[GraphBolt] Update docstring of sampled subgraph. (#6998)

Co-authored-by: Ubuntu <ubuntu@ip-172-31-0-133.us-west-2.compute.internal>
Co-authored-by: Hongzhi (Steve), Chen <chenhongzhi.nkcs@gmail.com>

python/dgl/graphbolt/sampled_subgraph.py

@@ -30,16 +30,18 @@ class SampledSubgraph:
         self,
     ) -> Union[CSCFormatBase, Dict[str, CSCFormatBase],]:
         """Returns the node pairs representing edges in csc format.
-        - If `sampled_csc` is a CSCFormatBase: It should be in the csc format.
-          `indptr` stores the index in the data array where each column
-          starts. `indices` stores the row indices of the non-zero elements.
-        - If `sampled_csc` is a dictionary: The keys should be edge type and
-          the values should be corresponding node pairs. The ids inside is
-          heterogeneous ids.
+          - If `sampled_csc` is a CSCFormatBase: It should be in the csc
+            format. `indptr` stores the index in the data array where each
+            column starts. `indices` stores the row indices of the non-zero
+            elements.
+          - If `sampled_csc` is a dictionary: The keys should be edge type and
+            the values should be corresponding node pairs. The ids inside is
+            heterogeneous ids.
 
         Examples
         --------
         1. Homogeneous graph.
+
         >>> import dgl.graphbolt as gb
         >>> import torch
         >>> sampled_csc = gb.CSCFormatBase(
@@ -51,6 +53,7 @@ class SampledSubgraph:
         )
 
         2. Heterogeneous graph.
+
         sampled_csc = {"A:relation:B": gb.CSCFormatBase(
         ...     indptr=torch.tensor([0, 1, 2, 3]),
         ...     indices=torch.tensor([0, 1, 2]))}
@@ -69,11 +72,11 @@ class SampledSubgraph:
         Column's reverse node ids in the original graph. A graph structure
         can be treated as a coordinated row and column pair, and this is
         the mapped ids of the column.
-        - If `original_column_node_ids` is a tensor: It represents the original
-          node ids.
-        - If `original_column_node_ids` is a dictionary: The keys should be
-          node type and the values should be corresponding original
-          heterogeneous node ids.
+          - If `original_column_node_ids` is a tensor: It represents the
+            original node ids.
+          - If `original_column_node_ids` is a dictionary: The keys should be
+            node type and the values should be corresponding original
+            heterogeneous node ids.
         If present, it means column IDs are compacted, and `sampled_csc`
         column IDs match these compacted ones.
         """
@@ -87,11 +90,11 @@ class SampledSubgraph:
         Row's reverse node ids in the original graph. A graph structure
         can be treated as a coordinated row and column pair, and this is
         the mapped ids of the row.
-        - If `original_row_node_ids` is a tensor: It represents the original
-          node ids.
-        - If `original_row_node_ids` is a dictionary: The keys should be node
-          type and the values should be corresponding original heterogeneous
-          node ids.
+          - If `original_row_node_ids` is a tensor: It represents the original
+            node ids.
+          - If `original_row_node_ids` is a dictionary: The keys should be node
+            type and the values should be corresponding original heterogeneous
+            node ids.
         If present, it means row IDs are compacted, and `sampled_csc`
         row IDs match these compacted ones."""
         return None
@@ -101,11 +104,11 @@ class SampledSubgraph:
         """Returns corresponding reverse edge ids the original graph.
         Reverse edge ids in the original graph. This is useful when edge
         features are needed.
-        - If `original_edge_ids` is a tensor: It represents the original edge
-          ids.
-        - If `original_edge_ids` is a dictionary: The keys should be edge type
-          and the values should be corresponding original heterogeneous edge
-          ids.
+          - If `original_edge_ids` is a tensor: It represents the original edge
+            ids.
+          - If `original_edge_ids` is a dictionary: The keys should be edge
+            type and the values should be corresponding original heterogeneous
+            edge ids.
         """
         return None
 
@@ -119,17 +122,17 @@ class SampledSubgraph:
     ):
         r"""Exclude edges from the sampled subgraph.
 
-        This function can be used with sampled subgraphs, regardless of whether they
-        have compacted row/column nodes or not. If the original subgraph has
-        compacted row or column nodes, the corresponding row or column nodes in the
-        returned subgraph will also be compacted.
+        This function can be used with sampled subgraphs, regardless of
+        whether they have compacted row/column nodes or not. If the original
+        subgraph has compacted row or column nodes, the corresponding row or
+        column nodes in the returned subgraph will also be compacted.
 
         Parameters
         ----------
         self : SampledSubgraph
             The sampled subgraph.
         edges : Union[Tuple[torch.Tensor, torch.Tensor],
-        Dict[str, Tuple[torch.Tensor, torch.Tensor]]]
+                Dict[str, Tuple[torch.Tensor, torch.Tensor]]]
             Edges to exclude. If sampled subgraph is homogeneous, then `edges`
             should be a pair of tensors representing the edges to exclude. If
             sampled subgraph is heterogeneous, then `edges` should be a