[Misc] Update comments in python code (#6551)

examples/sampling/graphbolt/link_prediction.py

@@ -83,7 +83,8 @@ def create_dataloader(args, graph, features, itemset, is_train=True):
     """Get a GraphBolt version of a dataloader for link prediction tasks. This
     function demonstrates how to utilize functional forms of datapipes in
     GraphBolt. Alternatively, you can create a datapipe using its class
-    constructor.
+    constructor. For a more detailed tutorial, please read the examples in
+    `dgl/notebooks/graphbolt/walkthrough.ipynb`.
     """
 
     ############################################################################

examples/sampling/graphbolt/node_classification.py

@@ -56,7 +56,8 @@ def create_dataloader(
     [HIGHLIGHT]
     Get a GraphBolt version of a dataloader for node classification tasks.
     This function demonstrates how to utilize functional forms of datapipes in
-    GraphBolt.
+    GraphBolt. For a more detailed tutorial, please read the examples in
+    `dgl/notebooks/graphbolt/walkthrough.ipynb`.
     Alternatively, you can create a datapipe using its class constructor.
 
     Parameters

python/dgl/graphbolt/impl/fused_csc_sampling_graph.py

@@ -772,7 +772,6 @@ def from_fused_csc(
     ...             node_type_offset=node_type_offset,
     ...             type_per_edge=type_per_edge,
     ...             edge_attributes=None, metadata=metadata)
-    None, metadata)
     >>> print(graph)
     FusedCSCSamplingGraph(csc_indptr=tensor([0, 2, 5, 7]),
                      indices=tensor([1, 3, 0, 1, 2, 0, 3]),

python/dgl/graphbolt/impl/neighbor_sampler.py

@@ -63,12 +63,11 @@ class NeighborSampler(SubgraphSampler):
     >>> node_pairs = torch.LongTensor([[0, 1], [1, 2]])
     >>> item_set = gb.ItemSet(node_pairs, names="node_pairs")
     >>> item_sampler = gb.ItemSampler(
-        ...item_set, batch_size=1,
-        ...)
+    ...     item_set, batch_size=1,)
     >>> neg_sampler = gb.UniformNegativeSampler(
-        ...item_sampler, graph, 2)
+    ...     item_sampler, graph, 2)
     >>> subgraph_sampler = gb.NeighborSampler(
-        ...neg_sampler, graph, [5, 10, 15])
+    ...     neg_sampler, graph, [5, 10, 15])
     >>> for data in subgraph_sampler:
     ...     print(data.compacted_node_pairs)
     ...     print(len(data.sampled_subgraphs))
@@ -197,14 +196,13 @@ class LayerNeighborSampler(NeighborSampler):
     >>> node_pairs = torch.LongTensor([[0, 1], [1, 2]])
     >>> item_set = gb.ItemSet(node_pairs, names="node_pairs")
     >>> item_sampler = gb.ItemSampler(
-        ...item_set, batch_size=1,
-        ...)
+    ...     item_set, batch_size=1,)
     >>> neg_sampler = gb.UniformNegativeSampler(
-        ...item_sampler, 2, data_format, graph)
+    ...     item_sampler, 2, data_format, graph)
     >>> fanouts = [torch.LongTensor([5]), torch.LongTensor([10]),
-        ...torch.LongTensor([15])]
+    ...     torch.LongTensor([15])]
     >>> subgraph_sampler = gb.LayerNeighborSampler(
-        ...neg_sampler, graph, fanouts)
+    ...     neg_sampler, graph, fanouts)
     >>> for data in subgraph_sampler:
     ...      print(data.compacted_node_pairs)
     ...      print(len(data.sampled_subgraphs))

python/dgl/graphbolt/impl/uniform_negative_sampler.py

@@ -36,14 +36,12 @@ class UniformNegativeSampler(NegativeSampler):
     >>> node_pairs = (torch.tensor([0, 1]), torch.tensor([1, 2]))
     >>> item_set = gb.ItemSet(node_pairs, names="node_pairs")
     >>> item_sampler = gb.ItemSampler(
-        ...item_set, batch_size=1,
-        ...)
+    ...     item_set, batch_size=1,)
     >>> neg_sampler = gb.UniformNegativeSampler(
-        ...item_sampler, graph, 2)
+    ...     item_sampler, graph, 2)
     >>> for minibatch in neg_sampler:
     ...       print(minibatch.negative_srcs)
     ...       print(minibatch.negative_dsts)
-        ...
     (tensor([0, 0, 0]), tensor([1, 1, 2]), tensor([1, 0, 0]))
     (tensor([1, 1, 1]), tensor([2, 1, 2]), tensor([1, 0, 0]))
     """