkBjRAV+x2Hk+0AFAYThAdzoiaOIx6G2jKVHMjJIwxpjUX0NEZLoKDHfAx1SAFxHJDiML8IqgERFJOW8L8OqAT4mcAifnuLGpCYD2nkFaugc5Z0E5OQEfkUP7nQPzq9QBLyKTJ5kBH+mFSDdULHW29x8GoCAUTE8Bvq8NsOyPFDKrRPEzIiIpFQwPd8DHVYAXEckOTgZ8RIuwioikhQrw2SC3BIC2Vqe7/c0mJ4pm+YwiaorC0OkuwDprPfQ0eTJEEZmGkhnwyQ/+Kpc4jyM64HsG05AB715vT38+M4q1AKuISEoFhgvwsUTC48GIiEwPxpifGGNajDHbxtj3aWOMNcZUuK+NMea7xphdxpjXjDHrxr2ATWbA+xVBIyKSBoqgyQZhpwDfebiVRMKyw12AdfmMQmqKwwR7G53vdc0q6G1z4hlERNLN72bAJwvwxXMhmAf9bgE+lKYImvZdAOzoK2KmOuBFRFIrECaQcBdhVQSNiMhkuRfYeORGY8wc4DJg/4jN7wYWu/9uBe4c9+x25CKsiqAREUk1jzvgA55ePmu4HfB58W4aOvrZcbCbysIQ5QUhaorC5A80Q9EMKKwBrBvPICKSZj43Az659kRBlfOBoVuAT9sirPufJxHM4007VwV4EZFUC4TxD3XAqwAvIjIZrLXPAIfG2PVt4H8BIyfkK4F/tY4XgBJjzIxxrgBABEXQiIikw4RmVmPMRmPMW+4tTLePsf9Txpjt7u1NTxhj5k3s6uqATwm3A76YXna2dPNmUxfLZxQBMKM4TGmsGVs0CwqqneO1EKuITAZf0MmA73U74AuqnA8M070Ia91zdFWsJUaAmcUqwIuIpFQwTCAxAKgDXkTES8aYK4EGa+2rR+yaBRwY8bre3Xbk+281xmw2xmzu6+0B3AgaFeBFRFJu3JnVGOMHvo9zG9MK4AZjzIojDnsZ2GCtXQM8BHxjQldXBnxq5JYCUGx62XGwm53NPSyvKQSguihMjW0nkj+yAK+FWEVkEvgCTuRVTwtgIK9idAd8KEhPqgvw/R3Q/AaNRacDMLNEGfAiIikVyMUfdyJo1AEvIuINY0we8DngCyd7DmvtXdbaDdbaDXm5ucRNAIuPUFARNCIiqTaRjzbPBHZZa/dYayPAAzi3NA2x1j5lre1zX74AzJ7Y1RVBkxLhYgBqcgb4/fZmIvHEcAd8UQ7V5hDdoWqn+xTUAS8ik8M/IoImv8J5fUQHfCSeYDAWT901D7wIWN4OrQZghjrgRURSKxDCN9QBr0VYRUQ8shCoBV41xuzDqcFsNcbUAA3AnBHHzna3HYclbpwGSXXAi4ik3kRm1gndvjTCx4DfjbVj5C1OAPhzJjhMOS6fH0LFzM2L8OoBp7C1bIbTAT8rp4ccE6fdXwH5KsCLyCTyBZwImp6W4TtwRnTAF4adD2FTGkNT9xz4grxsF1GWn0Nujjp4RERSKpiLPxHFkFAHvIiIR6y1r1trq6y1862183HqNOustU3Aw8BHjONsoNNae3CcExI3QXwGAj6T9vGLiEw3Kf1o0xhzI7AB+Mex9o+8xQlQBE0q5RYzI6cfgKDfsLCyAICZOAuuNtkKyMmDUJEiaERkyHhrfLjHXOuu8/GGMea+CZ/cF4S42wGfvAMnt3RUBzyQ2hiauudg5lr2d1lmFCt+RkQk5QLO3BoiSiyuAryIyGQwxtwPPA8sNcbUG2M+dpzDHwH2ALuAu4G/GvcC1hL3BQkF/BijAryISKpNJANmQrcvGWMuBT4PXGCtHZzQ1bUIa+qESyiPOQX4RVWFBP3OZyulUafYXhd3cuIpqFIHvIgAo9b4eBdO18wmY8zD1trtI45ZDPw9cJ619rAxpmrCF/AFAAvdTVC+2NmWWwKRHohHKQg5PwNS1gEf7YfGl+Gcv6Jx2wBzy/NSc14RERnmFuDDRLQIq4jIJLHW3jDO/vkjnlvgEyd4BWIECAUVPyMikg4TmV03AYuNMbXGmBzgepxbmoYYY9YCPwKusNZOvL3arwz4lMktpQhn5fLlbvwMgL+nEYDdgyXOhoJqdcCLSNK4a3wAtwDft9YeBjipOb67abgDPuzORf0dFITcCJrB6El/AaPUb3Yy5+edR2NnPzPVAS8iknrBER3wKsCLiGQHmyBqgsp/FxFJk3FnV2ttDPgk8BiwA3jQWvuGMebLxpgr3MP+ESgA/sMY84ox5uFjnO6Iq6sDPmVyS8hL9OD3GdbMKh7e3lnPACH29Ljf64JqdcCLSNJE1vhYAiwxxvzJGPOCMWbjWCcaucZHa2urszG50LaND2fA57oF+IGO4Qiavn544U6ITezmqWPa/zxg6K5aT/dAjJklWoBVRCTlkh3wRh3wIiLZw+2AD2j9JBGRdJhQC7q19hGcHLGR274w4vmlJ3V1ZcCnTriEwGAnj9x2PrUV+cPbO+vpCFbR3O0WttQBLyInJgAsBi7EiSB7xhiz2lrbMfIga+1dwF0AGzZscCoyIz9kHbkIK0B/B4V5MwHIO/AsvHQ7lNbC0jHr+xNT9yeoXsnBwRAAM1SAF5FpxBgzB/hXoBqwwF3W2n82UI8czAAAIABJREFUxpQB/w7MB/YB17qRYgb4Z+ByoA+4yVq7ddwLjYigiSUSqf9CRERk8llL1ATUAS8ikibezq4+RdCkTG4J9B9maXUBOSN/aHY10Buu5mDngPO6oAoGuyDS5804RSSTTGSNj3rgYWtt1Fq7F3gbpyA/vpFz/NAirCM74J0CfaCrztnWVX9Cgx8lHoMDm2DeudQfdua3WSrAi8j0EgM+ba1dAZwNfMIYswK4HXjCWrsYeMJ9DfBunPl8MXArcOeErhJ05tYQUXXAi4hkC5sgQlAZ8CIiaeLt7OrP8fTyWSVc4mQfR48orHc2EMmfRfdAjL5IbLgLtVdd8CIy/hofwK9xut8xxlTgRNLsmdDZR67zMUYHfH7IucU11OOm4HQetb73xDW9CtFemHsOe9uceXC+FmEVkWnEWnsw2cFure3GiY6chbO2x7+4h/0L8H73+ZXAv1rHC0CJMWbGuBcKOHcZOR3wKsCLiGQFa4laRdCIiKSLxwV4RdCkTG6p89g/IhUiFnHy3oudSOemzoHhIphiaESmvQmu8fEY0G6M2Q48BXzWWts+oQuM2QHvzlUDHYQCfnICPnJ73c73rlMowNc95zzOO5d9bb0UhgOU5etDXhGZnowx84G1wItAtbX2oLurCSeiBia2DsjRAm4HvIkSj6sALyKSHSwRFEEjIpIu3mbAKIImdUbEOiQL7nQ3ApacUidhoqlzgAXJIlh30+SPUUQyzgTW+LDAp9x/JyaZAe8LDne+J+eq/sMAFIUDFA40Otu6Gk/4EoDzYePmn0DNaiisYV97HbUV+TjxxiIi04sxpgD4BfA31tqukXOhtdYaY06oam6MuRUnooa5c+eqA15EJBtZS8SqAC8iki7qgM8W4dFFLWAoziG/ah4ATV0jO+CbJ3N0IjIdJT9kLagCn/vjxh+EYP7Q3ToFoQClg27hvfMkM+A33QOH9sAldwCwr72XeeX547xJRCT7GGOCOMX3n1trf+lubk5Gy7iPydsgJ7IOCNbau6y1G6y1GyorK4cy4MNElAEvIpI1LIPWrwgaEZE08XgRVhXgUyZ3OFd5iFvMKqlZAOAsxJpfAcanCBoRST//iAL8SLklzt06QE3OIHmJHvCHnA54e4LFnL5D8Ievw4KLYNGlRGIJGg73U6v8dxGZZozT6v5jYIe19lsjdj0M/Ln7/M+B34zY/hHjOBvoHBFVc2yBMOBE0MQSiRSNXkREPGUTDFotwioiki7qgM8WI3KVh3Q5BfhwxVyKc4M0dw2Azw95FeqAF5H0G+qArx69PVwy9GFhbaDN2TZ7A8QHoW9i8fJDnvkmDHTCZV8BYzhwuI+EhfkV6oAXkWnnPODDwMXGmFfcf5cDXwPeZYzZCVzqvgYnfmwPsAu4G/irCV3FLcCrA15EJItYy4D1K4JGRCRNvA1hVwE+dcJjdcA3ONtz8plRHHY64MEphqkDXkTSLXmXU37l6O0jOuDn+lqdbfPOhbo/OQux5ldM7Pztu+Glu2DtjVCzCoB9bb3O6RRBIyLTjLX2WeBYi19cMsbxFvjECV8o6HbAE2Ugqg54EZHsYBlIKIJGRCRdFEGTLUJFgBndAd9ZD8WzAaguCjsd8AAFVSR6mrEnGvUgInIijtUBn1s69GHhzGQU8dxznMfOo+KHxxaPwmOfA38OXPy/hzbvdQvwteqAFxFJj4CTAZ9notQf7vN4MCIikhLWMqBFWEVE/n/27jo6qmvt4/j3jMbdkQgkENzdCi2ltFSpy6Xu+ra3eu/tbW9dqAtUKDVqUIoVd3dCIBAj7m6Tsf3+sRNCIGgjBfZnLdYkc86c2ZOk0zPPefZvtxgVQXOu0OnAxbthEVYhIHsnBMUCEOIlO+BTC6vYWmQiNyuNhXG5bThgRVHOefoTRNDUZ8A7cinHDYK7y23lp1CAryyAmVfCwT9h7PPgGXJ4U1pRNV4uBnzd1P9fFEVRWoTeCGgEuDpJK1YFeEVRlHOCqOuAVxnwiqIoLaKNO+DbNgHnnOPakKtM4UGoyoeIkQCEeLtQUFHLuHdWsaPYRAClrEtUMTSKorQg3QkWYa27WBhozyPDGYhwD5T7n6wAn7UDpo2GrG1w1ecwtHF6wqGiKiID3JFrESqKoijNTtPA6EqgC6QXqQK8oijKucFJrTCoCBpFUZQW0nYFeE2T/5Tm4+rbEEGTukbeRsoCfP9wX/zdTdw9Moqbxw3EpDlIyTjFqAdFaU5CQNIycDraeiRKSwvsCuHDocOgxve7+ICtGuxWfKzZZIggauwCPMNOHEFTcgi+mgCaDu5YDL1vOGaX1MIqlf+uKIrS0gxm/MwO0oqq2nokiqIoSnMQAhsqgkZRFKWltOG7qyq+NzuXIzrgD60Fr/bgGwnAqJhAtv/rIp6dGIuHfxgAZfmZWGyqCKq0suyd8N01kLS8rUeitDT3ALh9IXiFNb7ftX7R6BK8LDlkiEAqLXbwbgfl2cc/Xu5ecNTCtTMgrM8xm2vtDrJLa4hQ+e+Koigty+CKr8lJucVOabW1rUejKIqiNINalQGvKIrSYtq2A15pXvWxDk4nHFonu9+b+jnX5TH7UkpCbkUrD1I575WmydvyzLYdh9J2XOoK8EWJGJwWMkQg5RY7eLU78d9FdaG8PSLz/UgZxTU4BUQGuDXzgBVFUZRGjC54GewAHFIxNIqiKOcEGwbMRhVBoyiK0hJUB/y5pH5hw4IEqC6CiBFN71dXgA+klLisslYcoKLQ0OFcWdC241DaTn0HfM5uADJEEJW1dtkpX54tLyI2pbpI3rr5N7n5UKGMQlARNIqiKC3M4IKHXhbgVQyNoijKuUFF0CiKorQc1QF/LqlfhPXQWvl93QKsx/AKQ6DR1VxIXGZp641PUaChAF+lFgE+b7n6ytvDBfhAKiw28G4PDmtDof1oVUVgdAeja5ObD9UVgSJVAV5RFKVlGVxw08kCvFqIVVEU5dxgxagWYVUURWkhqgP+XOLqC8IBBxaBT0fwDW96P5M7ml8UA12y2JOpOuCVVna4A14V4M9b9RE02bsAyKzPgK/Pij9eDE11Ibg33f0OsgDv7WrE193UnKNVFEVRjmZwQe+oJdjLTFqxKsAriqKcC2zoVQe8oihKC1Ed8OeS+qLWobUQMerE+4b0JNqZSmJ+pVqIVWldFTnytkpF0Jy36iNoCg/icAvEgpmK2roMeDj+QqxVheAWcNzDHiqsVguwKoqitAajC9hqCPdzlx3wZVnqwrqiKMpZrlYYMRtVAV5RFKUlqA74c0l9UctplwuwnkhIT3xqs3BzVrEvp7zlx6Yo9VQHvOLiXfeFQPjImToVFruMoAFZyGlKddFx898BUguriPBXC7AqiqK0OIMr2Gvp6O8m479+uhl+ub2tR6UoiqL8BTIDXkXQKIqitATVAX8uqe+Ah+Pnv9cL7Q1ArJZGnIqhUVqLEKoDXgG9EUweAOj8IgBkBI1bAOiMUH6CArx70x3wtXYH2WU1RKj8d0VRlJZnMIO9hnA/N8oqKhA5eyBtvVpgXVEU5SymFmFVFEVpOW337hrYpc2e+pxV3wHvFwXe7U68b0hPAAa6njgHfuuhYhVRozSf6iK5yKZ7ENSWg62mrUektJW6C4Y633DcTHq5CKtOJ3Pgj1eAryo8bgd8RnE1QkCkiqBRFEVpeUbZAR8e4E4XLQNNOAABBxe19cgURVGUM2TFoCJoFEVRWoiKoDmXuPrK24gRJ9/XIxjcAxnilkVcVmmTu2SV1nDtZxt5e/GBZhykcl6rj58J6yNvVQzN+av+gqFPOB5mA5W1dvm9V7umI2isVWCvOW4HfGqhXAQwXEXQKIqitDyDuS4D3o2eulR5n9kbEha27bgURVGUM6YiaBRFUVqOurx5LvEMhejx0PfWk++raRDSiy4ilaT8Sqqt9mN2qY+m+X5zOoWVtc09WuV8VF+AD60rwKsYmvNX/QVD33A8XQwyAx7k7J2mOuCri+TtER3wT/y0ixumbeR/8/fxx275t6U64BVFUVqBwRXsFsL93eiupWIxeEPvGyBlpbxgqiiKopx1aoVRRdAoiqK0EPXuei7RG+HmX6DDoFPbP6QnATWp6IWdfdnHLsQan12GTgOL3cEXa1MbbZu9I5NL3l9LjVXF0yinoUJ1wCt16hdi9QnHw8VIxZEd8OXZ4HQ23r+qUN66yQ743DILs3dmkVZUzcxNaczbnU2QpxkfN1MrvQBFUZTzmNEF7BZ83Ez0MRwi0yUaYi8DuwWSV7T16BRFUZQzoDLgFUVRWo6hrQegtKGQnuicNjprMgd+QIRfo83x2eVEB3kSE+LJzI2HuHdUFL7uJuKzy3hmdhxWu5Od6SUM69x0JISiHKM8BzQdBHeX31epAvx5y9VH/i14t8fLJZ9Ki03e79UOnDaoLgSPoIb96zvg6yJo1ifJgvyX/xhIdLAHyQWVuBrVlFlFUZRWYXABpx2s1USTwWJtIJ07DpPreyQsgNhJbT1CRVEU5TTZMGBW59OKoigtQl3ePJ+F9gZgqFsmcVnHLsQan11GrxAXHr6gE9VWB1+tT6XcYuOB73fg62ZEp8Hm1OLWHvX5pSgZtn3d1qNoPuXZcv0Bz1D5faWKoDlvdbsShj4EeiMe5qMiaICEg/sb73+4A15G0KxPKsTf3UTXEE+Meh1dQ7wI91fxM4qiKK3C4CJvc3ZhxM42a0fQGyBmAhz8ExzHRhsqiqIof18OnYlqzKoDXlEUpYWod9fzmV8UGN0Y7pHDltRihBCHNxVU1GItL+CVpKuIKVrBxJ4hzFh/iEd/3ElmSQ0f39SP2FAvth5q3QJ8elE109YkNxrrOW3lqzD/MagpaeuRNI+KbFl8N5hlBInqgD9/RV8E418GaLQIa6pNRtPMXb218f5HZMALIViXVMjQTv7odGpBb0VRlFZndJW36ZsAWFvRDrvDCV0vlecs6RvbcHCKoijK6Sp060QmwRjUubWiKEqLUAX485lOD8Hd6a5LJ6u0hvTi6sOb4rPLGKLbj8lRCekbeeiCaCpq7aw8UMAzE7oyIMKPgRF+7EgvwWp3nuBJmtesrem8ujCB7DJLqz1nm7HVwIFF8uvCxLYdS3Mpz8bpGcYXa1NwugWqDHgFAE8X4+EO+Om7rABUF6aTccR7EtWFoDOCizfJBZXkV9QyQsVfKYqitA2DWd6mb8Jq8CDFGUh2qQU6jQW9GQ4sbNvxKYqiKKfFKcBs0KNpqgCvKIrSElQB/nwX0ovAqoOA7CitF59dzmBdXQREXjzdwryYMiyCmwZ35K6RkQAMivTDYnOyN/vY+JqWkphfCUBKQWWrPWebSVwKtir5deHBth1LcynPIc3mzf8W7KdY84EqFUGjgIeL7IBPKahk1r5qbJqRUK2IubuyGnaqKpTxM5rGukT5XjVcFeAVRVHahqGuAz5jMzX+PRDoSCuuArMHdLoAEubD+TJbUVEU5RwghMBsVOUhRVGUlqLeYc93IT3RW8vp51V+eFFDkB3wI40H5Df5shD/4uXdefWqnoevig+sW7R1ayvmwCfVFeCT88+DAvy+32XBUW86NwrwtZVQW0ZchczpLhTeLdMBb60Gh635j6u0GC8XuR74u0sPYtDr0bw70NujjDk7sxripqqLDi/Aui6piI5+bnTwc2urISuKopzf6jvgLaXo28k1hQ4V1c1a6nwhlKbLf0dJLawiq7SmtUapKIqinCLZAa/KQ4qiKC1FvcOe70J6AXB5cBEbkotwOGWxKzMrk04iTeZ1Vxc2uVhmoKeZqAD3VsuBt9gcpBXJjvDkgqpWec42Y62GA39C7OXg1+nciKCpyAFgY4EJgCy7Z8tkwH9xISz9T/MfV2kxHmZZgJ+/J4frBrTHENCJLqZCkguqiM8ulztVF4GbH3aHk80pRar7XVEUpS3VZ8ADbuH9MRt0bEop4oPlifzfBvmeXpO+vdFDHE7BzdM38fAPO1p1qIqiKMrJCSEwG/RtPQxFUZRzlirAn++Cu4GmY4hrFqXVNvZll1NusRFaulNu73+7vM3f1+TDB0b4sfVQCU5ny08zTi2sov5pUgrP8Q74pLr4me5XQUD0udEBX54NwCGrNz5uRlJr3MBSBvba5nsOSznkx8ufn3LW8HQxAqDXadw7qhP4ReFrycCoh9931sXQVBWCWwB7ssqoqLWr/HdFUZS2ZHA5/KUurC8R/u4s2JPD1GUHSaIjNqEnafe6Rg9ZfTCf7DILO9JLVRe8oijK34xAdcAriqK0JPUOe74zukJADJHWBADWJxeyvy7/3aF3gb43y/2OU4AfFOlHWY2Ng/kVLT7U+vz3Hu28SM4/xzvg438H90AIHw4BMVCcCnZrW4/qr6nrgC/WB3DDwI4kV9fFhzRnDnz9hYrCg7Jgq5wVPOoiaK7oEyZjZfwi0ayVXNbZzB+7s+XMnOpCcA9gfV3++9BO/m05ZEVRlPNbfQHe6A7+nXjlqh68c21vtj5/Ib8/OpZ0Qzi16TsaYsSAHzZn4Fk342lRXE5bjFpRFEU5DqfKgFcURWlR6h1WgS6XYE5bzZiAStYnFbI3u5whuv042g0Ar3Yyh/wEBXhonRz4pLwKQrQSHvHdTG65hcpae4s/Z5uwVsPBPyF2EugNsgAvHFCS2tYj+0tEmexkju4UQ6/23hQIb7mhOXPg69YrACBjc/MdV2lR3UK9GBrlzyNjo+UdflEAXBtpJb+ilk2JuXK2hFsA65ML6R7mhZ+7qQ1HrCiKcp4z1hXgQ3qCTs+ACD+u6d+eAA8zmqahhfUhypbIlpQiAHLLLKxIyOOWoeHEhnqxaG9uGw5eURRFOZoQqAgaRVGUFqQK8AoMvg90Bh50/ZMtqcXsT02nqy4dU6fRoGkQ1K1xYfMI7X1dCfFyYXMrFOAT8yt52mMh45NeJlzLJaXgHI2hSVwCtmoZPwMyggbO+hia4tw0SoU7o3uEEx3kIRdhhebtgC9IkF15ehOkb2q+456ukjRIXdN2z3+WCfQ08+M9Q4gIkAv01hfgB3iV4mk28Nu6OAASK03sSCtV8TOKoihtrb4DPqxPk5vbdxuGn1bJ/HVbAPhlWwZOATcM7MClPUPYnlZCTpmKoVEURfm7cAqhImgURVFakHqHVcAzBHpdT7/ihbjbS6k4sBodQsafQEMBXhyV826zoGkagyL92HqouNE046bYHU5eW7ifJ3/ZzVO/7ObpX/ewdF/eKQ8zMa+CkWIbAIN0CSSfiwV4IWD3jw3xM3DOFOBLclPJE36Miw0mIsCdUp2P3NCcHfAFCXLGQFjftivAl2bAVxPgh+vBcY7O0mhpPh1B02EqS+Oy3qHEJyUDMHVDMVaHk9ExgW08QEVRlPOceyAY3SBqTJObTR36AVB0cAu5ZRZmbc1gROcAwv3duaRnKACL4lQXvKIoyqnSNO0rTdPyNU3be8R9L2uatkfTtF2api3RNC2s7v4xmqaV1d2/S9O0f5/s+LIDXpWHFEVRWop6h1WkYQ+jd1iYYlzKQPZh10zQrr/cFhQL1kooTW/Yf8dMeL0D7PiWgZF+5JXXklF84k6mRXtz+XxNCmsTC1iXVMiCuBye/m0PFpvjpMOz2p2YihMIsMuC/UDdwXMvB95uhTn3yfiZgXeBrm4KoNkTPMOgMLFtx/cXOcuyqXENws/dhFGvw8NffgCn6ogCfGESxP165k+SnwCBXaHDYMjeCbZW7q6rLobvroGKbDmLofBA6z7/ucJgBq/2UJzCfyZ158MrOgLw8KShzH1wuMp/VxRFaWuuPvDPVOhySdPbg7sjdAa6aSk8MmsnWaU13DhIvpd3CvSga4gni/aqHHhFUZTTMAOYcNR9bwkhegkh+gDzgSML7WuFEH3q/r10soPLDngVQaMoitJSVAFekQK7QMwlTDEsZZRuD2UBfRryPYO6ydsjY2i2fQVOO/zxEJdlf4AeB28uTqDCYjvuU3y1PpVwfzc2PjOOjc+OY/ptAyiusjJnZ9ZJh5dWVMUYtstvwvox1HCQlMJzqAO+phS+uxr2zIKxL8DopxtvD4hulg54q91JbpnlLx/ndKUXVeNtL8TVv8Ph+8KDA6jCtXEH/KpX4be7oKro9J/EUg7lmWSbwiny7w9OmyzCtxZrNfx4A5Qcgsumyvta8/nPNX6RUJyCi1FPjIf8m43tFEnvDj5omtbGg1MURVEOnyceZ5sWGMsojyy2pBbj727iom7BhzdP7BnKtrQS8spb6ZzEXqtmpSmKclYTQqwBio+6r/yIb92BE09JP+HxUYuwKoqitCD1Dqs0GP4oXqKcGF0WhsiRDfcHdZW39QuxFibKwuKFL8KQB/Dd8wUrQj5idVwKE95by8bkY4unO9NL2JleypRhEeh0sng2JMqPbqFefLUu9aTxNYn5lVyo30F1QC/ofiUdRDbFuZnN8KL/BqxVMrIkfRNcPR1GPSWz948U2AUKDh4bA3SaXl24nwvfXU1VKy9gu3RvBoGUEdIu6vB90cEe5Du9sJfXxRA5nZC8EhCQuur0n6TuAsVr2zWe2+Yq72vNGJq5D0LGFrhmOvSbAiYPVYD/K/yioDhFfl1d91nDTXW+K4qinDXCetPVmQwIJvdvj+mIaIOJPUMQAhbFnbwL3mJzkFr4F2Y9Oh3wxTiYffeZH0NRWltVERQlt/UolLOApmmvaJqWAdxM4w74oZqm7dY0bZGmad1PdhwVQaMoitKy1Dus0qDjEBxhAwDwjh3TcL+LN3h3aOiA3/MzaDrodT1MeA0u/4jw0s0sGnYQo17jxumbeG9Z427tr9cfwtNs4NoBDR3QmqZx54hIEvMrWZNYeMKhZWak00dLxhg7EToOAyCgZCcO518rSDdSkQvv9YRD65rvmKcicQkU7Idrv4Ze1zW9T0AMWCvkGM9QQUUtP25Jp7LW3uRFkpa0Lf4AOk3gHRJ++L6YYE8K8cZSWveacndDTV2hNXnl6T9J3d/nntoQFqfasfl2br0CfE0pxM+BoQ9CtytAp4PQPpC9q3We/1zkFyX/HmpKoaru/cHVr23HpCiKopy60D6YrCW8e3EA94/p1GhT5yBPYoI9WHiSHPjKWjs3f7GZC99dTWJexZmNI+5XyI2Dfb/LRdLPJY7jzzxVznILn4QvLpQRlYpyAkKI54UQHYDvgYfq7t4BhAshegMfAr839VhN0+7RNG2bpmnbHE6HiqBRFEVpQaoArzTQNPTjX4JO46D9oMbbgmJlB7wQEPczRI6Wi7cC9LsVgrrTvngTCx8dydV92/HeskRmrE8Fu5WihPXEx+3g5r5+eJgNjQ47qXcYgZ5mvlyXesKhuaUtQ6cJjN0mQmhv7DoX+rGfzJLq5nv9e36WOffxTZ6ftJzEpeDiAzHHyVGFZlmI9ev1qVgdTswGHSsPnMbCpyVp8FZnSN98Rs9bXGUlL7Ouk9kz7PD9McEeFApvnBV1HfDJK+Rtx6GyAH+63f4FCdh1ZjJEEJoG+43dIWOT7KxvaRlbAAExFzfcF9ZHfuBXH47PjF+kvC1JhepCcPUFveHEj1EURVH+PsL6AnB1SAE+bqZjNl/Rpx1bDhVz21db2J9Tfsz2GquDO2dsZVdGKUa9xjtLzuAcyGGDVa+BX90FgG1fnf4x/q7WvgNTe0DZyaMclbOMwwZJy2QjQsoZNKUo56vvgWtARtMIISrrvl4IGDVNCzj6AUKIaUKIAUKIAWg61QGvKIrSgtQ7rNJYxHC4dfaxuZ5BsbL4m7ZBZlwf3and6QJI34SbZuOta3tzUbdg/jt/HwfmvoH/rIksNz3BM7suhNfDIWn54YeZDDpuGxLOmoMFJ+xsiixeS7E+EEJ6gcFEdVBfBuoSSC5oxhz4PT/L20Nrm++YJ+N0ygJ853EnLi4GxMjbMynAb5mO9fdHMG58nxfCE7gq0smqAwUnjf05bP8fUFUAO2ee/nMDy/fnEVwfV+gVevj+cH93ivDBaKnrbk5aASE95cyK8szTX3S2IIEsQwc6+HswOiaQ34s6gqWsdRZCTVsPOiO0G9BwX1hfcNQ2XjtBOXV+dXFFxSmyA97tmM8MiqIoyt9ZcHfQ9MedDXbPqCheuDSW3RmlTPxgLU/9spul+/LIKK6mxurgnm+3sfVQMe9e15t7R3Xiz/hcdmeUnt4Ydv8oL+Re/Ap0vRR2zDx2gfYzWXfmRP5iXOApsVbDhg+hMhd+v691mg2ai8Mm1/vZPqOtR/L3lbEZausuSu2d3bZjOdc4nTD/iTNuLPq70TQt+ohvrwAS6u4P0eoWTdI0bRCy7nPCNzunECoDXlEUpQWpd1jl1AR1A4dVdhEZXKHrZY23R42Rxcb0jeh1Gh/c0Jc+HXyw7fmNBML5KvAZuOglcPWBP5+VeZx1bh4Sjtmg46v1TXfB22ur6WPdwSH/EYez0U2Rw+impZGefRqd3CeSFw95cZS6tIeChMYLg7ak3N1QlQ/R40+8n2eozBQ/3aJ0WRYsehrdnlk8rv3Anbkv8XL2ndhLs0jKP8WLFwcXy9v9846dBluaDvvnn/DhS/flEeNad3HFq93h+416HQ63QFzt5VBTIj9sdBorL+ZAQ0f8KRL5Cey1hjIowo8bBnZgeXVdB3X6xtM6zhlJ2yAL7ia3hvvqOv9UDvwZ8o2Qt8WpUF2k8t8VRVHONkZX2cCR03QB3liewV1eW1l3ixf3Dw1h7q5s7p65jZFvrqTni4tZl1TIm5N7c0Wfdtw1MpJAV1gzZxr8eiesfhNydp+42G2vlfu16w8xE2DQPbKj+MiCZtyv8FYn2DK9eV5zXrzsSk9c1jzHAyhMOvw6DzdPxP0iz5363gqpa2DjR833fM3F6ZBjPNqyF+X4/3wWyk++BsB5KXGJbOzodiUkLABbKy1WfD44tAa2fQnrprb1SE6bpmk/AhuBLpqmZWqadifwuqZpezVN2wOMBx6ZalpIAAAgAElEQVSt230ysFfTtN3AB8AN4hS6r1QEjaIoSstRBXjl1AR1k7eH1kKXS8DFq/H28GHyRDFlFQCuJj1fXxFEDy2V2bZhxE64B4Y/Chf+V3Yk13ebA37uJq7u147fdmQ1OQW5IG457lot1RENRWqXziPQawJnRjN1L+z5Gaem5/HyGxteZ2s4uASBxs+lMRRW1h5/P02TMTSn2wG/dToCwVXae9zdbg7cvgiDsPGAYe6pxdBYymQBO6Sn/ProabDzn4CfboGyphfErbE6WJNYwAVe2fICgqtvo+0m72D5RfwccNqwRlxAlhYsu5+PLsBn74L6uJpjxlmOVp5JvC2MwVH+jO0aTJVbB8r0vi3f4WKtlkX28GGN7/eLArO3KsCfKZO7vPBUX4B3Vx3wiqIoZ5369VCOrPtUFsCip+HD/jDnHjy/vZh/7hhHQtBz7In8iFVR3zOzwzwW99vK5NrfYesXeK59mTWGh3i4+BVsiStg5avw+Sjsb8dSsfrjpp97x0woy4ALnpfnUREjIbArbPlcjidjK/z+gFzXaPnLDeuNHM/JaldC1BWVM2Hxc+BohgXv982Fj/rD6jcpqbJy2YfrePa3PbD5c3ludvmHsilm+UuQs+evP19zEQJ+mQLvdmsc7ZiwQF4s6HaF7IRf+b82G+LfWuJSCB8qYz6tFTKORmkeO7+Tt0nLmr5A9DcmhLhRCBEqhDAKIdoLIb4UQlwjhOghhOglhJgkhMiq2/cjIUR3IURvIcQQIcSGU3kOFUGjKIrSctQ7rHJqAmLkBxSQESFHM7lDh8GNFs/0SZOd0wMnTmFIVN3iibGXyxiZVa81ysZ+7MIYfN2M3PXNNgoqGheibfsXUC3MeMWObbiz/UAc6PDO3/bXX5vTCXG/sMdlAGucvagUrliTVv/1456KxCUUePXgn4tyuOubbVhsjuPvGxBzeh3w1mrY9jXpQWOJq/LmjrG9IXwYWt9buNGwkrj4+JMfI3klOO0w/n9yMd74OQ3b8vZB0lJAHHd67LqkQnxsBfQqWw69bzw8g6Geh7/siHfsmoUwuHLTYo2xb6+ivN1IuRhufcd9/n748iL4+damx1kgY2YOivYMjvTDZNBxTf8ObLRF40hd27I57FnbwGk7tgCvaTIHXhXgz5xv5BERNKoDXlEU5awT1keu45G5TZ4rLHgS3u8tO8773AT3rILrv4Mxz6IL7YWXwUFE9V6GFc0mJn4qLHkeFvwfbPgIU9QwHje+wHWe3/DHRSv52OsJtpT74rnyOX744DmWxOdid9RFsZTnyIz0jkPl7DqQ/18edLfsnI+fjZh1E3aPEBIn/oSwVVG16D9U1tqbjug78Ce801UWxI/nwCJIXQ3RF8tmk90/NNq86kA+by8+gM1xinExNgsseQHQEGve5JUvZxGfXU7q9sWQHw+D75Ov6fIP5f8jf7sLrFUA7Ewvad51kk7Xti9lhKGLD/zyD3nBpOQQ/H4/hPaGq6fD4Hth5/dyvZy/G7u1daKEmlKWKdfdih4v19xy9YN4FUPTLGpKYN8fcq0zp01eEFIaUQV4RVGUlqPeYZVTY3SRC1i5+sm88qZEjYHcPQ1Zmvv/gJCeXDR8CFp94VWng7H/gtI02Pnt4YcGe7kw/bYBFFXVct932xsK0WkbaZfyK0uc/YkKO6ID1uxJjms04VW7//prS1sP5Vl8VT6QMV1D2ezsSvXBVX/9uCdTVYjI2s4v5bFEBbizK6OUF37fe/xs9oBo2VVVe4rRMXtmgaWUN0ovoG9Hn4aLICOfRKdpDM+eQYXlJIXpxCXyw1P4COg6qfE02I0fgdENAmPlVOImLInP5T6XxWjCCcMeOma7f7AswOszN7PH0IMdWVXodRrTsyLAVgWZW+QU5rkPygikjM2QvunYJypIAKDMI4r2vq4AXDewA7/ZR6CvyIL175/853Wm0jYCmrwAdbSwvnI6uv0EsxuU4/OLguJk1QGvKIpytgrtI2+/vBB+vV12n8ZcDA9ugcs/kP+fjJ0EY56G676BO5fAY3vg+Vx4oQCeSYcnE+GfKehv/J7BF13PzswKHpmXza/O0ey64GsO+o3hpuKPWfbD2/R9aSkvvv0OFe8NxlZVzP4eT3LkWVVB1FVYdO7w6x1UVFZycf5DXPSblS+s43GN+54bXvyUKV9vbXx+tOtHmHWTXA9n7kNycfqj2a3yYkFADNzwvVwTZuVrYKvB4RS8s+QAU77eykcrk3ji5904nKdQ3N30MZSm45g8g3LNi7sK3+TDa7vxsPtySvCirPMVcj83P7jqUyg8iPOribz9y0qu+mQDV368nqT8JtZXOiLOxOEUxz3vPO454sHFcgbD8Zob8uLhz+eg84XwyA7oczOsfgM+HS6L2tfOAIMZRj0Jrj4Uzfkn01YnNVw8OU1Op+DD5YncPXMbt32xgT/evJ29r43h4z93siGpkBrrEc0tGVshYaG8uO88TtNL5jZ4NxbmPXJG4/nLEpfK2+jxoDfK2QIH/pSNNSdTWymbZU5l39PlsMPi55s3Xqm17f1NRqZOfBN8wlW+fhPMRhVBoyiK0lJUAV45dRc8CxPfkieDTanP7k5dLTuPMjbLjvejRV8kOw9Wv9XoQ0Cv9j68e10ftqeV8OzsONKT9mL/4Uby9cF85nY/HubGi5SWBAygh0ikpLyuIO10NL0IVU2JPNk+3snonp+w6t1Y4ujPvyd1I8O7Pz7VaTjLsk/2E/lrkpajIVhq68Xnt/bnkXHR/Lo9k6/XH2p6//qFWDd8ILu6lrwAqceJynE6YdOnlPp0Z2FZBPeP7tRwEcSnA4UxN3CNbhU7djWdy3r4GIlL5AcovQG6XyUXhEpeDuXZMkao7y3Qf4q88FLQeLFTh1OweX8qN2jL0bpf1ZDpfYSwdh0Pfz23oiuvXd2T5ybG8nV2B5yaXsbQbPoEsrbDpA/kBaAmiukifz8WTLSP7Hr4dXYK9KCs43iW64YhVr3Rcouhpq2H4B5yfYNjXmBf2WGTd4LZBsf7ANhKNE2boGnaAU3TkjRNe6aJ7VM0TSvQNG1X3b+7Wm1wfpFQmQfCoRZhVRRFORu16wejn4YJb8DdK+HZDLj2awjofOLHaRoYTHL2nUfQ4f/HTu7fnhcndePne4ey4v9G88DYrsQ88DPOTuN4w/gFP3l/yIuVL5GHH5dbX+WS2bVc8v5avtuUxqsL9zPyvS18ZxuDAx1Lur/OnVdO4Mt/DCD86v9iNfvyecDPrE8q4LrPN5FfboGNn8hFTiNGwH1151y/3dUoXqay1k7mkvehOIWCYf+m2CKoHPkCVGRTtvpjpny9hQ9XJHFt//Y8OT6GebuzeXb2HpxNFOGFEOxIL+HnlVuxrXqL3LALef5AFI/V3ElXXQaT0t9gmH0zPzrG8srilIYHdhpL2vgvseQmcOveKTzVsxrQuHH6ZpILjmjc2PolvBqGmPswczYfYOhry5n82UZSjtjHYnPw8h+7uful95jyxXp2HbnwbVEy4pfbYfNnOBc/d+zvzVoNv9wuf29XfibXAbjiY7j4Vbn9io8PL7Kea3XlJ7cb8c/bwIbFPzHl662UVluPPeYJCCF4af4+3ll6kKyCYh7If4nLq2fTo3Ynndc/yc1fbKTXfxdz21dbWD/nU8SXF8GsG+GDvjj+F0rppxc3Ppc+uBhmXCZnEuyYKT8/tKTSdBl/VF3ccF/iUuye7Xlpo52nf91DUeSlsiklcfGJj5WxBT4bIaN/Ph4ko3+asYu/ZtkrsPEjxKyb5CzVs9GObyG4p7ww2P0qGZ16JoswW6vlY9tqlkQLUh3wiqIoLcdw8l0UpU6Pa068PbSPzLxOWSk7VqHpArymwbh/wTeTZDeUm5/MF9cZmNj1Mp4eG8OnK/bzUPx/qNBs3GB9gq7dwo49TsehuGR8T+1PN4G9SMazGFygw0DZjezmJ0+cU1fLGJXo8XDDj7KYXM9mQeyby3JtMH2iQgn3dydy4ARY+QUJGxfQbcLdZ/7zOomKuAXUCi96DxxNdLAnj43zICGnnP8t2EdZjQ1/DxNmg45gLxdGRgeiD+kFaLKLSP4gZVfWw9uPLf6mrIDCg0x3e5zoIE8ujA1utNl/wjM4E2bhvvldGPJj0wPM3im7vWIult9H1U+DnSMvrggHDH1QLsq7+FnZBT/2hcMP355WwmW1C3Ex1sj8/ya0ax9++OvOQy/n+oEdcToFc3ZmsSsvmt57fkFflQ9dJkK/22Thf/Xrstgf2OXwY2uy40lxhjEwKqjR8f85oQtPzbiDviIO3Y934/PQqsa//7/KYYPMrXIRtKPYHU7y3boSBiTsWEPnkD4Y9Eec1Fqr4dc75DT1e1bJD6utTNM0PfAxcBGQCWzVNO0PIcS+o3b9SQhx7BSGllb3IR1QETSKoihnI50eLmiiUHuGDHodU4ZHHnWnGd3138H3k+mWthaGPEjnC//Db049c3dlM3NjGi/8vhedBlf2ace4Cz5Fbyxlsm/4EQcJBl6i3R8PsSnqaxIy8rBMLQCRQ2GHCcyLfInUTTZGdHqW8fuepezPl9kW9QC/78pm574EFureZZWzN1N+1gGyg3mGsTd91r7Lfns4r101iBt6eqHVlmGudOerDbt5m3KuHNkHIWTTwtrEAn7ZnklSfiVvGj5H6K1cnzqRtJQM7hpxNdhzYNd3aJoeBtzBzxszGd45gPIaG4v25rIpxYUh7q/ylfldHkx9iOuGvsDlGzpx47RNfH/XYDrun4Z51UtU+3TBZee39HUuZpTPUyzND+eS99fyzwldGRjhy3c/fMNdlZ8TY8piZeZArv/4QUbGtifS18SNe+7A3wZLnaOYvGUaz2/SscrtYib1DmPKwABCVj0NhQeIGzuD135M5lDhHib1CeP6Af8gavD9ONDYl1nG6oP5fLY6BRzDuNhzHh+5/cB1qYFc8XE1X9w2gOhgz8a/Y5tFnv9lbIHIUbKZJ7QvU5cnMWPDIR4e4scThf9Gy9wqL/YguPjPZ1jZayPfu91C5Z55DE57nU3OWN6yX0cnXTYxWiaX5W7C55vLcERdgD5ihIzKCelJ8rhptF80BdP8x9A6DpGfKU6kMAkWPglFSfJ8dcAdJ5+5l7FVXgyoKpDrLd06h0NFVYQmruBX23BmbkpDr9NYFAebXQJw2TtbNrQcxVpbi3PVG7hsmgpe7WHS+zg2T0P/yz/Ya+rN6ojHuP7ySwnwMB9/LNYqOSugNF3+8wyBbleSXWbh89XJ1BxcyeuVU5nnHEIPMun4/Q3o71wk1yGoKZGzPfb9LqOFokaf+HU3J6dTNmp4hZ5839w4uSD0hDfkZ9Ee18D692D/XPn7OlUOG/x0s2wSGvcfGPnEmY//b0gtwqooitJytFNYDLtFDBgwQGzb1gz53crfy6yb5SJQfhGyC/6hrcfkfh/2061yARwXH7moa00pVOYijO5UmwNwrcpi19hv0EWMoEuwJ66mxicE2bnZ6D8dit7kil9ET3SBXaC2Qp6c5+8HhMyQ7nY5VU4T7hvfwtZ3CoZJU9F0Otm1sP59WPYfbrY+yzWTb+Hqfu2x2WzUvBLOTveRjH7qp2b5sdgdThbE5VBjdTAo0o9IPzNVr0Sy3NmPkU/9ip+7CZBdVDdP38TuzLJGj4/wd+POkVFcG63hYtDLDwKFiTBtNAy6By55o/ETfjeZ2sxd9Cx9m9euHcA1/dsfM6blU+9gdNkc9COfQOswSE6Xdj+iyLnyVVjzFjyV3PDB449H5PRNTSejiK6dIe+feSWUpMIjuw7/vl+ft5M7t12Bb1R/DP+Yw/HUvhSC1eCJxzMH5O8FOJhXwcKPHucx/S/yos6Dm+XJdVUhTO0BPa+RXVR1ql7vwpKqTvR65Gc6BXo0On5WaQ0/fjWVJ8vfYHHofQRPfJboIA/czadWiN96qJgQLxc6+LkduzFzG3wxDjF5Bn+KIWxOLeZQURWHCqvILKnB7nSy03wvix0D2NP/f7xyZQ/ZoV9TAj9cL4v3IGcRXDa10aHtDidGg367EGLAKQ30DGiaNhR4UQhxcd33zwIIIV47Yp8pwIDTKcA32/t79k6YNkZ+fctvcjaGoijKOUDTtBZ9f28pf+vzd5tFRhwecYEeZJf03qxyPF0MRAS4H//xTid8ewXk7qXavT3ri9zZYo3kS8dEnOhwN+mpsjp4wzCNa/Wrme0cSQ99OjGkg6axdtxc8lwiqK614xTgV5HAlZtvwGH2QW+vllF6RzngbM98xxAWOIdQJLyYGFrJNaGF9N/3GhX97id70LMAdAn2RKsth89HQ/hwLJd+wKUfrCW5QGa+dwp0Z2LPUO4cEYmPKJcNLqlrsLmH8k71RLxt+dxvmMdcxzD+z3YfY90PMdX0GW6WXKzhY9hQ6MbWEjd66VKZoN9KjXsHXHtOgk2fkO4ziMmlD3Gf8yfu0C3gh8jXqYkYx0U7HySsbCdvhrxLVnoiLxi+I1Qr5nu3m3m++FKCvcx0C/ViTWIhDqcgNtSLzOJqKmrl7IFRMYH874oedKzcDT/dgrO2nHfELcx0jOf24VHcMjScIE8XROY2an6+B7fyZJLoSBQZ6BBYDJ5U2DTc9U5cNSsaGlwzXUa2CAF/PCQjj4Y/itj0GRa/rsyM/gC70YPIAHci/N1ZvCuFyvXTeMT0B96igtyAoTzifIIt2TZitTT+ML/ANrdRbO3/FqM7+9GzYg26hHlYfTuzzdmF33P9mWSZx4icmWhGVwjrDalrsGsmVpovYHvE3cR27caQKH/83E3U2p3U2hx4JP6Oef7D8ty23z9g+X/Z6z+BN3P7MdP4Kt9HvcnoSbficAqenR3H+LR3uNGwinkDZhDSZTBdQjyJzyolacPvjEn/kM5ksEB3AQvbP4bZw4dle7O43LGUp4y/4k0Fi8QwigY9yeTxY3CpjxgRQjbUbPtaFs/tlkZ/m/kho7k+92YsdsEi07M4XbzYetFsPv9zG59YnsHHRYfLqEdh3VSwlIJ7oIzAuW2ubIY6GadDNu9kboPCg/KfbwRM/vrkBXWnExLmy6akvL1w2VRE/9tZn1REbKgn/k1dbFj0jFyb4P8OyM81QsBHA8AzFKbMP/l4639mv98Pu3+kzDsWr7IEtBt/hC6XNNrtbH1/N4dGiwXL13Fht+CT76woinIe+qvv76oArzSvLdNlBwia7AgY9+9Tf6zTKTtA4n6R+Yfj/g29m1jw9QifrU7m9UUJXNYrlPeuP6LDuKYUqotw+ETy/ookPlyRyNP6H7nPMI/X7DeS1f5S3jB9iXv6Sg6692dy5VNsfn784SJ/8geXYyjcj+PhnUQdVdA9HU6nYN6ebN5blkhqofyQpOHkcrd43ne+xvIebzJu8r3HPKba5qDW5qDW7mRXRimfr05md2YZ/u4mHhkXzc2DO8rXOv8J2D5DTosO7i4PsG8u/Hwbszxu5UP71ax6agxG/bHTCX9ft5P2S+6hvy4JjbronuiLYdL78sT389Eyo/POJQ0PSl4J314pv757pZxaDnIRrbkPwJ3LoMNANqUUsfTb1/mXmAb/mCe7lY7ni4ugwyC4+JVGd8+cM49bdt3K7I7PMfLaRwj2cpEbFjwpX/NjcXKclnJ4vQMf6W7mwX993BC1cwSrzUHKJ1cTWbyeN+3X85NjDH5+AVzZJ4wHLujc8GEEoCwLtk6HXtfzc5oHT8/eg5eLkS/+MYCBEUd1QK3/AJb+ixdj5jBjTw3uJj0RdR/qOvq7Ee7nxsU7H6C2NJchJf/l35d1447ervDt1VCUCNd8IS8YbfwI/jEfIkdSWWvnp60ZfLUulQ3PjmvpAvxkYIIQ4q66728FBh9ZbK8rwL8GFAAHgceFEBlNHOse4B6Ajh079k9LayIj93TVlMIbdR2K96yWi/kpiqKcA87WAs35dP6eVVrD6gMFRPi7ERPiSYCHmbIaG8mZeUTNvQIPSza69gPRhQ+BmAkN50RH2vgx5O6VMToewXWz3QTC6SA1Mxvv9KX4F20/9nG+kXDv6mNnx9lrQWcAnZ6k/AqW7c9nXNegYzvGhZAzUle9ARly7Zz9YdewrcfzuJnNXNwjBA9RBSv+B+mbEOVZaNVFWHUuOIY/geuoR+X6T7t+gLkP4vSPQVeYAAPvhkvfls9RXSwvkpdng9NGrms0T1XdQrpnb+4b3Ymr+7XDbNCTX27htx1ZrEzIp1OQB0Oi/Bgc6U+It0vDeCvzZb5+4mISXHqzoTIUk+Yk1lfQp3w5ecKH140PIjqN40BKKrFVWxmkO0BHPzeGdw1FZzBDt6ugff/GP6tvJskic1A3mLKgyU72lQfy+desDXSv3cUKZ18ig324eXA4LkYdvlvfY3z+l3xtv5gxul1E6vIo13nj4ShHpzV8hv7DMZT5oQ9j8gklKX47t2kLmWxYiwONafbL+Mx+GVaMDNft5Sr9Oq7Sr2e/qQebB76Pl38IBQtf4V77D5QYg/BxlqI9fQhM7nW/SsGSFcsZtfYmXKlllzOKOY6RjNPtYJQ+jkJjO7Z0eYLF9v7szyknp8zC+G4h3DKkI30CoXTZu7jumIbBaWWXrhshPu6EehnRV+ZCURLC5ElFzNWUhQzB4dURp1d79i7+ivHZn2DVuWIOjMJctB/uXgEhPSiqrOXVGbP5d8ETeGvV5Pj0I3PIi3TsEE7wr1dCTTFMWQghPY7zXxaQvQvmPyYbLcxe2P2i2VXlR7fydVhNXsSN+ZqefQbi42Zq/DiHTX7OWTdVFt79O8v/rtLW86n347yRN5BgLzOf3NyP/uFH/K7ttXIR5ajRJI3+kOdm76Wwqpa7bD9yveVnXor+lUuH9WVgmAmtNE3+vTTVQLb8JVj7DtP1N/B21QTmub9CZ102uruWQXC3w7udre/v5tBosWzNBkZGB7b1UBRFUf6WWqUAr2naBOB9QA98IYR4/ajtZmAm0B8oAq4XQhw60THPpxP480phEnxUd/LbSgWzz1cn89qiBC7tGcp7N/Q5XGwurKzl0Vk7WZ9UxFV92zEowodB25+kU/5SqnBBE4L1EQ/waMogrujbnteu7nX4mBWrP8Bz5b94POx7Hr9mLB39m+h+bkpNKRQlYw/oyoKEUj5ZmcyBvHKuCsji8ZDdBJXFYShOxOCowYYR8WQiJg/fkx5WCMHm1GLeX5bIxpQiYoI9+M+k7gwP08GH/SCou+ze2PkdzHuEisC+DE2/nycnDTh2qnadkiorV32ynvyiYq4IzueudplEHfwSzWCGC56HRU/JBXNHPdnwIIcdpnaTefRHdotYyuGtzoj+/2Ca270cXPYl/zZ8izkoCpf7Vx9/FgQ05PbrGl8kqLU7eHfuZr7cXopepzFlWAR3jIgk2J4jX3PsJDB7IRKXoFXm8UnYqzxwz4PHf57KAiw/3opL1kasendWul3Mq4Uj0Ad04o1resnievIKme1aXYRT0/ON7UI2dLyb5HIjmaU1vH99Hy7p2dCVU/3NZIrT4hlZ8zaPjI3mkXHR6HVHvdblLyHWvceXgU/jl7OGSa57MQqbXKSt0wUyiubTYVidgvc7z2Dm9nwqLHYGRfjxy/3D/g4FeH+gUghRq2navcj397EnOm6zvr+/GSUjrR6PB+9jZ3IoiqKcjc7WAo06f69TnwHfHLF2ZVmym9dhBf9oWVT0DT/+mkunQwg4tFYuHNv3lhOfj1mrAXG48HvYvrnw653y3O/u5TLXvV5ePMx7DHpdBwPuwC40dJqG7uhzoVMd67avYNXrOGwWLA6NGjtsMgzEMva/TBoci9mgRwhBckEVaUVVjIwOxHSizOrKfLmW0OD7ZKzKcWSX1vDr9kwGR/oxKNKvoZnDYYMvLoScXZT49GCO27XMqujFyAhXrg3KIcZ+kLKAvvxY1IlZWzIoqbYyuX97bh0STpSxGOfS/6CLn02VKRBN2HGzlWA1eLI74FJeqrmOuDzZdR4T5M73Qd8TmPQzdBoLtzYxc7S6mKptP8D2b3AvO4jN5I02+p8YBt8j10s4kcp8cha8RlXSespqnWg6PZ6e3qwzjWBacW9yahr/HWsaPD9Ixx35b6DL2QmXvgMDG5YAsjmcfDV7Afv2xTG3pi4iExgdVMPHlmdx0QvEuH+h84tE59sRizBTlJtGeUEGbumrCU/5HtwC0C55nWXacJ6Zs5eyGivXhBbyfwUvYMTOg44nGDZiHHcP64DJXiHXntr2lZwt7R9NxeAn2Od3IcviMxm1/VFG6OJY0fUlXk/vSlDZHv7ZJY/epiy0kjQoOQTWSjYPm8bt67xxM+nlrISqFF7KupNvxUR0jlquNGzAnRpsncZjvOJ98KqLQLWU4Vz7Hrr17/KDYyzTvR7htmERzFq+mZmOZ3B1dcP18rcwuvuBizdaSPez8v3dHBot1m7YzKDIk0QuKYqinKdavABflxF8kCMygoEbj8wI1jTtAaCXEOI+TdNuAK4SQpywdVmdwJ+jhID3esozt0f3nPhEvxl9sTaF/y3YT5CnmVBvF/w9zOzNKqOsxsbLV/TguoEd5I42C8y6CavDwVuGe5i+V949+4Fh9Ot4RCE8dy98NpynHffzq2MUV/cJ446h7Qj1ccPT1YRep8fiEOSUWcgpq8GYH0eHpB8ITJuH3l6DDQNxzghyzFGMMe3HvSpDZqV3HAyBsXJ6dIdBDV3rp0gIweL4PF5ZuI+M4hp6tPPiXrdVTMp4m4rIS/BMXYQl/AIecT7Otmwr658ee0x0z5Gsdidzdmby8cpk0ourGeJVzNuGT2lfLRcNtd29Bltgd2wOgZtJLy9uFCVjNXiSUuPCgdwKskst2BxOJuz7JyElO9hvD2GwLgFH2AD0V38GAdGn9RqPll5UzXvLDjJnVxZCQM923rytvU+XwiXU6t3Z5zaIGUWx9L/0Lm4b3unkB8zaAZs+hfjZ4LSzU+vGdzdSYWUAABAySURBVLWjGBdayyVFM6j27szarv+iYMNMbtavQHPzpbbHTUw94Mev+aGMH9gdi81JdnEV03KvZbk2GN8bP+eCLkFNP9/++TIrEijXPFnj7I0Y+hD24J64Gg1kFFeTsnURr1U8xzT7peyO/T/uGd6e3iEuaK4+bR5Bc9T+eqBYCHHCwPpmfX+fPg6ytsHzuY0/9CuKopzFVAFeOasUJsq1WE6Whd7MbA4n+jMt6DeXygJZwG0/4ISfa4QQCMGxY03fJKNSzF7QczJ0vkjOLgByyywk5lcwJMofIw5Y9iJ0vRTChx1/PEJAQYKMTjl6DahTsDO9hE9XJbNsfx5RgR706+hD346++LqZcAqBwymIDHCnRztveQEiLx5Cex/3tRdV1pKUX8mezDIWx+dSkrGX742vEqKVNLm/U2j84BjL58ZbCQgMYmd6KV1DPJl6fR9iQ72wFqTi+PZqXMtTjnlspv9wZukm8m1BJ8ossoFH0+DWAcH8q+xFjBnrEXoTmt2CQ2hkGzpQ5d4Bp08E+7TOPHkghv7hfnx8U7+GGRifDIX8fdh1LqwxDmdXpS/3G/5A0xspHf4ctqIMAhO+xcVZxRzHcDb1fJl/X9Ebd7OBgopapv34C09kPY6r1hAxpf23/Kx8fzeHRostW7bSu8Pp/10piqKcD1qjAH8qGcGL6/bZqGmaAcgFAsUJDq5O4M9hyStA07fuIjzA3F1ZrD5QQGGVlaLKWlyNel6+sgexoV7HfczG5CL25ZRzx/CIxtElTie81Qlhq8Hq1DA6ahpNNQWwCj21mLBiwF+roEaY+N0xnHXOnozzyeECt2R8yg7IfPVe10PsZWA+aorwGbLYHHy7MY3lCXnEZZTwk/YsPXSHmO8YzOO2B7Fh4PELY3j0wlMrftdn1M/bnc36xDxuF38QqeXwlP1e6rtaAFyMOjzMBkqrbdidjX8eF+u28rlpKhaDF+YJL6P1u+2Yrva/IqWgkkV7c1m+P4+kjGw6k8keEYWfpztdQjx5+9reDTE1p6I8B3b/iHPHd+hKkgH4zTGCF2x3UIMLo2ICmX6RCfOq/0LqWnDaACgQ3gjNgEEHfs4iii56H//hU47/PA4b7PgGgnuS49mdyZ9vIau0ptEuvTv48IbpS7pm1eXri7oPFS18Al/3fn0QGAdkIS+w3iSEiD9in1AhRE7d11cBTwshhpzouM36/v7b3ZCwAJ7Pbp7jKYqi/A2oAryiKG3J4RTHztpsBvnlFlbsy8JWnI57dTYeNVmYNTtmn1DcAzqg9+vA7lJXdqaXsC+nnDExQTwyLrrxTIbqYtg9i4O5pfyxt5BiC2xyxpIiwogKdGdolD/RQR5EBXrQJcRTnv9bq2REjKbDGT6Cb7LaMXt/JSkFlVRZHQBMGRbBcxNjGz9X5nYZZ9PtCnD1IS6zjPmr1jEu8WUGaftxCI3FYjBb209hyPALuLh745kUQgjW704gKXEfuXl5FBXm8/Z//3tWvr+bQ6PF7p3b6Rpy/M/OiqIo57PWKMCfSkTB3rp9Muu+T67bp/CoYzV/RrCitJQ9P0PaejC6U4WZ1FIHFpudWpsdu82Ou8GJt8GOp8GO3S+a/KhrsOg98HI10v3/27v/2Lvqu47jz1f7bUcpv1b5MdaCLYMwq3EUm4WBJmSbupll04RtzA0J0SwxI25Go86oxCVGTYwbxmWyMOZUsk0RtCGLONlESRzQrRMoZYxQB+3YWgZjlJa23/btH+eUe78/Wjr6vff2nvt8JDe953PP997PeX8/9/VtPveez3n1KfOuRT4I0wcOsvWR+3lhyxd55Nx3se9g87q/tG7lzLXNj9LufdP81yNPseXJH7B0ahFTi8LiRWHPvgPs2jvNc3unOW3ZEi581clc+KqTOXfFiSxdvIjFKbL5Njjvclh++sIe5CxP7drLt7+/h9WnL+eUE47xFO0qePx/qP172HnmpXxzx/N87/l9/Nzas3r127+nWa/yia9QT28ldbD5ualXwJuvm7tG6xHsmz7IM7v3sXvfAXbvm+aUE5Y0F3jduwvuvaF5rakTYMkycum1A/8PfJJfAD5Gs8TYTVX1J0k+Amysqg1J/hR4OzANPA38elU9fKTnXNAJmm9vas5IufiqhXk+SToOOAEvSS/t2T37uenurZyybAlvfO2ZrDnShZTnUVXs3LWX3XsPHPkizLN877k9bPrPW1l25vlcvG79Ec8onv16ixYtGst8P+s1a+uBTV/jzB/mC02SNEHGagK+n/+Bl6Qjc4JGkrrJfJekbjLfJambjjXfj2Z9iO3AOX3bq9q2efdplzQ4leZirJIkSZIkSZIkTaSjmYC/D7ggyZokS4ErgQ2z9tkAXN3evwL40pHWf5ckSZIkSZIkqeumXmqHqppOci1wB701gjf3rxEMfAr4+ySP0qwRfOUgOy1JkiRJkiRJ0vHuJSfgAarqC8AXZrX9Ud/9F4B3LmzXJEmSJA1SkrcA19N80ebGqvqzEXdJkiRJ6pSjWYJGkiRJUsckWQx8HHgrsBZ4T5K1o+2VJEmS1C1OwEuSJEmT6fXAo1X1WFXtAz4HvGPEfZIkSZI6xQl4SZIkaTKtBJ7o297Wtr0oyfuTbEyycefOnUPtnCRJktQFTsBLkiRJmldVfbKq1lfV+jPOOGPU3ZEkSZLGjhPwkiRJ0mTaDpzTt72qbZMkSZK0QJyAlyRJkibTfcAFSdYkWQpcCWwYcZ8kSZKkTpkadQckSZIkDV9VTSe5FrgDWAzcVFWbR9wtSZIkqVNSVaN54WQn8K2RvPjonQ48NepOHCesRY+1aFiHngur6uRRd+KHZb47flvWosdaNKxDj/k+nhzDDevQYy0a1qHHfB9PjuGGdeixFj3WonFM+T6yb8BX1cRexSnJxqpaP+p+HA+sRY+1aFiHniQbR92Hl8N8d/yCtehnLRrWocd8H0+O4YZ16LEWDevQY76PJ8dwwzr0WIsea9E41nx3DXhJkiRJkiRJkgbACXhJkiRJkiRJkgbACfjR+OSoO3AcsRY91qJhHXqsxfjxd9ZjLXqsRcM69FiL8eTvrWEdeqxFwzr0WIvx5O+tYR16rEWPtWgcUx1GdhFWSZIkSZIkSZK6zG/AS5IkSZIkSZI0AE7AD1iSc5J8OclDSTYn+WDbviLJF5N8s/33laPu6zAkWZxkU5Lb2+01Se5J8miSzydZOuo+DkOS05LckuThJFuSvGGCx8Rvtu+NB5N8NskJkzIuktyUZEeSB/va5h0HafxVW5P7k1w8up4LzPfZzPeG+d5jvpvv48p8n8l8b5jvPea7+T6uzPe5zHjzvZ/5Prh8dwJ+8KaB36qqtcAlwAeSrAV+D7izqi4A7my3J8EHgS19238OfLSqzgeeAX51JL0avuuBf6uq1wKvo6nJxI2JJCuB3wDWV9VPAIuBK5mccfG3wFtmtR1uHLwVuKC9vR/4xJD6qMMz32cy3xvmO+Y75vu4M99nMt8b5jvmO+b7uDPf5zLjzXfAfGfQ+V5V3oZ4A/4V+FngG8DZbdvZwDdG3bchHPuqdsC+EbgdCPAUMNU+/gbgjlH3cwh1OBXYSnsNhr72SRwTK4EngBXAVDsufn6SxgWwGnjwpcYBcAPwnvn283Z83Mx38918n3HM5rv53pmb+W6+m+8zjtl8N987c5vkfG+PdeIz3nyfcczm+wDz3W/AD1GS1cA64B7grKp6sn3oO8BZI+rWMH0M+B3gYLv9I8D3q2q63d5G84bvujXATuDT7aleNyZZzgSOiaraDvwF8DjwJPAs8FUmc1wccrhxcOiP4SGTVpfjmvluvrfM95b5Pi/zfQyZ7+Z7y3xvme/zMt/HkPkOmPFgvr/IfJ/XguW7E/BDkuQk4J+BD1XVD/ofq+bjkhpJx4YkyduAHVX11VH35TgwBVwMfKKq1gHPM+t0pkkYEwDt+lnvoPmj92pgOXNP+ZlYkzIOxp35br73Md9b5vuRTco4GHfmu/nex3xvme9HNinjYNxNer6DGd/HfG+Z70d2rOPACfghSLKEJtxvrqpb2+bvJjm7ffxsYMeo+jcklwFvT/J/wOdoTnG6HjgtyVS7zypg+2i6N1TbgG1VdU+7fQtN4E/amAB4M7C1qnZW1X7gVpqxMonj4pDDjYPtwDl9+01aXY5L5jtgvvcz33vM97nM9zFivgPmez/zvcd8n8t8HyPm+4vM+Ib53mO+z7Vg+e4E/IAlCfApYEtV/WXfQxuAq9v7V9OsPdZZVfXhqlpVVatpLuLwpap6L/Bl4Ip2t87XAaCqvgM8keTCtulNwENM2JhoPQ5ckuTE9r1yqBYTNy76HG4cbAB+pb3a9iXAs32nQmkEzPeG+d5jvs9gvs9lvo8J871hvveY7zOY73OZ72PCfO8x4xvm+wzm+1wLlu9pF4vXgCT5aeC/gQforav1+zTrjP0jcC7wLeBdVfX0SDo5ZEkuB367qt6W5DyaT1tXAJuA91XV3lH2bxiSXATcCCwFHgOuoflAbOLGRJI/Bt5Nc0X6TcCv0ayd1flxkeSzwOXA6cB3geuAf2GecdD+AfxrmlPAdgPXVNXGUfRbDfN9LvPdfO9nvpvv48p8n8t8N9/7me/m+7gy3+c36RlvvveY74PLdyfgJUmSJEmSJEkaAJegkSRJkiRJkiRpAJyAlyRJkiRJkiRpAJyAlyRJkiRJkiRpAJyAlyRJkiRJkiRpAJyAlyRJkiRJkiRpAJyAl45SksuT3D7qfkiSFpb5LkndZcZLUjeZ7xonTsBLkiRJkiRJkjQATsCrc5K8L8m9Sb6e5IYki5PsSvLRJJuT3JnkjHbfi5J8Jcn9SW5L8sq2/fwk/5Hkf5N8Lclr2qc/KcktSR5OcnOSjOxAJWnCmO+S1F1mvCR1k/kuOQGvjknyY8C7gcuq6iLgAPBeYDmwsap+HLgLuK79kb8DfreqfhJ4oK/9ZuDjVfU64FLgybZ9HfAhYC1wHnDZwA9KkmS+S1KHmfGS1E3mu9SYGnUHpAX2JuCngPvaDz6XATuAg8Dn233+Abg1yanAaVV1V9v+GeCfkpwMrKyq2wCq6gWA9vnurapt7fbXgdXA3YM/LEmaeOa7JHWXGS9J3WS+SzgBr+4J8Jmq+vCMxuQPZ+1XL/P59/bdP4DvIUkaFvNdkrrLjJekbjLfJVyCRt1zJ3BFkjMBkqxI8qM0Y/2Kdp9fBu6uqmeBZ5L8TNt+FXBXVT0HbEvyi+1zvCLJiUM9CknSbOa7JHWXGS9J3WS+S/jJkDqmqh5K8gfAvydZBOwHPgA8D7y+fWwHzRpkAFcDf9OG92PANW37VcANST7SPsc7h3gYkqRZzHdJ6i4zXpK6yXyXGql6uWd5SOMjya6qOmnU/ZAkLSzzXZK6y4yXpG4y3zVpXIJGkiRJkiRJkqQB8BvwkiRJkiRJkiQNgN+AlyRJkiRJkiRpAJyAlyRJkiRJkiRpAJyAlyRJkiRJkiRpAJyAlyRJkiRJkiRpAJyAlyRJkiRJkiRpAJyAlyRJkiRJkiRpAP4feRhdkZjrn+YAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<Figure size 1152x648 with 6 Axes>"
+       "<Figure size 1512x648 with 4 Axes>"
       ]
      },
      "metadata": {
@@ -842,7 +1189,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 100/100 [13:51<00:00,  8.21s/it, discriminator_network_loss_actual=0.0489, loss=32.6, generator_network_loss=29.2, discriminator_network_loss=3.35, generator_network_psnr=168, discriminator_network_acc=0.679, val_discriminator_network_loss_actual=0.000673, val_loss=32.3, val_generator_network_loss=31.3, val_discriminator_network_loss=0.983, val_generator_network_psnr=164, val_discriminator_network_acc=0.887]"
+      "100%|██████████| 100/100 [12:36<00:00,  7.47s/epoch, discriminator_loss=0.0105, discriminator_accu=0.995, generator_loss=30.7, generator_psnr=160, val_discriminator_loss=0.000717, val_discriminator_accu=1, val_generator_loss=32.9, val_generator_psnr=160]"
      ]
     },
     {
@@ -851,94 +1198,127 @@
      "text": [
       "\n"
      ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
     }
    ],
    "source": [
     "epochs = 100\n",
-    "with tqdm.trange(epochs) as t:\n",
-    "    metric_names = [\"discriminator_network_loss_actual\"] + models[\n",
-    "        \"srgan_model\"\n",
-    "    ].metrics_names\n",
-    "    columns = metric_names + [f\"val_{metric_name}\" for metric_name in metric_names]\n",
-    "    dataframe = pd.DataFrame(index=np.arange(0, epochs), columns=columns)\n",
-    "    for i in t:\n",
-    "        ## Part 1 - Train Discriminator\n",
-    "        _, d_train_loss = train_discriminator(\n",
-    "            models=models,\n",
-    "            generator_inputs=[X_train, W1_train, W2_train],\n",
-    "            groundtruth_images=Y_train,\n",
-    "        )\n",
-    "        d_dev_loss = models[\"discriminator_model\"].evaluate(\n",
-    "            x=models[\"generator_model\"].predict(\n",
-    "                x=[X_dev, W1_dev, W2_dev], batch_size=32\n",
-    "            ),\n",
-    "            y=np.zeros(shape=len(X_dev)),\n",
-    "        )\n",
     "\n",
-    "        ## Part 2 - Train Generator\n",
-    "        _, g_train_metrics = train_generator(\n",
-    "            models=models,\n",
-    "            generator_inputs=[X_train, W1_train, W2_train],\n",
-    "            groundtruth_images=Y_train,\n",
+    "metric_names = [\n",
+    "    \"discriminator_loss\",\n",
+    "    \"discriminator_accu\",\n",
+    "    \"generator_loss\",\n",
+    "    \"generator_psnr\",\n",
+    "]\n",
+    "columns = metric_names + [f\"val_{metric_name}\" for metric_name in metric_names]\n",
+    "dataframe = pd.DataFrame(index=np.arange(epochs), columns=columns)\n",
+    "progressbar = tqdm.tqdm(unit=\"epoch\", total=epochs, position=0)\n",
+    "\n",
+    "train_iter.reset()\n",
+    "dev_iter.reset()\n",
+    "\n",
+    "for i in range(epochs):\n",
+    "    metrics_dict = {mn: [] for mn in columns}  # reset metrics dictionary\n",
+    "\n",
+    "    ## Part 1 - Training on training dataset\n",
+    "    while i == train_iter.epoch:  # while we are in epoch i, run minibatch training\n",
+    "        train_batch = train_iter.next()\n",
+    "        train_arrays = chainer.dataset.concat_examples(batch=train_batch)\n",
+    "        ## 1.1 - Train Discriminator\n",
+    "        d_train_loss, d_train_accu = train_eval_discriminator(\n",
+    "            input_arrays=train_arrays,\n",
+    "            g_model=generator_model,\n",
+    "            d_model=discriminator_model,\n",
+    "            d_optimizer=discriminator_optimizer,\n",
     "        )\n",
-    "        g_dev_metrics = models[\"srgan_model\"].evaluate(\n",
-    "            x=[X_dev, W1_dev, W2_dev],\n",
-    "            y={\n",
-    "                \"generator_network\": Y_dev,\n",
-    "                \"discriminator_network\": np.ones(shape=len(X_dev)),\n",
-    "            },\n",
+    "        metrics_dict[\"discriminator_loss\"].append(d_train_loss)\n",
+    "        metrics_dict[\"discriminator_accu\"].append(d_train_accu)\n",
+    "\n",
+    "        ## 1.2 - Train Generator\n",
+    "        g_train_loss, g_train_psnr = train_eval_generator(\n",
+    "            input_arrays=train_arrays,\n",
+    "            g_model=generator_model,\n",
+    "            d_model=discriminator_model,\n",
+    "            g_optimizer=generator_optimizer,\n",
     "        )\n",
-    "\n",
-    "        ## Plot loss and metric information using pandas and livelossplot\n",
-    "        dataframe.loc[i] = (\n",
-    "            [d_train_loss] + g_train_metrics + [d_dev_loss] + g_dev_metrics\n",
+    "        metrics_dict[\"generator_loss\"].append(g_train_loss)\n",
+    "        metrics_dict[\"generator_psnr\"].append(g_train_psnr)\n",
+    "\n",
+    "    ## Part 2 - Evaluation on development dataset\n",
+    "    while i == dev_iter.epoch:  # while we are in epoch i, evaluate on each minibatch\n",
+    "        dev_batch = dev_iter.next()\n",
+    "        dev_arrays = chainer.dataset.concat_examples(batch=dev_batch)\n",
+    "        ## 2.1 - Evaluate Discriminator\n",
+    "        d_train_loss, d_train_accu = train_eval_discriminator(\n",
+    "            input_arrays=dev_arrays,\n",
+    "            g_model=generator_model,\n",
+    "            d_model=discriminator_model,\n",
+    "            train=False,\n",
     "        )\n",
-    "        livelossplot.draw_plot(\n",
-    "            logs=dataframe.to_dict(orient=\"records\"),\n",
-    "            metrics=metric_names,\n",
-    "            max_cols=3,\n",
-    "            figsize=(16, 9),\n",
-    "            max_epoch=epochs,\n",
+    "        metrics_dict[\"val_discriminator_loss\"].append(d_train_loss)\n",
+    "        metrics_dict[\"val_discriminator_accu\"].append(d_train_accu)\n",
+    "\n",
+    "        ## 2.2 - Evaluate Generator\n",
+    "        g_dev_loss, g_dev_psnr = train_eval_generator(\n",
+    "            input_arrays=dev_arrays,\n",
+    "            g_model=generator_model,\n",
+    "            d_model=discriminator_model,\n",
+    "            train=False,\n",
     "        )\n",
-    "        t.set_postfix(ordered_dict=dataframe.loc[i].to_dict())\n",
-    "        experiment.log_metrics(dic=dataframe.loc[i].to_dict(), step=i)"
+    "        metrics_dict[\"val_generator_loss\"].append(g_dev_loss)\n",
+    "        metrics_dict[\"val_generator_psnr\"].append(g_dev_psnr)\n",
+    "\n",
+    "    ## Part 3 - Plot loss and metric information using livelossplot\n",
+    "    dataframe.loc[i] = [np.mean(metrics_dict[metric]) for metric in dataframe.keys()]\n",
+    "    livelossplot.draw_plot(\n",
+    "        logs=dataframe.to_dict(orient=\"records\"),\n",
+    "        metrics=metric_names,\n",
+    "        max_cols=4,\n",
+    "        figsize=(21, 9),\n",
+    "        max_epoch=epochs,\n",
+    "    )\n",
+    "    progressbar.set_postfix(ordered_dict=dataframe.loc[i].to_dict())\n",
+    "    experiment.log_metrics(dic=dataframe.loc[i].to_dict(), step=i)\n",
+    "    progressbar.update(n=1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
-    "model = models[\"generator_model\"]"
+    "model = generator_model"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
     "os.makedirs(name=\"model/weights\", exist_ok=True)\n",
-    "# generator model's parameter weights and architecture\n",
-    "model.save(filepath=\"model/weights/srgan_generator_model.hdf5\")\n",
-    "# just the model weights\n",
-    "model.save_weights(filepath=\"model/weights/srgan_generator_model_weights.hdf5\")\n",
-    "# just the model architecture\n",
-    "with open(\"model/weights/srgan_generator_model_architecture.json\", \"w\") as json_file:\n",
-    "    json_file.write(model.to_json(indent=2))\n",
+    "# Save generator model's parameter weights in Numpy Zipped format\n",
+    "chainer.serializers.save_npz(\n",
+    "    file=\"model/weights/srgan_generator_model_weights.npz\", obj=model\n",
+    ")\n",
+    "# Save generator model's architecture in ONNX format\n",
+    "dummy_inputs = {\n",
+    "    \"x\": np.random.rand(32, 1, 10, 10).astype(\"float32\"),\n",
+    "    \"w1\": np.random.rand(32, 1, 100, 100).astype(\"float32\"),\n",
+    "    \"w2\": np.random.rand(32, 1, 20, 20).astype(\"float32\"),\n",
+    "}\n",
+    "_ = onnx_chainer.export(\n",
+    "    model=model,\n",
+    "    args={\"inputs\": dummy_inputs},\n",
+    "    filename=\"model/weights/srgan_generator_model_architecture.onnx\",\n",
+    "    export_params=False,\n",
+    "    save_text=True,\n",
+    ")\n",
     "\n",
     "# Upload model weights file to Comet.ML and finish Comet.ML experiment\n",
     "experiment.log_asset(\n",
-    "    file_path=\"model/weights/srgan_generator_model_weights.hdf5\",\n",
-    "    file_name=\"srgan_generator_model_weights\",\n",
+    "    file_path=\"model/weights/srgan_generator_model_weights.npz\",\n",
+    "    file_name=\"srgan_generator_model_weights.npz\",\n",
     ")"
    ]
   },
@@ -946,19 +1326,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 4. Evaluate model"
+    "# 4. Evaluate model"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Evaluation on independent test set"
+    "## Evaluation on independent test set"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 23,
    "metadata": {
     "lines_to_next_cell": 2
    },
@@ -979,8 +1359,8 @@
     "    )\n",
     "\n",
     "    # Run input datasets through trained neural network model\n",
-    "    model = deepbedmap.load_trained_model(model_inputs=(X_tile, W1_tile, W2_tile))\n",
-    "    Y_hat = model.predict(x=[X_tile, W1_tile, W2_tile], verbose=1)\n",
+    "    model = deepbedmap.load_trained_model()\n",
+    "    Y_hat = model.forward(inputs={\"x\": X_tile, \"w1\": W1_tile, \"w2\": W2_tile}).array\n",
     "\n",
     "    # Save infered deepbedmap to grid file(s)\n",
     "    deepbedmap.save_array_to_grid(\n",
@@ -1007,7 +1387,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -1017,8 +1397,7 @@
       "Tiling: lowres/bedmap2_bed.tif\n",
       "Tiling: misc/REMA_100m_dem.tif\n",
       "Tiling: misc/MEaSUREs_IceFlowSpeed_450m.tif\n",
-      "1/1 [==============================] - 0s 400ms/step\n",
-      "Experiment yielded Root Mean Square Error of 110.07 on test set\n"
+      "Experiment yielded Root Mean Square Error of 136.98 on test set\n"
      ]
     }
    ],
@@ -1030,7 +1409,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -1038,9 +1417,8 @@
      "output_type": "stream",
      "text": [
       "COMET INFO: Uploading stats to Comet before program termination (may take several seconds)\n",
-      "COMET INFO: Waiting for completion of the file uploads (may take several seconds)\n",
       "COMET INFO: Still uploading\n",
-      "COMET INFO: Experiment is live on comet.ml https://www.comet.ml/weiji14/deepbedmap/497bd90c68d74aaa97a63818161b3897\n",
+      "COMET INFO: Experiment is live on comet.ml https://www.comet.ml/weiji14/deepbedmap/bc5b3144750442a1ab0230509489940b\n",
       "\n"
      ]
     }
diff --git a/srgan_train.py b/srgan_train.py
index 7454858..38e2f91 100644
--- a/srgan_train.py
+++ b/srgan_train.py
@@ -14,7 +14,7 @@
 # ---
 
 # %% [markdown]
-# # Super-Resolution Generative Adversarial Network training
+# # **Super-Resolution Generative Adversarial Network training**
 #
 # Here in this jupyter notebook, we will train a super-resolution generative adversarial network (SRGAN), to create a high-resolution Antarctic bed Digital Elevation Model(DEM) from a low-resolution DEM.
 # In addition to that, we use additional correlated inputs that can also tell us something about the bed topography.
@@ -22,7 +22,7 @@
 # <img src="https://yuml.me/diagram/scruffy;dir:LR/class/[BEDMAP2 (1000m)]->[Generator model],[REMA (100m)]->[Generator model],[MEASURES Ice Flow Velocity (450m)]->[Generator model],[Generator model]->[High res bed DEM (250m)],[High res bed DEM (250m)]->[Discriminator model],[Groundtruth Image (250m)]->[Discriminator model],[Discriminator model]->[True/False]" alt="3 input SRGAN model"/>
 
 # %% [markdown]
-# ## 0. Setup libraries
+# # 0. Setup libraries
 
 # %%
 import os
@@ -39,46 +39,34 @@
 import pandas as pd
 import quilt
 import skimage.transform
-import sklearn.model_selection
 import tqdm
 
-import keras
-from keras import backend as K
-from keras.layers import (
-    Add,
-    BatchNormalization,
-    Concatenate,
-    Conv2D,
-    Conv2DTranspose,
-    Dense,
-    Flatten,
-    Input,
-    Lambda,
-)
-from keras.layers.advanced_activations import LeakyReLU
-from keras.models import Model
+import chainer
+import chainer.functions as F
+import chainer.links as L
+import cupy
 import livelossplot
+import onnx_chainer
 
 from features.environment import _load_ipynb_modules
 
 print("Python       :", sys.version.split("\n")[0])
-print("Numpy        :", np.__version__)
-print("Keras        :", keras.__version__)
-print("Tensorflow   :", K.tf.__version__)
-K.tf.test.gpu_device_name()
+chainer.print_runtime_info()
 
 # %%
 # Set seed values
 seed = 42
 random.seed = seed
 np.random.seed(seed=seed)
-K.tf.set_random_seed(seed=seed)
+# cupy.random.seed(seed=seed)
 
 # Start tracking experiment using Comet.ML
-experiment = comet_ml.Experiment(workspace="weiji14", project_name="deepbedmap", disabled=False)
+experiment = comet_ml.Experiment(
+    workspace="weiji14", project_name="deepbedmap", disabled=False
+)
 
 # %% [markdown]
-# ## 1. Load data
+# # 1. Load data
 
 # %%
 hash = "1ccc9dc7f6344e1ec27b7aa972f2739d192d3e5adef8a64528b86bc799e2df60"
@@ -98,54 +86,69 @@
 print(W1_data.shape, W2_data.shape, X_data.shape, Y_data.shape)
 
 # %% [markdown]
-# ### Split dataset into training (train) and development (dev) sets
+# ## 1.1 Convert arrays for Chainer
+# - From Numpy (CPU) to CuPy (GPU) format
+# - From NHWC format to NCHW format, where N=number of tiles, H=height, W=width, C=channels
 
 # %%
-def train_dev_split(dataset: np.ndarray, test_size=0.05, random_state=42):
-    """
-    Split our dataset up into training and development sets.
-    Used for cross validation purposes to check for overfitting.
-
-    >>> dataset = np.ones(shape=(100, 4, 4, 1))
-    >>> train, dev = train_dev_split(dataset=dataset, test_size=0.05, random_state=42)
-    >>> train.shape
-    (95, 4, 4, 1)
-    >>> dev.shape
-    (5, 4, 4, 1)
-    """
-    return sklearn.model_selection.train_test_split(
-        dataset,
-        test_size=test_size,
-        train_size=1 - test_size,
-        random_state=random_state,
-        shuffle=True,
-    )
+# Detect if there is a CUDA GPU first
+try:
+    cupy.cuda.get_device_id()
+    xp = cupy
+    print("Using GPU")
+
+    W1_data = chainer.backend.cuda.to_gpu(array=W1_data)
+    W2_data = chainer.backend.cuda.to_gpu(array=W2_data)
+    X_data = chainer.backend.cuda.to_gpu(array=X_data)
+    Y_data = chainer.backend.cuda.to_gpu(array=Y_data)
+except:  # CUDARuntimeError
+    xp = np
+    print("Using CPU only")
 
+# %%
+W1_data = xp.rollaxis(a=W1_data, axis=3, start=1)
+W2_data = xp.rollaxis(a=W2_data, axis=3, start=1)
+X_data = xp.rollaxis(a=X_data, axis=3, start=1)
+Y_data = xp.rollaxis(a=Y_data, axis=3, start=1)
+print(W1_data.shape, W2_data.shape, X_data.shape, Y_data.shape)
+
+# %% [markdown]
+# ## 1.2 Split dataset into training (train) and development (dev) sets
 
 # %%
-W1_train, W1_dev = train_dev_split(dataset=W1_data)
-W2_train, W2_dev = train_dev_split(dataset=W2_data)
-X_train, X_dev = train_dev_split(dataset=X_data)
-Y_train, Y_dev = train_dev_split(dataset=Y_data)
+dataset = chainer.datasets.DictDataset(X=X_data, W1=W1_data, W2=W2_data, Y=Y_data)
+train_set, dev_set = chainer.datasets.split_dataset_random(
+    dataset=dataset, first_size=int(len(X_data) * 0.95), seed=seed
+)
+print(f"Training dataset: {len(train_set)} tiles, Test dataset: {len(dev_set)} tiles")
 
+# %%
+batch_size = 32
+train_iter = chainer.iterators.SerialIterator(
+    dataset=train_set, batch_size=batch_size, repeat=True, shuffle=True
+)
+dev_iter = chainer.iterators.SerialIterator(
+    dataset=dev_set, batch_size=batch_size, repeat=True, shuffle=False
+)
 
 # %% [markdown]
-# ## 2. Architect model **(Note: Work in Progress!!)**
+# # 2. Architect model **(Note: Work in Progress!!)**
 #
 # Enhanced Super Resolution Generative Adversarial Network (ESRGAN) model based on [Wang et al. 2018](https://arxiv.org/abs/1809.00219v2).
 # Refer to original Pytorch implementation at https://github.com/xinntao/ESRGAN.
-#
-# See also previous (non-enhanced) SRGAN model architecture based on [Ledig et al. 2017](https://arxiv.org/abs/1609.04802).
-# Keras implementation below takes some hints from  https://github.com/eriklindernoren/Keras-GAN/blob/master/srgan/srgan.py
+# See also previous (non-enhanced) SRGAN model architecture by [Ledig et al. 2017](https://arxiv.org/abs/1609.04802).
 
 # %% [markdown]
-# ### Generator Network Architecture
+# ## 2.1 Generator Network Architecture
 #
 # ![ESRGAN architecture - Generator Network composed of many Dense Convolutional Blocks](https://github.com/xinntao/ESRGAN/raw/master/figures/architecture.jpg)
-# ![The Residual in Residual Dense Block in detail](https://github.com/xinntao/ESRGAN/raw/master/figures/RRDB.png)
-# ![3 inputs feeding into the Generator Network, producing a high resolution prediction output](https://yuml.me/01862e1a.png)
 #
-# Details of the first convolutional layer:
+# 3 main components: 1) Input Block, 2) Residual Blocks, 3) Upsampling Blocks
+
+# %% [markdown]
+# ### 2.1.1 Input block, specially customized for DeepBedMap to take in 3 different inputs
+#
+# Details of the first convolutional layer for each input:
 #
 # - Input tiles are 8000m by 8000m.
 # - Convolution filter kernels are 3000m by 3000m.
@@ -157,128 +160,266 @@ def train_dev_split(dataset: np.ndarray, test_size=0.05, random_state=42):
 # - Convolution filter kernels are 30pixels by 30pixels
 # - Strides are 10pixels by 10pixels
 #
-# Note that first convolutional layer uses '**valid**' padding, see https://keras.io/layers/convolutional/ for more information.
+# Note that these first convolutional layers uses '**valid**' padding, see https://keras.io/layers/convolutional/ for more information.
+
+# %%
+class DeepbedmapInputBlock(chainer.Chain):
+    """
+    Custom input block for DeepBedMap.
+
+    Each filter kernel is 3km by 3km in size, with a 1km stride and no padding.
+    So for a 1km resolution image, (i.e. 1km pixel size):
+    kernel size is (3, 3), stride is (1, 1), and pad is (0, 0)
+
+      (?,1,10,10) --Conv2D-- (?,32,8,8) \
+    (?,1,100,100) --Conv2D-- (?,32,8,8) --Concat-- (?,96,8,8)
+      (?,1,20,20) --Conv2D-- (?,32,8,8) /
+
+    """
+
+    def __init__(self, out_channels=32):
+        super().__init__()
+        init_weights = chainer.initializers.GlorotUniform(scale=1.0)
+
+        with self.init_scope():
+            self.conv_on_X = L.Convolution2D(
+                in_channels=1,
+                out_channels=out_channels,
+                ksize=(3, 3),
+                stride=(1, 1),
+                pad=(0, 0),  # 'valid' padding
+                initialW=init_weights,
+            )
+            self.conv_on_W1 = L.Convolution2D(
+                in_channels=1,
+                out_channels=out_channels,
+                ksize=(30, 30),
+                stride=(10, 10),
+                pad=(0, 0),  # 'valid' padding
+                initialW=init_weights,
+            )
+            self.conv_on_W2 = L.Convolution2D(
+                in_channels=1,
+                out_channels=out_channels,
+                ksize=(6, 6),
+                stride=(2, 2),
+                pad=(0, 0),  # 'valid' padding
+                initialW=init_weights,
+            )
+
+    def forward(self, x, w1, w2):
+        """
+        Forward computation, i.e. evaluate based on inputs X, W1 and W2
+        """
+        x_ = self.conv_on_X(x)
+        w1_ = self.conv_on_W1(w1)
+        w2_ = self.conv_on_W2(w2)
+
+        a = F.concat(xs=(x_, w1_, w2_))
+        return a
+
+
+# %% [markdown]
+# ### 2.1.2 Residual Block
+#
+# ![The Residual in Residual Dense Block in detail](https://arxiv-sanity-sanity-production.s3.amazonaws.com/render-output/518727/x4.png)
+
+# %%
+class ResidualBlock(chainer.Chain):
+    """
+    Residual block made of Convoutional2D-LeakyReLU-Convoutional2D layers
+
+       -----------------------------
+      |                             |
+    -----Conv2D--LeakyReLu--Conv2D-(+)--
+
+    """
+
+    def __init__(self, out_channels=64):
+        super().__init__()
+        init_weights = chainer.initializers.GlorotUniform(scale=1.0)
+
+        with self.init_scope():
+            self.conv_layer1 = L.Convolution2D(
+                in_channels=None,
+                out_channels=out_channels,
+                ksize=(3, 3),
+                stride=(1, 1),
+                pad=1,  # 'same' padding
+                initialW=init_weights,
+            )
+            self.conv_layer2 = L.Convolution2D(
+                in_channels=out_channels,
+                out_channels=out_channels,
+                ksize=(3, 3),
+                stride=(1, 1),
+                pad=1,  # 'same' padding
+                initialW=init_weights,
+            )
+
+    def forward(self, x):
+        """
+        Forward computation, i.e. evaluate based on input x
+        """
+        a = self.conv_layer1(x)
+        a = F.leaky_relu(x=a, slope=0.2)
+        a = self.conv_layer2(a)
+
+        a = F.add(x, a)
+        return a
+
+
+# %% [markdown]
+# ### 2.1.3 Build the Generator Network, with upsampling layers!
+#
+# ![3 inputs feeding into the Generator Network, producing a high resolution prediction output](https://yuml.me/dffffcb0.png)
 #
 # <!--[W2_input(MEASURES)|20x20x1]-k6n32s2>[W2_inter|8x8x32],[W2_inter]->[Concat|8x8x96]
 # [X_input(BEDMAP2)|10x10x1]-k3n32s1>[X_inter|8x8x32],[X_inter]->[Concat|8x8x96]
 # [W1_input(REMA)|100x100x1]-k30n32s10>[W1_inter|8x8x32],[W1_inter]->[Concat|8x8x96]
-# [Concat|8x8x96]->[Generator-Network],[Generator-Network]->[Y_hat(High-Resolution_DEM)|32x32x1]-->
+# [Concat|8x8x96]->[Generator-Network|Many-Residual-Blocks],[Generator-Network]->[Y_hat(High-Resolution_DEM)|32x32x1]-->
 
 # %%
-def generator_network(
-    input1_shape: typing.Tuple[int, int, int] = (10, 10, 1),
-    input2_shape: typing.Tuple[int, int, int] = (100, 100, 1),
-    input3_shape: typing.Tuple[int, int, int] = (20, 20, 1),
-    num_residual_blocks: int = 16,
-    scaling: int = 4,
-    output_channels: int = 1,
-) -> keras.engine.network.Network:
+class GeneratorModel(chainer.Chain):
     """
     The generator network which is a deconvolutional neural network.
     Converts a low resolution input into a super resolution output.
 
+    Glues the input block with several residual blocks and upsampling layers
+
     Parameters:
       input_shape -- shape of input tensor in tuple format (height, width, channels)
       num_residual_blocks -- how many Conv-LeakyReLU-Conv blocks to use
       scaling -- even numbered integer to increase resolution (e.g. 0, 2, 4, 6, 8)
-      output_channels -- integer representing number of output channels/filters/kernels
+      out_channels -- integer representing number of output channels/filters/kernels
 
     Example:
       An input_shape of (8,8,1) passing through 16 residual blocks with a scaling of 4
       and output_channels 1 will result in an image of shape (32,32,1)
 
-    >>> generator_network().input_shape
-    [(None, 10, 10, 1), (None, 100, 100, 1), (None, 20, 20, 1)]
-    >>> generator_network().output_shape
-    (None, 32, 32, 1)
-    >>> generator_network().count_params()
+    >>> generator_model = GeneratorModel(
+    ...     inblock_class=DeepbedmapInputBlock,
+    ...     resblock_class=ResidualBlock,
+    ...     num_residual_blocks=16,
+    ... )
+    >>> y_pred = generator_model.forward(
+    ...     inputs={
+    ...         "x": np.random.rand(1, 1, 10, 10).astype("float32"),
+    ...         "w1": np.random.rand(1, 1, 100, 100).astype("float32"),
+    ...         "w2": np.random.rand(1, 1, 20, 20).astype("float32"),
+    ...     }
+    ... )
+    >>> y_pred.shape
+    (1, 1, 32, 32)
+    >>> generator_model.count_params()
     1604929
     """
 
-    assert num_residual_blocks >= 1  # ensure that we have 1 or more residual blocks
-    assert scaling % 2 == 0  # ensure scaling factor is even, i.e. 0, 2, 4, 8, etc
-    assert scaling >= 0  # ensure that scaling factor is zero or a positive number
-    assert output_channels >= 1  # ensure that we have 1 or more output channels
-
-    ## Input images
-    inp1 = Input(shape=input1_shape)  # low resolution image
-    assert inp1.shape.ndims == 4  # has to be shape like (?,10,10,1) for 10x10 grid
-    inp2 = Input(shape=input2_shape)  # other image (e.g. REMA)
-    assert inp2.shape.ndims == 4  # has to be shape like (?,100,100,1) for 100x100 grid
-    inp3 = Input(shape=input3_shape)  # other image (MEASURES Ice Flow)
-    assert inp3.shape.ndims == 4  # has to be shape like (?,20,20,1) for 20x20 grid
-
-    # 0 part
-    # Resize inputs to right scale using convolution (hardcoded kernel_size and strides)
-    inp1r = Conv2D(filters=32, kernel_size=(3, 3), strides=(1, 1), padding="valid")(
-        inp1
-    )
-    inp2r = Conv2D(filters=32, kernel_size=(30, 30), strides=(10, 10), padding="valid")(
-        inp2
-    )
-    inp3r = Conv2D(filters=32, kernel_size=(6, 6), strides=(2, 2), padding="valid")(
-        inp3
-    )
-
-    # Concatenate all inputs
-    # SEE https://distill.pub/2016/deconv-checkerboard/
-    X = Concatenate()([inp1r, inp2r, inp3r])  # Concatenate all the inputs together
-
-    # 1st part
-    # Pre-residual k3n64s1 (originally k9n64s1)
-    X0 = Conv2D(filters=64, kernel_size=(3, 3), strides=(1, 1), padding="same")(X)
-    X0 = LeakyReLU(alpha=0.2)(X0)
-
-    # 2nd part
-    # Residual blocks k3n64s1
-    def residual_block(input_tensor):
-        x = Conv2D(filters=64, kernel_size=(3, 3), strides=(1, 1), padding="same")(
-            input_tensor
-        )
-        x = LeakyReLU(alpha=0.2)(x)
-        x = Conv2D(filters=64, kernel_size=(3, 3), strides=(1, 1), padding="same")(x)
-        return Add()([x, input_tensor])
-
-    X = residual_block(X0)
-    for _ in range(num_residual_blocks - 1):
-        X = residual_block(X)
-
-    # 3rd part
-    # Post-residual blocks k3n64s1
-    X = Conv2D(filters=64, kernel_size=(3, 3), strides=(1, 1), padding="same")(X)
-    X = Add()([X, X0])
-
-    # 4th part
-    # Upsampling (if 4; run twice, if 8; run thrice, etc.) k3n256s1
-    for p, _ in enumerate(range(scaling // 2), start=1):
-        X = Conv2D(filters=256, kernel_size=(3, 3), strides=(1, 1), padding="same")(X)
-        pixelshuffleup = lambda images: K.tf.depth_to_space(input=images, block_size=2)
-        X = Lambda(function=pixelshuffleup, name=f"pixelshuffleup_{p}")(X)
-        X = LeakyReLU(alpha=0.2)(X)
-
-    # 5th part
-    # Generate high resolution output k9n1s1 (originally k9n3s1 for RGB image)
-    outp = Conv2D(
-        filters=output_channels,
-        kernel_size=(9, 9),
-        strides=(1, 1),
-        padding="same",
-        name="generator_output",
-    )(X)
-
-    # Create neural network with input low-res images and output prediction
-    network = keras.engine.network.Network(
-        inputs=[inp1, inp2, inp3], outputs=[outp], name="generator_network"
-    )
-
-    return network
+    def __init__(
+        self,
+        inblock_class,
+        resblock_class,
+        num_residual_blocks: int = 16,
+        out_channels: int = 1,
+    ):
+        super().__init__()
+        init_weights = chainer.initializers.GlorotUniform(scale=1.0)
+
+        with self.init_scope():
+
+            # Initial Input and Residual Blocks
+            self.input_block = inblock_class()
+            self.pre_residual_conv_layer = L.Convolution2D(
+                in_channels=None,
+                out_channels=64,
+                ksize=(3, 3),
+                stride=(1, 1),
+                pad=1,  # 'same' padding
+                initialW=init_weights,
+            )
+            self.residual_network = resblock_class().repeat(
+                n_repeat=num_residual_blocks
+            )
+            self.post_residual_conv_layer = L.Convolution2D(
+                in_channels=None,
+                out_channels=64,
+                ksize=(3, 3),
+                stride=(1, 1),
+                pad=1,  # 'same' padding
+                initialW=init_weights,
+            )
+
+            # Upsampling Layers
+            self.pre_upsample_conv_layer_1 = L.Convolution2D(
+                in_channels=None,
+                out_channels=256,
+                ksize=(3, 3),
+                stride=(1, 1),
+                pad=1,  # 'same' padding
+                initialW=init_weights,
+            )
+            self.pre_upsample_conv_layer_2 = L.Convolution2D(
+                in_channels=None,
+                out_channels=256,
+                ksize=(3, 3),
+                stride=(1, 1),
+                pad=1,  # 'same' padding
+                initialW=init_weights,
+            )
+            self.post_upsample_conv_layer = L.Convolution2D(
+                in_channels=None,
+                out_channels=out_channels,
+                ksize=(9, 9),
+                stride=(1, 1),
+                pad=4,  # 'same' padding
+                initialW=init_weights,
+            )
+
+    def forward(self, inputs: dict):
+        """
+        Forward computation, i.e. evaluate based on inputs
+
+        Input dictionary needs to have keys "x", "w1", "w2"
+        """
+        # 0 part
+        # Resize inputs o right scale using convolution (hardcoded kernel_size and strides)
+        # Also concatenate all inputs
+        a0 = self.input_block(x=inputs["x"], w1=inputs["w1"], w2=inputs["w2"])
+
+        # 1st part
+        # Pre-residual k3n64s1 (originally k9n64s1)
+        a1 = self.pre_residual_conv_layer(a0)
+        a1 = F.leaky_relu(x=a1, slope=0.2)
+
+        # 2nd part
+        # Residual blocks k3n64s1
+        a2 = self.residual_network(a1)
+
+        # 3rd part
+        # Post-residual blocks k3n64s1
+        a3 = self.post_residual_conv_layer(a2)
+        a3 = F.add(a1, a3)
+
+        # 4th part
+        # Upsampling (if 4; run twice, if 8; run thrice, etc.) k3n256s1
+        a4_1 = self.pre_upsample_conv_layer_1(a3)
+        a4_1 = F.depth2space(X=a4_1, r=2)
+        a4_1 = F.leaky_relu(x=a4_1, slope=0.2)
+        a4_2 = self.pre_upsample_conv_layer_2(a4_1)
+        a4_2 = F.depth2space(X=a4_2, r=2)
+        a4_2 = F.leaky_relu(x=a4_2, slope=0.2)
+
+        # 5th part
+        # Generate high resolution output k9n1s1 (originally k9n3s1 for RGB image)
+        a5 = self.post_upsample_conv_layer(a4_2)
+
+        return a5
 
 
 # %% [markdown]
-# ### Discriminator Network Architecture
+# ## 2.2 Discriminator Network Architecture
 #
 # Discriminator component is based on Deep Convolutional Generative Adversarial Networks by [Radford et al., 2015](https://arxiv.org/abs/1511.06434).
-# Keras implementation below takes some hints from https://github.com/erilyth/DCGANs/blob/master/DCGAN-CIFAR10/dcgan.py and https://github.com/yashk2810/DCGAN-Keras/blob/master/DCGAN.ipynb
 #
 # Note that figure below shows the 2017 (non-enhanced) SRGAN discriminator neural network architecture.
 # The 2018 ESRGAN version is basically the same architecture, as only the loss function was changed.
@@ -289,74 +430,122 @@ def residual_block(input_tensor):
 # ![Discriminator Network](https://yuml.me/diagram/scruffy/class/[High-Resolution_DEM|32x32x1]->[Discriminator-Network],[Discriminator-Network]->[False/True|0/1])
 
 # %%
-def discriminator_network(
-    input_shape: typing.Tuple[int, int, int] = (32, 32, 1)
-) -> keras.engine.network.Network:
+class DiscriminatorModel(chainer.Chain):
     """
     The discriminator network which is a convolutional neural network.
     Takes ONE high resolution input image and predicts whether it is
     real or fake on a scale of 0 to 1, where 0 is fake and 1 is real.
 
-    >>> discriminator_network().input_shape
-    (None, 32, 32, 1)
-    >>> discriminator_network().output_shape
-    (None, 1)
-    >>> discriminator_network().count_params()
-    6828033
-    """
-
-    ## Input images
-    inp = Input(shape=input_shape)  # high resolution/groundtruth image to discriminate
-    assert inp.shape.ndims == 4  # needs to be shape like (?,32,32,1) for 8x8 grid
+    Consists of several Conv2D-BatchNorm-LeakyReLU blocks, followed by
+    a fully connected linear layer with LeakyReLU activation and a final
+    fully connected linear layer with Sigmoid activation.
 
-    # 1st part
-    # Convolutonal Block without Batch Normalization k3n64s1
-    X = Conv2D(filters=64, kernel_size=(3, 3), strides=(1, 1), padding="same")(inp)
-    X = LeakyReLU(alpha=0.2)(X)
-
-    # 2nd part
-    # Convolutional Blocks with Batch Normalization k3n{64*f}s{1or2}
-    for f, s in zip([1, 1, 2, 2, 4, 4, 8, 8], [1, 2, 1, 2, 1, 2, 1, 2]):
-        X = Conv2D(filters=64 * f, kernel_size=(3, 3), strides=(s, s), padding="same")(
-            X
-        )
-        X = BatchNormalization()(X)
-        X = LeakyReLU(alpha=0.2)(X)
-
-    # 3rd part
-    # Flatten, Dense (Fully Connected) Layers and Output
-    X = Flatten()(X)
-    X = Dense(units=1024)(X)  # ??!! Flatten?
-    X = LeakyReLU(alpha=0.2)(X)
-    outp = Dense(units=1, activation="sigmoid", name="discriminator_output")(X)
-
-    # Create neural network with input highres/groundtruth images, output validity 0/1
-    network = keras.engine.network.Network(
-        inputs=[inp], outputs=[outp], name="discriminator_network"
-    )
+    >>> discriminator_model = DiscriminatorModel()
+    >>> y_pred = discriminator_model.forward(
+    ...     inputs={
+    ...         "x": np.random.rand(2, 1, 32, 32).astype("float32"),
+    ...     }
+    ... )
+    >>> y_pred.shape
+    (2, 1)
+    >>> discriminator_model.count_params()
+    6824193
+    """
 
-    return network
+    def __init__(self):
+        super().__init__()
+        init_weights = chainer.initializers.GlorotUniform(scale=1.0)
+
+        with self.init_scope():
+
+            self.conv_layer0 = L.Convolution2D(
+                in_channels=None,
+                out_channels=64,
+                ksize=(3, 3),
+                stride=(1, 1),
+                pad=1,  # 'same' padding
+                nobias=False,  # default, have bias
+                initialW=init_weights,
+            )
+            self.conv_layer1 = L.Convolution2D(None, 64, 3, 1, 1, False, init_weights)
+            self.conv_layer2 = L.Convolution2D(None, 64, 3, 2, 1, False, init_weights)
+            self.conv_layer3 = L.Convolution2D(None, 128, 3, 1, 1, False, init_weights)
+            self.conv_layer4 = L.Convolution2D(None, 128, 3, 2, 1, False, init_weights)
+            self.conv_layer5 = L.Convolution2D(None, 256, 3, 1, 1, False, init_weights)
+            self.conv_layer6 = L.Convolution2D(None, 256, 3, 2, 1, False, init_weights)
+            self.conv_layer7 = L.Convolution2D(None, 512, 3, 1, 1, False, init_weights)
+            self.conv_layer8 = L.Convolution2D(None, 512, 3, 2, 1, False, init_weights)
+
+            self.batch_norm1 = L.BatchNormalization(axis=(0, 2, 3), eps=0.001)
+            self.batch_norm2 = L.BatchNormalization(axis=(0, 2, 3), eps=0.001)
+            self.batch_norm3 = L.BatchNormalization(axis=(0, 2, 3), eps=0.001)
+            self.batch_norm4 = L.BatchNormalization(axis=(0, 2, 3), eps=0.001)
+            self.batch_norm5 = L.BatchNormalization(axis=(0, 2, 3), eps=0.001)
+            self.batch_norm6 = L.BatchNormalization(axis=(0, 2, 3), eps=0.001)
+            self.batch_norm7 = L.BatchNormalization(axis=(0, 2, 3), eps=0.001)
+            self.batch_norm8 = L.BatchNormalization(axis=(0, 2, 3), eps=0.001)
+
+            self.linear_1 = L.Linear(in_size=None, out_size=1024, initialW=init_weights)
+            self.linear_2 = L.Linear(in_size=None, out_size=1, initialW=init_weights)
+
+    def forward(self, inputs: dict):
+        """
+        Forward computation, i.e. evaluate based on inputs
+
+        Input dictionary needs to have keys "x"
+        """
+
+        # 1st part
+        # Convolutonal Block without Batch Normalization k3n64s1
+        a0 = self.conv_layer0(x=inputs["x"])
+        a0 = F.leaky_relu(x=a0, slope=0.2)
+
+        # 2nd part
+        # Convolutional Blocks with Batch Normalization k3n{64*f}s{1or2}
+        a1 = self.conv_layer1(x=a0)
+        a1 = self.batch_norm1(x=a1)
+        a1 = F.leaky_relu(x=a1, slope=0.2)
+        a2 = self.conv_layer2(x=a1)
+        a2 = self.batch_norm2(x=a2)
+        a2 = F.leaky_relu(x=a2, slope=0.2)
+        a3 = self.conv_layer3(x=a2)
+        a3 = self.batch_norm3(x=a3)
+        a3 = F.leaky_relu(x=a3, slope=0.2)
+        a4 = self.conv_layer4(x=a3)
+        a4 = self.batch_norm4(x=a4)
+        a4 = F.leaky_relu(x=a4, slope=0.2)
+        a5 = self.conv_layer5(x=a4)
+        a5 = self.batch_norm5(x=a5)
+        a5 = F.leaky_relu(x=a5, slope=0.2)
+        a6 = self.conv_layer6(x=a5)
+        a6 = self.batch_norm6(x=a6)
+        a6 = F.leaky_relu(x=a6, slope=0.2)
+        a7 = self.conv_layer7(x=a6)
+        a7 = self.batch_norm7(x=a7)
+        a7 = F.leaky_relu(x=a7, slope=0.2)
+        a8 = self.conv_layer8(x=a7)
+        a8 = self.batch_norm8(x=a8)
+        a8 = F.leaky_relu(x=a8, slope=0.2)
+
+        # 3rd part
+        # Flatten, Dense (Fully Connected) Layers and Output
+        a9 = F.reshape(x=a8, shape=(len(a8), -1))  # flatten while keeping batch_size
+        a9 = self.linear_1(x=a9)
+        a9 = F.leaky_relu(x=a9, slope=0.2)
+        a10 = self.linear_2(x=a9)
+        # a10 = F.sigmoid(x=a10)  # no sigmoid activation, as it is in the loss function
+
+        return a10
 
 
 # %% [markdown]
-# ### Combine Generator and Discriminator Networks
+# ## 2.3 Define Loss function and Metrics for the Generator and Discriminator Networks
 #
-# Here we combine the Generator and Discriminator neural network models together, and define the Perceptual Loss function where:
+# Now we define the Perceptual Loss function for our Generator and Discriminator neural network models, where:
 #
 # $$Perceptual Loss = Content Loss + Adversarial Loss$$
 #
-# The original SRGAN paper by [Ledig et al. 2017](https://arxiv.org/abs/1609.04802v5) calculates *Content Loss* based on the ReLU activation layers of the pre-trained 19 layer VGG network.
-# The implementation below is less advanced, simply using an L1 loss, i.e., a pixel-wise [Mean Absolute Error (MAE) loss](https://keras.io/losses/#mean_absolute_error) as the *Content Loss*.
-# Specifically, the *Content Loss* is calculated as the MAE difference between the output of the generator model (i.e. the predicted Super Resolution Image) and that of the groundtruth image (i.e. the true High Resolution Image).
-#
-# The *Adversarial Loss* or *Generative Loss* (confusing I know) is the same as in the original SRGAN paper.
-# It is defined based on the probabilities of the discriminator believing that the reconstructed Super Resolution Image is a natural High Resolution Image.
-# The implementation below uses the [Binary CrossEntropy loss](https://keras.io/losses/#binary_crossentropy).
-# Specifically, this *Adversarial Loss* is calculated between the output of the discriminator model (a value between 0 and 1) and that of the groundtruth label (a boolean value of either 0 or 1).
-#
-# Source code for the implementations of these loss functions in Keras can be found at https://github.com/keras-team/keras/blob/master/keras/losses.py.
-#
-# ![Perceptual Loss in an Enhanced Super Resolution Generative Adversarial Network](https://yuml.me/db58d683.png )
+# ![Perceptual Loss in an Enhanced Super Resolution Generative Adversarial Network](https://yuml.me/db58d683.png)
 #
 # <!--
 # [LowRes-Inputs]-Generator>[SuperResolution_DEM]
@@ -371,115 +560,185 @@ def discriminator_network(
 # [note:Adversarial-Loss]-.->[note:Perceptual-Loss{bg:gold}]
 # -->
 
+# %% [markdown]
+# ### Content Loss
+#
+# The original SRGAN paper by [Ledig et al. 2017](https://arxiv.org/abs/1609.04802v5) calculates *Content Loss* based on the ReLU activation layers of the pre-trained 19 layer VGG network.
+# The implementation below is less advanced, simply using an L1 loss, i.e., a pixel-wise [Mean Absolute Error (MAE) loss](https://keras.io/losses/#mean_absolute_error) as the *Content Loss*.
+# Specifically, the *Content Loss* is calculated as the MAE difference between the output of the generator model (i.e. the predicted Super Resolution Image) and that of the groundtruth image (i.e. the true High Resolution Image).
+#
+# $$ e_i = ||G(x_{i}) - y_i||_{1} $$
+#
+# $$ Loss_{Content} = Mean Absolute Error = \dfrac{1}{n} \sum\limits_{i=1}^n e_i $$
+#
+# where $G(x_{i})$ is the Generator Network's predicted value, and $y_i$ is the groundtruth value, respectively at pixel $i$.
+# $e_i$ thus represents the absolute error (L1 loss) (denoted by $||\dots||_{1}$) between the predicted and groundtruth value.
+# We then sum all the pixel-wise errors $e_i,\dots,e_n$ and divide by the number of pixels $n$ to get the Arithmetic Mean $\dfrac{1}{n} \sum\limits_{i=1}^n$ of our error which is our *Content Loss*.
+
+# %% [markdown]
+# ### Adversarial Loss
+#
+# The *Adversarial Loss* or *Generative Loss* (confusing I know) is the same as in the original SRGAN paper.
+# It is defined based on the probabilities of the discriminator believing that the reconstructed Super Resolution Image is a natural High Resolution Image.
+# The implementation below uses the [Binary CrossEntropy loss](https://keras.io/losses/#binary_crossentropy).
+# Specifically, this *Adversarial Loss* is calculated between the output of the discriminator model (a value between 0 and 1) and that of the groundtruth label (a boolean value of either 0 or 1).
+#
+# $$ Loss_{Adversarial} = Binary Cross Entropy Loss = -\dfrac{1}{n} \sum\limits_{i=1}^n ( y_i ln(\sigma(x_i)) + (1-y_i) ln(1 - \sigma(x_i) ) $$
+#
+# where $\sigma$ is the [Sigmoid](https://en.wikipedia.org/wiki/Sigmoid_function) activation function, $\sigma = \dfrac{1}{1+e^{-x}} = \dfrac{e^x}{e^x+1}$, $y_i$ is the groundtruth label (1 for real, 0 for fake) and $x_i$ is the prediction (before sigmoid activation is applied), all respectively at pixel $i$.
+#
+# $\sigma(x)$ is basically the sigmoid activated output from a Standard Discriminator neural network, which some people also denote as $D(.)$.
+# Technically, some people also write $D(x) = \sigma(C(x))$, where $C(x)$ is the raw, non-transformed output from the Discriminator neural network (i.e. no sigmoid activation applied) on the input data $x$.
+# For simplicity, we now denote $C(x)$ simply as $x$ in the following equations, i.e. using $\sigma(x)$ to replace $\sigma(C(x))$.
+#
+# Again, the [Binary Cross Entropy Loss](https://en.wikipedia.org/wiki/Cross_entropy#Cross-entropy_error_function_and_logistic_regression) calculated on one pixel is defined as follows:
+#
+# $$ -( y ln(\sigma(x)) + (1-y) ln(1 - \sigma(x) )$$
+#
+# With the full expansion as such:
+#
+# $$ -\bigg[ y ln\big(\dfrac{e^x}{e^x+1}\big) + (1-y) ln\big(1 - \dfrac{e^x}{e^x+1}\big) \bigg] $$
+#
+# The above equation is mathematically equivalent to the one below, and can be derived using [Logarithm rules](https://en.wikipedia.org/wiki/Logarithm#Product,_quotient,_power,_and_root) such as the Power Rule and Product Rule, and using the fact that $ln(e)=1$ and $ln(1)=0$:
+#
+# $$ -[ xy - ln(1+e^x) ] $$
+#
+# However, this reformed equation is numerically unstable (see discussion [here](https://www.reddit.com/r/MachineLearning/comments/4euzmk/logsumexp_for_logistic_regression/)), and is good for values of $x<0$.
+# For values of $x>=0$, there is an alternative representation which we can derive:
+#
+# $$ -[ xy - ln(1+e^x) - x + x ] $$
+# $$ -[ x(y-1) - ln(1 + e^x) + ln(e^x) ] $$
+# $$ -\bigg[ x(y-1) - ln\big(\dfrac{e^x}{1+e^x}\big) \bigg] $$
+# $$ -\bigg[ x(y-1) - ln\big(\dfrac{1}{1+e^{-x}}\big) \bigg] $$
+# $$ - [ x(y-1) - ln(1) + ln(1+e^{-x}) ] $$
+# $$ - [ x(y-1) + ln(1+e^{-x}) $$
+#
+# In order to have a numerically stable function that works for both $x<0$ and $x>=0$, we can write it like so as in Caffe's implementation:
+#
+# $$ -[ x(y - 1_{x>=0} - ln(1+e^{x-2x\cdot1_{x>=0}}) ] $$
+#
+# Alternatively, Chainer does it like so:
+#
+# $$ -[ x(y - 1_{x>=0} - ln(1+e^{-|x|}) ] $$
+#
+# Or in Python code (the Chainer implemention from [here](https://github.com/chainer/chainer/blob/v6.0.0b1/chainer/functions/loss/sigmoid_cross_entropy.py#L41-L44)), bearing in mind that the natural logarithm $ln$ is `np.log` in Numpy:
+#
+# ```python
+#     sigmoidbinarycrossentropyloss = -(x * (y - (x >= 0)) - np.log1p(np.exp(-np.abs(x))))
+# ```
+#
+# See also how [Pytorch](https://pytorch.org/docs/stable/nn.html?highlight=bcewithlogitsloss#torch.nn.BCEWithLogitsLoss) and [Tensorflow](https://www.tensorflow.org/api_docs/python/tf/nn/sigmoid_cross_entropy_with_logits) implements this in a numerically stable manner.
+
 # %%
-def compile_srgan_model(
-    g_network: keras.engine.network.Network,
-    d_network: keras.engine.network.Network,
-    metrics: typing.Dict[str, str] = None,
-) -> typing.Dict[str, keras.engine.training.Model]:
+def calculate_generator_loss(
+    y_pred: chainer.variable.Variable,
+    y_true: cupy.ndarray,
+    pred_labels: cupy.ndarray,
+    true_labels: cupy.ndarray,
+) -> chainer.variable.Variable:
     """
-    Creates a Super Resolution Generative Adversarial Network (SRGAN)
-    by joining a generator network with a discriminator network.
-
-    Returns a dictionary containing:
-    1) generator model (trainable, not compiled)
-    2) discriminator model (trainable, compiled)
-    3) srgan model (trainable generator, untrainable discriminator, compiled)
-
-    The SRGAN model will be compiled with an optimizer (e.g. Adam)
-    and have separate loss functions and metrics for its
-    generator and discriminator component.
-
-    >>> metrics = {"generator_network": 'mse', "discriminator_network": 'accuracy'}
-    >>> models = compile_srgan_model(
-    ...     g_network=generator_network(),
-    ...     d_network=discriminator_network(),
-    ...     metrics=metrics,
+    Calculate the batchwise loss of the Generator Network.
+
+    >>> calculate_generator_loss(
+    ...     y_pred=chainer.variable.Variable(data=np.ones(shape=(2, 1, 3, 3))),
+    ...     y_true=np.full(shape=(2, 1, 3, 3), fill_value=10.0),
+    ...     pred_labels=np.zeros(shape=(2, 1, 3, 3)),
+    ...     true_labels=np.ones(shape=(2, 1, 3, 3)).astype(np.int32),
     ... )
-    >>> models['discriminator_model'].trainable
-    True
-    >>> models['srgan_model'].get_layer(name='generator_network').trainable
-    True
-    >>> models['srgan_model'].get_layer(name='discriminator_network').trainable
-    False
-    >>> models['srgan_model'].count_params()
-    8432962
+    variable(9.69314718)
     """
+    # Content Loss (L1, Mean Absolute Error)
+    content_loss = F.mean_absolute_error(x0=y_pred, x1=y_true)
 
-    # Check that our neural networks are named properly
-    assert g_network.name == "generator_network"
-    assert d_network.name == "discriminator_network"
-    assert g_network.trainable == True  # check that generator is trainable
-    assert d_network.trainable == True  # check that discriminator is trainable
+    # Adversarial Loss
+    adversarial_loss = F.sigmoid_cross_entropy(x=pred_labels, t=true_labels)
 
-    ## Both trainable
-    # Create keras models (trainable) out of the networks (graph only)
-    g_model = Model(
-        inputs=g_network.inputs, outputs=g_network.outputs, name="generator_model"
-    )
-    d_model = Model(
-        inputs=d_network.inputs, outputs=d_network.outputs, name="discriminator_model"
-    )
-    d_model.compile(
-        optimizer=keras.optimizers.Adam(lr=0.001),
-        loss={"discriminator_output": keras.losses.binary_crossentropy},
-    )
+    # Get generator loss
+    g_loss = (1 * content_loss) + (1 * adversarial_loss)
+    g_loss
+    return g_loss
 
-    ## One trainable (generator), one untrainable (discriminator)
-    # Connect Generator Network to Discriminator Network
-    g_out = g_network(inputs=g_network.inputs)  # g_in --(g_network)--> g_out
-    d_out = d_network(inputs=g_out)  # g_out --(d_network)--> d_out
-
-    # Create and Compile the Super Resolution Generative Adversarial Network Model!
-    model = Model(inputs=g_network.inputs, outputs=[g_out, d_out])
-    model.get_layer(
-        name="discriminator_network"
-    ).trainable = False  # combined model should not train discriminator
-    model.compile(
-        optimizer=keras.optimizers.Adam(lr=0.001),
-        loss={
-            "generator_network": keras.losses.mean_absolute_error,
-            "discriminator_network": keras.losses.binary_crossentropy,
-        },
-        metrics=metrics,
-    )
 
-    return {
-        "generator_model": g_model,
-        "discriminator_model": d_model,
-        "srgan_model": model,
-    }
+# %%
+def psnr(
+    y_true: cupy.ndarray, y_pred: cupy.ndarray, data_range=2 ** 32
+) -> cupy.ndarray:
+    """
+    Peak Signal-Noise Ratio (PSNR) metric, calculated batchwise.
+    See https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio#Definition
+
+    Can take in either numpy (CPU) or cupy (GPU) arrays as input.
+    Implementation is same as skimage.measure.compare_psnr with data_range=2**32
+
+    >>> psnr(
+    ...     y_true=np.ones(shape=(2, 1, 3, 3)),
+    ...     y_pred=np.full(shape=(2, 1, 3, 3), fill_value=2),
+    ... )
+    192.65919722494797
+    """
+    xp = chainer.backend.get_array_module(y_true)
+
+    # Calculate Mean Squred Error along predetermined axes
+    mse = xp.mean(xp.square(xp.subtract(y_pred, y_true)), axis=None)
+
+    # Calculate Peak Signal-Noise Ratio, setting MAX_I as 2^32, i.e. max for int32
+    return xp.multiply(20, xp.log10(data_range / xp.sqrt(mse)))
 
 
 # %%
-def psnr(y_true: np.ndarray, y_pred: np.ndarray) -> np.ndarray:
+def calculate_discriminator_loss(
+    y_pred: chainer.variable.Variable, y_true: cupy.ndarray
+) -> chainer.variable.Variable:
     """
-    Peak Signal-Noise Ratio (PSNR) metric.
-    See https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio#Definition
+    Calculate the batchwise loss of the Discriminator Network.
 
-    >>> y_true, y_pred = np.ones(shape=(3, 3)), np.full(shape=(3, 3), fill_value=2)
-    >>> K.eval(psnr(y_true=y_true, y_pred=y_pred))
-    array([221.80709678, 221.80709678, 221.80709678])
+    Original formula:
+    -(y * np.log(sigmoid(x)) + (1 - y) * np.log(1 - sigmoid(x)))
+
+    Numerically stable formula:
+    -(x * (y - (x >= 0)) - np.log1p(np.exp(-np.abs(x))))
+
+    >>> calculate_discriminator_loss(
+    ...     y_pred=chainer.variable.Variable(data=np.array([[0.5], [1.5], [-0.5]])),
+    ...     y_true=np.array([[0], [1], [0]]),
+    ... )
+    variable(0.54985575)
     """
 
-    mse = (
-        K.mean(K.square(K.np.subtract(y_pred, y_true)), axis=-1) + K.epsilon()
-    )  # add epsilon to prevent zero division
-    return K.np.multiply(
-        20, K.log(2 ** 16 / K.sqrt(mse))
-    )  # setting MAX_I as 2^16, i.e. max for int16
+    # Binary Cross-Entropy Loss
+    bce_loss = F.sigmoid_cross_entropy(x=y_pred, t=y_true)
+
+    # Get discriminator loss
+    d_loss = bce_loss
+
+    return d_loss
 
 
 # %%
-K.clear_session()  # Reset Keras/Tensorflow graph
-metrics = {"generator_network": psnr, "discriminator_network": "accuracy"}
-models = compile_srgan_model(
-    g_network=generator_network(), d_network=discriminator_network(), metrics=metrics
+# Build the models
+generator_model = GeneratorModel(
+    inblock_class=DeepbedmapInputBlock,
+    resblock_class=ResidualBlock,
+    num_residual_blocks=16,
+)
+discriminator_model = DiscriminatorModel()
+
+# Transfer models to GPU if available
+if xp == cupy:  # Check if CuPy was loaded, i.e. GPU is available
+    generator_model.to_gpu(device=0)
+    discriminator_model.to_gpu(device=0)
+
+# %%
+# Setup optimizer, using Adam
+generator_optimizer = chainer.optimizers.Adam(alpha=0.001, eps=1e-7).setup(
+    link=generator_model
+)
+discriminator_optimizer = chainer.optimizers.Adam(alpha=0.001, eps=1e-7).setup(
+    link=discriminator_model
 )
-models["srgan_model"].summary()
 
 # %% [markdown]
-# ## 3. Train model
+# # 3. Train model
 #
 # [Gherkin](https://en.wikipedia.org/wiki/Gherkin_(language))/Plain English statement at what the Super-Resolution Generative Adversarial Network below does
 #
@@ -493,22 +752,24 @@ def psnr(y_true: np.ndarray, y_pred: np.ndarray) -> np.ndarray:
 #       Scenario: Train discriminator to beat generator
 #         Given fake generated images from a generator
 #           And real groundtruth images
-#          When the two sets of images are fed into the discriminator
-#          Then the discriminator should know the fakes from the real images
+#          When the two sets of images are fed into the discriminator for comparison
+#          Then the discriminator should learn to know the fakes from the real images
 #
 #       Scenario: Train generator to fool discriminator
-#         Given what we think the discriminator believes is real
-#          When our inputs are fed into the super resolution model
-#          Then the generator should create a more authentic looking image
+#         Given fake generated images from a generator
+#           And what we think the discriminator believes is real
+#          When we compare the fake images to the real ones
+#          Then the generator should learn to create a more authentic looking image
 # ```
 
 # %%
-def train_discriminator(
-    models: typing.Dict[str, keras.engine.training.Model],
-    generator_inputs: typing.List[np.ndarray],
-    groundtruth_images: np.ndarray,
-    verbose: int = 1,
-) -> (typing.Dict[str, keras.engine.training.Model], list):
+def train_eval_discriminator(
+    input_arrays: typing.Dict[str, cupy.ndarray],
+    g_model,
+    d_model,
+    d_optimizer=None,
+    train: bool = True,
+) -> (float, float):
     """
     Trains the Discriminator within a Super Resolution Generative Adversarial Network.
     Discriminator is trainable, Generator is not trained (only produces predictions).
@@ -518,194 +779,267 @@ def train_discriminator(
     - Fake images combined with real groundtruth images
     - Discriminator trained with these images and their Fake(0)/Real(1) labels
 
-    >>> generator_inputs = [
-    ...     np.random.RandomState(seed=42).rand(32, s, s, 1) for s in [10, 100, 20]
-    ... ]
-    >>> groundtruth_images = np.random.RandomState(seed=42).rand(32,32,32,1)
-    >>> models = compile_srgan_model(
-    ...     g_network=generator_network(), d_network=discriminator_network()
+    >>> train_arrays = {
+    ...     "X": np.random.RandomState(seed=42).rand(2, 1, 10, 10).astype(np.float32),
+    ...     "W1": np.random.RandomState(seed=42).rand(2, 1, 100, 100).astype(np.float32),
+    ...     "W2": np.random.RandomState(seed=42).rand(2, 1, 20, 20).astype(np.float32),
+    ...     "Y": np.random.RandomState(seed=42).rand(2, 1, 32, 32).astype(np.float32),
+    ... }
+    >>> discriminator_model = DiscriminatorModel()
+    >>> discriminator_optimizer = chainer.optimizers.Adam(alpha=0.001, eps=1e-7).setup(
+    ...     link=discriminator_model
     ... )
-
-    >>> d_weight0 = K.eval(models['discriminator_model'].weights[0][0,0,0,0])
-    >>> _, _ = train_discriminator(
-    ...     models=models,
-    ...     generator_inputs=generator_inputs,
-    ...     groundtruth_images=groundtruth_images,
-    ...     verbose=0,
+    >>> generator_model = GeneratorModel(
+    ...     inblock_class=DeepbedmapInputBlock,
+    ...     resblock_class=ResidualBlock,
+    ...     num_residual_blocks=1,
     ... )
-    >>> d_weight1 = K.eval(models['discriminator_model'].weights[0][0,0,0,0])
 
+    >>> d_weight0 = [d for d in discriminator_model.params()][-1][0].array
+    >>> d_train_loss, d_train_accu = train_eval_discriminator(
+    ...     input_arrays=train_arrays,
+    ...     g_model=generator_model,
+    ...     d_model=discriminator_model,
+    ...     d_optimizer=discriminator_optimizer,
+    ... )
+    >>> d_weight1 = [d for d in discriminator_model.params()][-1][0].array
     >>> d_weight0 != d_weight1  #check that training has occurred (i.e. weights changed)
     True
     """
-
-    # hardcoded check that we are passing in 3 numpy arrays as input
-    assert len(generator_inputs) == 3
-    # check that X_data and W1_data have same length (batch size)
-    assert generator_inputs[0].shape[0] == generator_inputs[1].shape[0]
-    # check that X_data and W2_data have same length (batch size)
-    assert generator_inputs[0].shape[0] == generator_inputs[2].shape[0]
-
     # @pytest.fixture
-    g_model = models["generator_model"]
-    d_model = models["discriminator_model"]
+    if train == True:
+        assert d_optimizer is not None  # Optimizer required for neural network training
+    xp = chainer.backend.get_array_module(input_arrays["Y"])
 
     # @given("fake generated images from a generator")
-    fake_images = g_model.predict(x=generator_inputs, batch_size=32)
-    fake_labels = np.zeros(shape=len(generator_inputs[0]))
+    generator_inputs = {
+        "x": input_arrays["X"],
+        "w1": input_arrays["W1"],
+        "w2": input_arrays["W2"],
+    }
+    fake_images = g_model.forward(inputs=generator_inputs).array
+    fake_labels = xp.zeros(shape=(len(fake_images), 1)).astype(xp.int32)
 
     # @given("real groundtruth images")
-    real_images = groundtruth_images  # groundtruth images i.e. Y_data
-    real_labels = np.ones(shape=len(groundtruth_images))
+    real_images = input_arrays["Y"]
+    real_labels = xp.ones(shape=(len(real_images), 1)).astype(xp.int32)
+
+    # @when("the two sets of images are fed into the discriminator for comparison")
+    images = xp.concatenate([fake_images, real_images])
+    labels = xp.concatenate([fake_labels, real_labels])
+    y_pred = d_model.forward(inputs={"x": images})
 
-    # @when("the two sets of images are fed into the discriminator")
-    images = np.concatenate([fake_images, real_images])
-    labels = np.concatenate([fake_labels, real_labels])
-    assert d_model.trainable == True
-    d_metrics = d_model.fit(
-        x=images, y=labels, epochs=1, batch_size=32, shuffle=True, verbose=verbose
-    ).history
+    d_loss = calculate_discriminator_loss(y_pred=y_pred, y_true=labels)
+    d_accu = F.binary_accuracy(y=y_pred, t=labels)
 
-    # @then("the discriminator should know the fakes from the real images")
-    # assert d_weight0 != d_weight1  # check that training occurred i.e. weights changed
+    # @then("the discriminator should learn to know the fakes from the real images")
+    if train == True:
+        d_model.cleargrads()  # clear/zero all gradients
+        d_loss.backward()  # renew gradients
+        d_optimizer.update()  # backpropagate the loss using optimizer
 
-    return models, d_metrics["loss"][0]
+    return float(d_loss.array), float(d_accu.array)  # return discriminator metrics
 
 
 # %%
-def train_generator(
-    models: typing.Dict[str, keras.engine.training.Model],
-    generator_inputs: typing.List[np.ndarray],
-    groundtruth_images: np.ndarray,
-    verbose: int = 1,
-) -> (typing.Dict[str, keras.engine.training.Model], list):
+def train_eval_generator(
+    input_arrays: typing.Dict[str, cupy.ndarray],
+    g_model,
+    d_model,
+    g_optimizer=None,
+    train: bool = True,
+) -> (float, float):
     """
-    Trains the Generator within a Super Resolution Generative Adversarial Network.
+    Evaluates and/or trains the Generator for one minibatch
+    within a Super Resolution Generative Adversarial Network.
     Discriminator is not trainable, Generator is trained.
 
+    If train is set to False, only forward pass is run, i.e. evaluation/prediction only
+    If train is set to True, forward and backward pass are run, i.e. train with backprop
+
     Steps:
-    - Labels of the SRGAN output are set to Real(1)
-    - Generator is trained to match these Real(1) labels
-
-    >>> generator_inputs = [
-    ...     np.random.RandomState(seed=42).rand(32, s, s, 1) for s in [10, 100, 20]
-    ... ]
-    >>> groundtruth_images = np.random.RandomState(seed=42).rand(32,32,32,1)
-    >>> models = compile_srgan_model(
-    ...     g_network=generator_network(), d_network=discriminator_network()
+    - Generator produces fake images
+    - Fake images compared with real groundtruth images
+    - Generator is trained to be more like real image
+
+    >>> train_arrays = {
+    ...     "X": np.random.RandomState(seed=42).rand(2, 1, 10, 10).astype(np.float32),
+    ...     "W1": np.random.RandomState(seed=42).rand(2, 1, 100, 100).astype(np.float32),
+    ...     "W2": np.random.RandomState(seed=42).rand(2, 1, 20, 20).astype(np.float32),
+    ...     "Y": np.random.RandomState(seed=42).rand(2, 1, 32, 32).astype(np.float32),
+    ... }
+    >>> generator_model = GeneratorModel(
+    ...     inblock_class=DeepbedmapInputBlock,
+    ...     resblock_class=ResidualBlock,
+    ...     num_residual_blocks=1,
     ... )
-
-    >>> g_weight0 = K.eval(models['generator_model'].weights[0][0,0,0,0])
-    >>> _, _ = train_generator(
-    ...     models=models,
-    ...     generator_inputs=generator_inputs,
-    ...     groundtruth_images=groundtruth_images,
-    ...     verbose=0,
+    >>> generator_optimizer = chainer.optimizers.Adam(alpha=0.001, eps=1e-7).setup(
+    ...     link=generator_model
     ... )
-    >>> g_weight1 = K.eval(models['generator_model'].weights[0][0,0,0,0])
-
+    >>> discriminator_model = DiscriminatorModel()
+
+    >>> g_weight0 = [g for g in generator_model.params()][0][0, 0, 0, 0].array
+    >>> _ = train_eval_generator(
+    ...     input_arrays=train_arrays,
+    ...     g_model=generator_model,
+    ...     d_model=discriminator_model,
+    ...     g_optimizer=generator_optimizer,
+    ... )
+    >>> g_weight1 = [g for g in generator_model.params()][0][0, 0, 0, 0].array
     >>> g_weight0 != g_weight1  #check that training has occurred (i.e. weights changed)
     True
     """
 
     # @pytest.fixture
-    srgan_model = models["srgan_model"]
+    if train == True:
+        assert g_optimizer is not None  # Optimizer required for neural network training
+    xp = chainer.backend.get_array_module(input_arrays["Y"])
 
-    # @given("what we think the discriminator believes is real")
-    true_labels = np.ones(shape=len(generator_inputs[0]))
+    # @given("fake generated images from a generator")
+    generator_inputs = {
+        "x": input_arrays["X"],
+        "w1": input_arrays["W1"],
+        "w2": input_arrays["W2"],
+    }
+    y_pred = g_model.forward(inputs=generator_inputs)
+    predicted_labels = d_model.forward(inputs={"x": y_pred}).array
 
-    # @when("our inputs are fed into the super resolution model")
-    assert srgan_model.get_layer(name="discriminator_network").trainable == False
-    g_metrics = srgan_model.fit(
-        x=generator_inputs,
-        y={
-            "generator_network": groundtruth_images,
-            "discriminator_network": true_labels,
-        },
-        batch_size=32,
-        verbose=verbose,
-    ).history
+    # @given("what we think the discriminator believes is real")
+    groundtruth_images = input_arrays["Y"]
+    groundtruth_labels = xp.ones(shape=(len(groundtruth_images), 1)).astype(xp.int32)
+
+    # @when("we compare the fake images to the real ones")
+    g_loss = calculate_generator_loss(
+        y_pred=y_pred,
+        y_true=groundtruth_images,
+        pred_labels=predicted_labels,
+        true_labels=groundtruth_labels,
+    )
+    g_psnr = psnr(y_pred=y_pred.array, y_true=groundtruth_images)
 
-    # @then("the generator should create a more authentic looking image")
-    # assert g_weight0 != g_weight1  # check that training occurred i.e. weights changed
+    # @then("the generator should learn to create a more authentic looking image")
+    if train == True:
+        g_model.cleargrads()  # clear/zero all gradients
+        g_loss.backward()  # renew gradients
+        g_optimizer.update()  # backpropagate the loss using optimizer
 
-    return models, [m[0] for m in g_metrics.values()]
+    return float(g_loss.array), float(g_psnr)  # return generator loss and metric values
 
 
 # %%
 epochs = 100
-with tqdm.trange(epochs) as t:
-    metric_names = ["discriminator_network_loss_actual"] + models[
-        "srgan_model"
-    ].metrics_names
-    columns = metric_names + [f"val_{metric_name}" for metric_name in metric_names]
-    dataframe = pd.DataFrame(index=np.arange(0, epochs), columns=columns)
-    for i in t:
-        ## Part 1 - Train Discriminator
-        _, d_train_loss = train_discriminator(
-            models=models,
-            generator_inputs=[X_train, W1_train, W2_train],
-            groundtruth_images=Y_train,
-        )
-        d_dev_loss = models["discriminator_model"].evaluate(
-            x=models["generator_model"].predict(
-                x=[X_dev, W1_dev, W2_dev], batch_size=32
-            ),
-            y=np.zeros(shape=len(X_dev)),
-        )
 
-        ## Part 2 - Train Generator
-        _, g_train_metrics = train_generator(
-            models=models,
-            generator_inputs=[X_train, W1_train, W2_train],
-            groundtruth_images=Y_train,
+metric_names = [
+    "discriminator_loss",
+    "discriminator_accu",
+    "generator_loss",
+    "generator_psnr",
+]
+columns = metric_names + [f"val_{metric_name}" for metric_name in metric_names]
+dataframe = pd.DataFrame(index=np.arange(epochs), columns=columns)
+progressbar = tqdm.tqdm(unit="epoch", total=epochs, position=0)
+
+train_iter.reset()
+dev_iter.reset()
+
+for i in range(epochs):
+    metrics_dict = {mn: [] for mn in columns}  # reset metrics dictionary
+
+    ## Part 1 - Training on training dataset
+    while i == train_iter.epoch:  # while we are in epoch i, run minibatch training
+        train_batch = train_iter.next()
+        train_arrays = chainer.dataset.concat_examples(batch=train_batch)
+        ## 1.1 - Train Discriminator
+        d_train_loss, d_train_accu = train_eval_discriminator(
+            input_arrays=train_arrays,
+            g_model=generator_model,
+            d_model=discriminator_model,
+            d_optimizer=discriminator_optimizer,
         )
-        g_dev_metrics = models["srgan_model"].evaluate(
-            x=[X_dev, W1_dev, W2_dev],
-            y={
-                "generator_network": Y_dev,
-                "discriminator_network": np.ones(shape=len(X_dev)),
-            },
+        metrics_dict["discriminator_loss"].append(d_train_loss)
+        metrics_dict["discriminator_accu"].append(d_train_accu)
+
+        ## 1.2 - Train Generator
+        g_train_loss, g_train_psnr = train_eval_generator(
+            input_arrays=train_arrays,
+            g_model=generator_model,
+            d_model=discriminator_model,
+            g_optimizer=generator_optimizer,
         )
-
-        ## Plot loss and metric information using pandas and livelossplot
-        dataframe.loc[i] = (
-            [d_train_loss] + g_train_metrics + [d_dev_loss] + g_dev_metrics
+        metrics_dict["generator_loss"].append(g_train_loss)
+        metrics_dict["generator_psnr"].append(g_train_psnr)
+
+    ## Part 2 - Evaluation on development dataset
+    while i == dev_iter.epoch:  # while we are in epoch i, evaluate on each minibatch
+        dev_batch = dev_iter.next()
+        dev_arrays = chainer.dataset.concat_examples(batch=dev_batch)
+        ## 2.1 - Evaluate Discriminator
+        d_train_loss, d_train_accu = train_eval_discriminator(
+            input_arrays=dev_arrays,
+            g_model=generator_model,
+            d_model=discriminator_model,
+            train=False,
         )
-        livelossplot.draw_plot(
-            logs=dataframe.to_dict(orient="records"),
-            metrics=metric_names,
-            max_cols=3,
-            figsize=(16, 9),
-            max_epoch=epochs,
+        metrics_dict["val_discriminator_loss"].append(d_train_loss)
+        metrics_dict["val_discriminator_accu"].append(d_train_accu)
+
+        ## 2.2 - Evaluate Generator
+        g_dev_loss, g_dev_psnr = train_eval_generator(
+            input_arrays=dev_arrays,
+            g_model=generator_model,
+            d_model=discriminator_model,
+            train=False,
         )
-        t.set_postfix(ordered_dict=dataframe.loc[i].to_dict())
-        experiment.log_metrics(dic=dataframe.loc[i].to_dict(), step=i)
+        metrics_dict["val_generator_loss"].append(g_dev_loss)
+        metrics_dict["val_generator_psnr"].append(g_dev_psnr)
+
+    ## Part 3 - Plot loss and metric information using livelossplot
+    dataframe.loc[i] = [np.mean(metrics_dict[metric]) for metric in dataframe.keys()]
+    livelossplot.draw_plot(
+        logs=dataframe.to_dict(orient="records"),
+        metrics=metric_names,
+        max_cols=4,
+        figsize=(21, 9),
+        max_epoch=epochs,
+    )
+    progressbar.set_postfix(ordered_dict=dataframe.loc[i].to_dict())
+    experiment.log_metrics(dic=dataframe.loc[i].to_dict(), step=i)
+    progressbar.update(n=1)
 
 # %%
-model = models["generator_model"]
+model = generator_model
 
 # %%
 os.makedirs(name="model/weights", exist_ok=True)
-# generator model's parameter weights and architecture
-model.save(filepath="model/weights/srgan_generator_model.hdf5")
-# just the model weights
-model.save_weights(filepath="model/weights/srgan_generator_model_weights.hdf5")
-# just the model architecture
-with open("model/weights/srgan_generator_model_architecture.json", "w") as json_file:
-    json_file.write(model.to_json(indent=2))
+# Save generator model's parameter weights in Numpy Zipped format
+chainer.serializers.save_npz(
+    file="model/weights/srgan_generator_model_weights.npz", obj=model
+)
+# Save generator model's architecture in ONNX format
+dummy_inputs = {
+    "x": np.random.rand(32, 1, 10, 10).astype("float32"),
+    "w1": np.random.rand(32, 1, 100, 100).astype("float32"),
+    "w2": np.random.rand(32, 1, 20, 20).astype("float32"),
+}
+_ = onnx_chainer.export(
+    model=model,
+    args={"inputs": dummy_inputs},
+    filename="model/weights/srgan_generator_model_architecture.onnx",
+    export_params=False,
+    save_text=True,
+)
 
 # Upload model weights file to Comet.ML and finish Comet.ML experiment
 experiment.log_asset(
-    file_path="model/weights/srgan_generator_model_weights.hdf5",
-    file_name="srgan_generator_model_weights",
+    file_path="model/weights/srgan_generator_model_weights.npz",
+    file_name="srgan_generator_model_weights.npz",
 )
 
 # %% [markdown]
-# ## 4. Evaluate model
+# # 4. Evaluate model
 
 # %% [markdown]
-# ### Evaluation on independent test set
+# ## Evaluation on independent test set
 
 # %%
 def get_deepbedmap_test_result(test_filepath: str = "highres/2007tx"):
@@ -723,8 +1057,8 @@ def get_deepbedmap_test_result(test_filepath: str = "highres/2007tx"):
     )
 
     # Run input datasets through trained neural network model
-    model = deepbedmap.load_trained_model(model_inputs=(X_tile, W1_tile, W2_tile))
-    Y_hat = model.predict(x=[X_tile, W1_tile, W2_tile], verbose=1)
+    model = deepbedmap.load_trained_model()
+    Y_hat = model.forward(inputs={"x": X_tile, "w1": W1_tile, "w2": W2_tile}).array
 
     # Save infered deepbedmap to grid file(s)
     deepbedmap.save_array_to_grid(
diff --git a/test_ipynb.ipynb b/test_ipynb.ipynb
index 88d2698..48e441e 100644
--- a/test_ipynb.ipynb
+++ b/test_ipynb.ipynb
@@ -252,137 +252,128 @@
    "execution_count": 3,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Trying:\n",
-      "    metrics = {\"generator_network\": 'mse', \"discriminator_network\": 'accuracy'}\n",
+      "    discriminator_model = DiscriminatorModel()\n",
       "Expecting nothing\n",
       "ok\n",
       "Trying:\n",
-      "    models = compile_srgan_model(\n",
-      "        g_network=generator_network(),\n",
-      "        d_network=discriminator_network(),\n",
-      "        metrics=metrics,\n",
+      "    y_pred = discriminator_model.forward(\n",
+      "        inputs={\n",
+      "            \"x\": np.random.rand(2, 1, 32, 32).astype(\"float32\"),\n",
+      "        }\n",
       "    )\n",
       "Expecting nothing\n",
       "ok\n",
       "Trying:\n",
-      "    models['discriminator_model'].trainable\n",
+      "    y_pred.shape\n",
       "Expecting:\n",
-      "    True\n",
+      "    (2, 1)\n",
       "ok\n",
       "Trying:\n",
-      "    models['srgan_model'].get_layer(name='generator_network').trainable\n",
+      "    discriminator_model.count_params()\n",
       "Expecting:\n",
-      "    True\n",
+      "    6824193\n",
       "ok\n",
       "Trying:\n",
-      "    models['srgan_model'].get_layer(name='discriminator_network').trainable\n",
-      "Expecting:\n",
-      "    False\n",
-      "ok\n",
-      "Trying:\n",
-      "    models['srgan_model'].count_params()\n",
-      "Expecting:\n",
-      "    8432962\n",
+      "    generator_model = GeneratorModel(\n",
+      "        inblock_class=DeepbedmapInputBlock,\n",
+      "        resblock_class=ResidualBlock,\n",
+      "        num_residual_blocks=16,\n",
+      "    )\n",
+      "Expecting nothing\n",
       "ok\n",
       "Trying:\n",
-      "    discriminator_network().input_shape\n",
-      "Expecting:\n",
-      "    (None, 32, 32, 1)\n",
+      "    y_pred = generator_model.forward(\n",
+      "        inputs={\n",
+      "            \"x\": np.random.rand(1, 1, 10, 10).astype(\"float32\"),\n",
+      "            \"w1\": np.random.rand(1, 1, 100, 100).astype(\"float32\"),\n",
+      "            \"w2\": np.random.rand(1, 1, 20, 20).astype(\"float32\"),\n",
+      "        }\n",
+      "    )\n",
+      "Expecting nothing\n",
       "ok\n",
       "Trying:\n",
-      "    discriminator_network().output_shape\n",
+      "    y_pred.shape\n",
       "Expecting:\n",
-      "    (None, 1)\n",
+      "    (1, 1, 32, 32)\n",
       "ok\n",
       "Trying:\n",
-      "    discriminator_network().count_params()\n",
+      "    generator_model.count_params()\n",
       "Expecting:\n",
-      "    6828033\n",
+      "    1604929\n",
       "ok\n",
       "Trying:\n",
-      "    generator_network().input_shape\n",
+      "    calculate_discriminator_loss(\n",
+      "        y_pred=chainer.variable.Variable(data=np.array([[0.5], [1.5], [-0.5]])),\n",
+      "        y_true=np.array([[0], [1], [0]]),\n",
+      "    )\n",
       "Expecting:\n",
-      "    [(None, 10, 10, 1), (None, 100, 100, 1), (None, 20, 20, 1)]\n",
+      "    variable(0.54985575)\n",
       "ok\n",
       "Trying:\n",
-      "    generator_network().output_shape\n",
+      "    calculate_generator_loss(\n",
+      "        y_pred=chainer.variable.Variable(data=np.ones(shape=(2, 1, 3, 3))),\n",
+      "        y_true=np.full(shape=(2, 1, 3, 3), fill_value=10.0),\n",
+      "        pred_labels=np.zeros(shape=(2, 1, 3, 3)),\n",
+      "        true_labels=np.ones(shape=(2, 1, 3, 3)).astype(np.int32),\n",
+      "    )\n",
       "Expecting:\n",
-      "    (None, 32, 32, 1)\n",
+      "    variable(9.69314718)\n",
       "ok\n",
       "Trying:\n",
-      "    generator_network().count_params()\n",
-      "Expecting:\n",
-      "    1604929\n",
-      "ok\n",
-      "Trying:\n",
-      "    y_true, y_pred = np.ones(shape=(3, 3)), np.full(shape=(3, 3), fill_value=2)\n",
-      "Expecting nothing\n",
-      "ok\n",
-      "Trying:\n",
-      "    K.eval(psnr(y_true=y_true, y_pred=y_pred))\n",
+      "    psnr(\n",
+      "        y_true=np.ones(shape=(2, 1, 3, 3)),\n",
+      "        y_pred=np.full(shape=(2, 1, 3, 3), fill_value=2),\n",
+      "    )\n",
       "Expecting:\n",
-      "    array([221.80709678, 221.80709678, 221.80709678])\n",
+      "    192.65919722494797\n",
       "ok\n",
       "Trying:\n",
-      "    dataset = np.ones(shape=(100, 4, 4, 1))\n",
+      "    train_arrays = {\n",
+      "        \"X\": np.random.RandomState(seed=42).rand(2, 1, 10, 10).astype(np.float32),\n",
+      "        \"W1\": np.random.RandomState(seed=42).rand(2, 1, 100, 100).astype(np.float32),\n",
+      "        \"W2\": np.random.RandomState(seed=42).rand(2, 1, 20, 20).astype(np.float32),\n",
+      "        \"Y\": np.random.RandomState(seed=42).rand(2, 1, 32, 32).astype(np.float32),\n",
+      "    }\n",
       "Expecting nothing\n",
       "ok\n",
       "Trying:\n",
-      "    train, dev = train_dev_split(dataset=dataset, test_size=0.05, random_state=42)\n",
-      "Expecting nothing\n",
-      "ok\n",
-      "Trying:\n",
-      "    train.shape\n",
-      "Expecting:\n",
-      "    (95, 4, 4, 1)\n",
-      "ok\n",
-      "Trying:\n",
-      "    dev.shape\n",
-      "Expecting:\n",
-      "    (5, 4, 4, 1)\n",
-      "ok\n",
-      "Trying:\n",
-      "    generator_inputs = [\n",
-      "        np.random.RandomState(seed=42).rand(32, s, s, 1) for s in [10, 100, 20]\n",
-      "    ]\n",
+      "    discriminator_model = DiscriminatorModel()\n",
       "Expecting nothing\n",
       "ok\n",
       "Trying:\n",
-      "    groundtruth_images = np.random.RandomState(seed=42).rand(32,32,32,1)\n",
+      "    discriminator_optimizer = chainer.optimizers.Adam(alpha=0.001, eps=1e-7).setup(\n",
+      "        link=discriminator_model\n",
+      "    )\n",
       "Expecting nothing\n",
       "ok\n",
       "Trying:\n",
-      "    models = compile_srgan_model(\n",
-      "        g_network=generator_network(), d_network=discriminator_network()\n",
+      "    generator_model = GeneratorModel(\n",
+      "        inblock_class=DeepbedmapInputBlock,\n",
+      "        resblock_class=ResidualBlock,\n",
+      "        num_residual_blocks=1,\n",
       "    )\n",
       "Expecting nothing\n",
       "ok\n",
       "Trying:\n",
-      "    d_weight0 = K.eval(models['discriminator_model'].weights[0][0,0,0,0])\n",
+      "    d_weight0 = [d for d in discriminator_model.params()][-1][0].array\n",
       "Expecting nothing\n",
       "ok\n",
       "Trying:\n",
-      "    _, _ = train_discriminator(\n",
-      "        models=models,\n",
-      "        generator_inputs=generator_inputs,\n",
-      "        groundtruth_images=groundtruth_images,\n",
-      "        verbose=0,\n",
+      "    d_train_loss, d_train_accu = train_eval_discriminator(\n",
+      "        input_arrays=train_arrays,\n",
+      "        g_model=generator_model,\n",
+      "        d_model=discriminator_model,\n",
+      "        d_optimizer=discriminator_optimizer,\n",
       "    )\n",
       "Expecting nothing\n",
       "ok\n",
       "Trying:\n",
-      "    d_weight1 = K.eval(models['discriminator_model'].weights[0][0,0,0,0])\n",
+      "    d_weight1 = [d for d in discriminator_model.params()][-1][0].array\n",
       "Expecting nothing\n",
       "ok\n",
       "Trying:\n",
@@ -391,36 +382,47 @@
       "    True\n",
       "ok\n",
       "Trying:\n",
-      "    generator_inputs = [\n",
-      "        np.random.RandomState(seed=42).rand(32, s, s, 1) for s in [10, 100, 20]\n",
-      "    ]\n",
+      "    train_arrays = {\n",
+      "        \"X\": np.random.RandomState(seed=42).rand(2, 1, 10, 10).astype(np.float32),\n",
+      "        \"W1\": np.random.RandomState(seed=42).rand(2, 1, 100, 100).astype(np.float32),\n",
+      "        \"W2\": np.random.RandomState(seed=42).rand(2, 1, 20, 20).astype(np.float32),\n",
+      "        \"Y\": np.random.RandomState(seed=42).rand(2, 1, 32, 32).astype(np.float32),\n",
+      "    }\n",
       "Expecting nothing\n",
       "ok\n",
       "Trying:\n",
-      "    groundtruth_images = np.random.RandomState(seed=42).rand(32,32,32,1)\n",
+      "    generator_model = GeneratorModel(\n",
+      "        inblock_class=DeepbedmapInputBlock,\n",
+      "        resblock_class=ResidualBlock,\n",
+      "        num_residual_blocks=1,\n",
+      "    )\n",
       "Expecting nothing\n",
       "ok\n",
       "Trying:\n",
-      "    models = compile_srgan_model(\n",
-      "        g_network=generator_network(), d_network=discriminator_network()\n",
+      "    generator_optimizer = chainer.optimizers.Adam(alpha=0.001, eps=1e-7).setup(\n",
+      "        link=generator_model\n",
       "    )\n",
       "Expecting nothing\n",
       "ok\n",
       "Trying:\n",
-      "    g_weight0 = K.eval(models['generator_model'].weights[0][0,0,0,0])\n",
+      "    discriminator_model = DiscriminatorModel()\n",
       "Expecting nothing\n",
       "ok\n",
       "Trying:\n",
-      "    _, _ = train_generator(\n",
-      "        models=models,\n",
-      "        generator_inputs=generator_inputs,\n",
-      "        groundtruth_images=groundtruth_images,\n",
-      "        verbose=0,\n",
+      "    g_weight0 = [g for g in generator_model.params()][0][0, 0, 0, 0].array\n",
+      "Expecting nothing\n",
+      "ok\n",
+      "Trying:\n",
+      "    _ = train_eval_generator(\n",
+      "        input_arrays=train_arrays,\n",
+      "        g_model=generator_model,\n",
+      "        d_model=discriminator_model,\n",
+      "        g_optimizer=generator_optimizer,\n",
       "    )\n",
       "Expecting nothing\n",
       "ok\n",
       "Trying:\n",
-      "    g_weight1 = K.eval(models['generator_model'].weights[0][0,0,0,0])\n",
+      "    g_weight1 = [g for g in generator_model.params()][0][0, 0, 0, 0].array\n",
       "Expecting nothing\n",
       "ok\n",
       "Trying:\n",
@@ -428,19 +430,29 @@
       "Expecting:\n",
       "    True\n",
       "ok\n",
-      "2 items had no tests:\n",
+      "12 items had no tests:\n",
       "    srgan_train\n",
+      "    srgan_train.DeepbedmapInputBlock\n",
+      "    srgan_train.DeepbedmapInputBlock.__init__\n",
+      "    srgan_train.DeepbedmapInputBlock.forward\n",
+      "    srgan_train.DiscriminatorModel.__init__\n",
+      "    srgan_train.DiscriminatorModel.forward\n",
+      "    srgan_train.GeneratorModel.__init__\n",
+      "    srgan_train.GeneratorModel.forward\n",
+      "    srgan_train.ResidualBlock\n",
+      "    srgan_train.ResidualBlock.__init__\n",
+      "    srgan_train.ResidualBlock.forward\n",
       "    srgan_train.get_deepbedmap_test_result\n",
       "7 items passed all tests:\n",
-      "   6 tests in srgan_train.compile_srgan_model\n",
-      "   3 tests in srgan_train.discriminator_network\n",
-      "   3 tests in srgan_train.generator_network\n",
-      "   2 tests in srgan_train.psnr\n",
-      "   4 tests in srgan_train.train_dev_split\n",
-      "   7 tests in srgan_train.train_discriminator\n",
-      "   7 tests in srgan_train.train_generator\n",
-      "32 tests in 9 items.\n",
-      "32 passed and 0 failed.\n",
+      "   4 tests in srgan_train.DiscriminatorModel\n",
+      "   4 tests in srgan_train.GeneratorModel\n",
+      "   1 tests in srgan_train.calculate_discriminator_loss\n",
+      "   1 tests in srgan_train.calculate_generator_loss\n",
+      "   1 tests in srgan_train.psnr\n",
+      "   8 tests in srgan_train.train_eval_discriminator\n",
+      "   8 tests in srgan_train.train_eval_generator\n",
+      "27 tests in 19 items.\n",
+      "27 passed and 0 failed.\n",
       "Test passed.\n"
      ]
     }
@@ -521,7 +533,7 @@
       "    Given some view of Antarctica -1593714.328,-164173.7848,-1575464.328,-97923.7848        # features/steps/test_deepbedmap.py:6\n",
       "    When we gather low and high resolution images related to that view                      # features/steps/test_deepbedmap.py:14\n",
       "    And pass those images into our trained neural network model                             # features/steps/test_deepbedmap.py:30\n",
-      "    Then a four times upsampled super resolution bed elevation map is returned              # features/steps/test_deepbedmap.py:40\n",
+      "    Then a four times upsampled super resolution bed elevation map is returned              # features/steps/test_deepbedmap.py:38\n",
       "\n"
      ]
     }