Fix second linter errors

python/dgl/dataloading/capped_neighbor_sampler.py

@@ -62,14 +62,14 @@ def __init__(
         self.prefetch_edge_feats = prefetch_edge_feats
         self.output_device = output_device
 
-    def sample(self, g, seed_nodes, exclude_eids=None):
+    def sample(self, g, indices, exclude_eids=None):
         """Sampling function.
 
         Parameters
         ----------
         g : DGLGraph
             The graph to sample from.
-        seed_nodes : Tensor or dict[str, Tensor]
+        indices : Tensor or dict[str, Tensor]
             Nodes which induce the subgraph.
         exclude_eids : Tensor or dict[etype, Tensor], optional
             The edges to exclude from the sampled subgraph.
@@ -85,15 +85,15 @@ def sample(self, g, seed_nodes, exclude_eids=None):
         """
 
         # Define empty dictionary to store reached nodes.
-        output_nodes = seed_nodes
-        all_reached_nodes = [seed_nodes]
+        output_nodes = indices
+        all_reached_nodes = [indices]
 
         # Iterate over fanout.
         for fanout in reversed(self.fanouts):
 
             # Sample frontier.
             frontier = g.sample_neighbors(
-                seed_nodes,
+                indices,
                 fanout,
                 output_device=self.output_device,
                 replace=self.replace,
@@ -104,7 +104,7 @@ def sample(self, g, seed_nodes, exclude_eids=None):
             # Get reached nodes.
             curr_reached = defaultdict(list)
             for c_etype in frontier.canonical_etypes:
-                (src_type, rel_type, dst_type) = c_etype
+                (src_type, _, _) = c_etype
                 src, _ = frontier.edges(etype=c_etype)
                 curr_reached[src_type].append(src)
 
@@ -153,18 +153,18 @@ def sample(self, g, seed_nodes, exclude_eids=None):
 
             # Downsample nodes.
             curr_reached_k = {}
-            for node_type, node_IDs in curr_reached.items():
+            for node_type, node_ids in curr_reached.items():
 
                 # Get number of total nodes and number to sample.
-                num_nodes = node_IDs.shape[0]
+                num_nodes = node_ids.shape[0]
                 n_to_sample = min(num_nodes, n_per_type[node_type])
 
                 # Downsample nodes of current type.
                 random_indices = torch.randperm(num_nodes)[:n_to_sample]
-                curr_reached_k[node_type] = node_IDs[random_indices]
+                curr_reached_k[node_type] = node_ids[random_indices]
 
             # Update seed nodes.
-            seed_nodes = curr_reached_k
+            indices = curr_reached_k
             all_reached_nodes.append(curr_reached_k)
 
         # Merge all reached nodes before sending to `DGLGraph.subgraph`.
@@ -185,4 +185,4 @@ def sample(self, g, seed_nodes, exclude_eids=None):
         set_node_lazy_features(subg, self.prefetch_node_feats)
         set_edge_lazy_features(subg, self.prefetch_edge_feats)
 
-        return seed_nodes, output_nodes, subg
+        return indices, output_nodes, subg