fix multiple docstring inconsistencies (#2167)

python/dgl/dataloading/dataloader.py

@@ -299,7 +299,7 @@ class NodeCollator(Collator):
     a homogeneous graph where each node takes messages from all neighbors (assume
     the backend is PyTorch):
 
-    >>> sampler = dgl.dataloading.NeighborSampler([None, None, None])
+    >>> sampler = dgl.dataloading.MultiLayerNeighborSampler([15, 10, 5])
     >>> collator = dgl.dataloading.NodeCollator(g, train_nid, sampler)
     >>> dataloader = torch.utils.data.DataLoader(
     ...     collator.dataset, collate_fn=collator.collate,
@@ -460,9 +460,9 @@ class EdgeCollator(Collator):
     computation dependencies of the incident nodes.  This is a common trick to avoid
     information leakage.
 
-    >>> sampler = dgl.dataloading.NeighborSampler([None, None, None])
+    >>> sampler = dgl.dataloading.MultiLayerNeighborSampler([15, 10, 5])
     >>> collator = dgl.dataloading.EdgeCollator(
-    ...     g, train_eid, sampler, exclude='reverse',
+    ...     g, train_eid, sampler, exclude='reverse_id',
     ...     reverse_eids=reverse_eids)
     >>> dataloader = torch.utils.data.DataLoader(
     ...     collator.dataset, collate_fn=collator.collate,
@@ -474,10 +474,10 @@ class EdgeCollator(Collator):
     homogeneous graph where each node takes messages from all neighbors (assume the
     backend is PyTorch), with 5 uniformly chosen negative samples per edge:
 
-    >>> sampler = dgl.dataloading.NeighborSampler([None, None, None])
+    >>> sampler = dgl.dataloading.MultiLayerNeighborSampler([15, 10, 5])
     >>> neg_sampler = dgl.dataloading.negative_sampler.Uniform(5)
     >>> collator = dgl.dataloading.EdgeCollator(
-    ...     g, train_eid, sampler, exclude='reverse',
+    ...     g, train_eid, sampler, exclude='reverse_id',
     ...     reverse_eids=reverse_eids, negative_sampler=neg_sampler,
     >>> dataloader = torch.utils.data.DataLoader(
     ...     collator.dataset, collate_fn=collator.collate,
@@ -498,7 +498,7 @@ class EdgeCollator(Collator):
     To train a 3-layer GNN for edge classification on a set of edges ``train_eid`` with
     type ``click``, you can write
 
-    >>> sampler = dgl.dataloading.NeighborSampler([None, None, None])
+    >>> sampler = dgl.dataloading.MultiLayerNeighborSampler([15, 10, 5])
     >>> collator = dgl.dataloading.EdgeCollator(
     ...     g, {'click': train_eid}, sampler, exclude='reverse_types',
     ...     reverse_etypes={'click': 'clicked-by', 'clicked-by': 'click'})
@@ -511,7 +511,7 @@ class EdgeCollator(Collator):
     To train a 3-layer GNN for link prediction on a set of edges ``train_eid`` with type
     ``click``, you can write
 
-    >>> sampler = dgl.dataloading.NeighborSampler([None, None, None])
+    >>> sampler = dgl.dataloading.MultiLayerNeighborSampler([15, 10, 5])
     >>> neg_sampler = dgl.dataloading.negative_sampler.Uniform(5)
     >>> collator = dgl.dataloading.EdgeCollator(
     ...     g, train_eid, sampler, exclude='reverse_types',

python/dgl/transform.py

@@ -1632,7 +1632,7 @@ def to_block(g, dst_nodes=None, include_dst_in_src=True):
     Examples
     --------
     Converting a homogeneous graph to a block as described above:
-    >>> g = dgl.graph(([0, 1, 2], [1, 2, 3]))
+    >>> g = dgl.graph(([1, 2], [2, 3]))
     >>> block = dgl.to_block(g, torch.LongTensor([3, 2]))
 
     The output nodes would be exactly the same as the ones given: [3, 2].
@@ -1665,10 +1665,17 @@ def to_block(g, dst_nodes=None, include_dst_in_src=True):
     >>> induced_src[src], induced_dst[dst]
     (tensor([2, 1]), tensor([3, 2]))
 
+    The output nodes specified must be a superset of the nodes that have edges connecting
+    to them.  For example, the following will raise an error since the output nodes
+    does not contain node 3, which has an edge connecting to it.
+
+    >>> g = dgl.graph(([1, 2], [2, 3]))
+    >>> dgl.to_block(g, torch.LongTensor([2]))     # error
+
     Converting a heterogeneous graph to a block is similar, except that when specifying
     the output nodes, you have to give a dict:
 
-    >>> g = dgl.heterograph({('A', '_E', 'B'): ([0, 1, 2], [1, 2, 3])})
+    >>> g = dgl.heterograph({('A', '_E', 'B'): ([1, 2], [2, 3])})
 
     If you don't specify any node of type A on the output side, the node type ``A``
     in the block would have zero nodes on the output side.