simplify the logic. Reduce the number of args.

Author: mfbalin

python/dgl/graphbolt/impl/fused_csc_sampling_graph.py

@@ -797,7 +797,7 @@ def sample_neighbors(
         returning_indices_and_original_edge_ids_are_optional: bool
             Boolean indicating whether it is okay for the call to this function
             to leave the indices and the original edge ids tensors
-            uninitialized. In this case, it is the user's responsibility to 
+            uninitialized. In this case, it is the user's responsibility to
             gather them using _edge_ids_in_fused_csc_sampling_graph if either is
             missing.
         async_op: bool
@@ -1035,7 +1035,7 @@ def sample_layer_neighbors(
         returning_indices_and_original_edge_ids_are_optional: bool
             Boolean indicating whether it is okay for the call to this function
             to leave the indices and the original edge ids tensors
-            uninitialized. In this case, it is the user's responsibility to 
+            uninitialized. In this case, it is the user's responsibility to
             gather them using _edge_ids_in_fused_csc_sampling_graph if either is
             missing.
         random_seed: torch.Tensor, optional

python/dgl/graphbolt/impl/neighbor_sampler.py

@@ -272,7 +272,13 @@ def __init__(
         if (
             overlap_fetch
             and sampler.__name__ == "sample_neighbors"
-            and (graph.indices.is_pinned() or (original_edge_ids is not None and original_edge_ids.is_pinned()))
+            and (
+                graph.indices.is_pinned()
+                or (
+                    original_edge_ids is not None
+                    and original_edge_ids.is_pinned()
+                )
+            )
             and graph._gpu_graph_cache is None
         ):
             datapipe = datapipe.transform(self._sample_per_layer)