diff --git a/src/operator/contrib/dgl_graph.cc b/src/operator/contrib/dgl_graph.cc
index 89ba4fc4b40d..ed7caacfdbae 100644
--- a/src/operator/contrib/dgl_graph.cc
+++ b/src/operator/contrib/dgl_graph.cc
@@ -768,7 +768,10 @@ static void CSRNeighborUniformSampleComputeExCPU(const nnvm::NodeAttrs& attrs,
 NNVM_REGISTER_OP(_contrib_dgl_csr_neighbor_uniform_sample)
 .describe(R"code(This operator samples sub-graph from a csr graph via an
 uniform probability. 
-Example::
+
+Example:
+
+   .. code:: python
 
   shape = (5, 5)
   data_np = np.array([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20], dtype=np.int64)
@@ -850,7 +853,10 @@ static void CSRNeighborNonUniformSampleComputeExCPU(const nnvm::NodeAttrs& attrs
 NNVM_REGISTER_OP(_contrib_dgl_csr_neighbor_non_uniform_sample)
 .describe(R"code(This operator samples sub-graph from a csr graph via an
 uniform probability. 
-Example::
+
+Example:
+
+   .. code:: python
 
   shape = (5, 5)
   prob = mx.nd.array([0.9, 0.8, 0.2, 0.4, 0.1], dtype=np.float32)
@@ -1379,6 +1385,8 @@ the data value of float32.
 
 Example:
 
+   .. code:: python
+
   x = [[ 1, 0, 0 ],
        [ 0, 2, 0 ],
        [ 0, 0, 3 ]]
@@ -1549,14 +1557,33 @@ NNVM_REGISTER_OP(_contrib_dgl_graph_compact)
 .describe(R"code(This operator compacts a CSR matrix generated by
 dgl_csr_neighbor_uniform_sample and dgl_csr_neighbor_non_uniform_sample.
 The CSR matrices generated by these two operators may have many empty
-rows at the end. This operator removes these empty rows and empty columns.
-Example::
-  subgs = mx.nd.contrib.csr_neighbor_uniform_sample(csr, seed, num_hops=1,
-          num_neighbor=2, max_num_vertices=5)
-  subg_v = subgs[0]
-  subg = subgs[1]
-  compacts = mx.nd.contrib.dgl_graph_compact(subg, subg_v,
-          graph_sizes=(subg_v[-1].asnumpy()[0]))
+rows at the end and many empty columns. This operator removes these
+empty rows and empty columns.
+
+Example:
+
+   .. code:: python
+
+  shape = (5, 5)
+  data_np = np.array([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20], dtype=np.int64)
+  indices_np = np.array([1,2,3,4,0,2,3,4,0,1,3,4,0,1,2,4,0,1,2,3], dtype=np.int64)
+  indptr_np = np.array([0,4,8,12,16,20], dtype=np.int64)
+  a = mx.nd.sparse.csr_matrix((data_np, indices_np, indptr_np), shape=shape)
+  seed = mx.nd.array([0,1,2,3,4], dtype=np.int64)
+  out = mx.nd.contrib.dgl_csr_neighbor_uniform_sample(a, seed, num_args=2, num_hops=1,
+          num_neighbor=2, max_num_vertices=6)
+  subg_v = out[0]
+  subg = out[1]
+  compact = mx.nd.contrib.dgl_graph_compact(subg, subg_v,
+          graph_sizes=(subg_v[-1].asnumpy()[0]), return_mapping=False)
+
+  compact.asnumpy()
+  array([[0, 0, 0, 1, 0],
+         [2, 0, 3, 0, 0],
+         [0, 4, 0, 0, 5],
+         [0, 6, 0, 0, 7],
+         [8, 9, 0, 0, 0]])
+
 )code" ADD_FILELINE)
 .set_attr_parser(ParamParser<SubgraphCompactParam>)
 .set_num_inputs([](const NodeAttrs& attrs) {