[pre-commit.ci] auto fixes from pre-commit.com hooks

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examples/multi_gpu/distributed_sampling.py

@@ -1,3 +1,4 @@
+import copy
 import os
 
 import torch
@@ -6,7 +7,7 @@
 import torch.nn.functional as F
 from torch.nn.parallel import DistributedDataParallel
 from tqdm import tqdm
-import copy
+
 from torch_geometric.datasets import Reddit
 from torch_geometric.loader import NeighborLoader
 from torch_geometric.nn import SAGEConv
@@ -64,12 +65,13 @@ def run(rank, world_size, dataset):
 
     kwargs = {'batch_size': 1024, 'num_workers': 0}
     train_loader = NeighborLoader(data, input_nodes=data.train_mask,
-                                  num_neighbors=[25, 10],
-                                  shuffle=True, drop_last=True, **kwargs)
+                                  num_neighbors=[25, 10], shuffle=True,
+                                  drop_last=True, **kwargs)
 
     if rank == 0:
         subgraph_loader = NeighborLoader(copy.copy(data), input_nodes=None,
-                                         num_neighbors=[-1], shuffle=False, **kwargs)
+                                         num_neighbors=[-1], shuffle=False,
+                                         **kwargs)
         # No need to maintain these features during evaluation:
         del subgraph_loader.data.x, subgraph_loader.data.y
         # Add global node index information.

training_benchmark.sh

@@ -1,2 +1,2 @@
 # python training_benchmark.py --datasets Reddit --models gcn
-python examples/multi_gpu/distributed_sampling.py > 
+python examples/multi_gpu/distributed_sampling.py >