autoformat (#5322)

Co-authored-by: Ubuntu <ubuntu@ip-172-31-28-63.ap-northeast-1.compute.internal>

benchmarks/benchmarks/api/bench_add_self_loop.py

 import time
 
-import numpy as np
-import torch
-
 import dgl
 import dgl.function as fn
 
+import numpy as np
+import torch
+
 from .. import utils
 
 

benchmarks/benchmarks/api/bench_batch.py

 import time
 
-import torch
-
 import dgl
 
+import torch
+
 from .. import utils
 
 

benchmarks/benchmarks/api/bench_builtin_apply_edges.py

 import time
 
-import numpy as np
-import torch
-
 import dgl
 import dgl.function as fn
 
+import numpy as np
+import torch
+
 from .. import utils
 
 

benchmarks/benchmarks/api/bench_builtin_apply_edges_hetero.py

 import time
 
-import numpy as np
-import torch
-
 import dgl
 import dgl.function as fn
 
+import numpy as np
+import torch
+
 from .. import utils
 
 

benchmarks/benchmarks/api/bench_builtin_multi_update_all.py

 import time
+
 import dgl
-import torch
-import numpy as np
 import dgl.function as fn
+import numpy as np
+import torch
 
 from .. import utils
 
 
-@utils.benchmark('time', timeout=600)
-@utils.parametrize('feat_size', [32, 128, 512])
-@utils.parametrize('num_relations', [5, 50, 500])
-@utils.parametrize('multi_reduce_type', ["sum", "stack"])
+@utils.benchmark("time", timeout=600)
+@utils.parametrize("feat_size", [32, 128, 512])
+@utils.parametrize("num_relations", [5, 50, 500])
+@utils.parametrize("multi_reduce_type", ["sum", "stack"])
 def track_time(feat_size, num_relations, multi_reduce_type):
     device = utils.get_bench_device()
     dd = {}
-    candidate_edges = [dgl.data.CoraGraphDataset(verbose=False)[0].edges(), dgl.data.PubmedGraphDataset(verbose=False)[
-        0].edges(), dgl.data.CiteseerGraphDataset(verbose=False)[0].edges()]
+    candidate_edges = [
+        dgl.data.CoraGraphDataset(verbose=False)[0].edges(),
+        dgl.data.PubmedGraphDataset(verbose=False)[0].edges(),
+        dgl.data.CiteseerGraphDataset(verbose=False)[0].edges(),
+    ]
     for i in range(num_relations):
-        dd[('n1', 'e_{}'.format(i), 'n2')] = candidate_edges[i %
-                                                             len(candidate_edges)]
+        dd[("n1", "e_{}".format(i), "n2")] = candidate_edges[
+            i % len(candidate_edges)
+        ]
     graph = dgl.heterograph(dd)
 
     graph = graph.to(device)
-    graph.nodes['n1'].data['h'] = torch.randn(
-        (graph.num_nodes('n1'), feat_size), device=device)
-    graph.nodes['n2'].data['h'] = torch.randn(
-        (graph.num_nodes('n2'), feat_size), device=device)
+    graph.nodes["n1"].data["h"] = torch.randn(
+        (graph.num_nodes("n1"), feat_size), device=device
+    )
+    graph.nodes["n2"].data["h"] = torch.randn(
+        (graph.num_nodes("n2"), feat_size), device=device
+    )
 
     # dry run
     update_dict = {}
     for i in range(num_relations):
-        update_dict['e_{}'.format(i)] = (
-            fn.copy_u('h', 'm'), fn.sum('m', 'h'))
-    graph.multi_update_all(
-        update_dict,
-        multi_reduce_type)
+        update_dict["e_{}".format(i)] = (fn.copy_u("h", "m"), fn.sum("m", "h"))
+    graph.multi_update_all(update_dict, multi_reduce_type)
 
     # timing
-    
+
     with utils.Timer() as t:
         for i in range(3):
-            graph.multi_update_all(
-                update_dict,
-                multi_reduce_type)
+            graph.multi_update_all(update_dict, multi_reduce_type)
 
     return t.elapsed_secs / 3

benchmarks/benchmarks/api/bench_builtin_update_all_coo.py

 import time
 
-import numpy as np
-import torch
-
 import dgl
 import dgl.function as fn
 
+import numpy as np
+import torch
+
 from .. import utils
 
 

benchmarks/benchmarks/api/bench_builtin_update_all_csc.py

 import time
 
-import numpy as np
-import torch
-
 import dgl
 import dgl.function as fn
 
+import numpy as np
+import torch
+
 from .. import utils
 
 

benchmarks/benchmarks/api/bench_edge_ids.py

 import time
 
+import dgl
+
 import numpy as np
 import torch
 
-import dgl
-
 from .. import utils
 
 

benchmarks/benchmarks/api/bench_edge_subgraph.py

 import time
 
-import numpy as np
-import torch
-
 import dgl
 import dgl.function as fn
 
+import numpy as np
+import torch
+
 from .. import utils
 
 

benchmarks/benchmarks/api/bench_find_edges.py

 import time
 
+import dgl
+
 import numpy as np
 import torch
 
-import dgl
-
 from .. import utils
 
 

benchmarks/benchmarks/api/bench_format_conversion.py

 import time
 
+import dgl
+
 import numpy as np
 import torch
 
-import dgl
-
 from .. import utils
 
 

benchmarks/benchmarks/api/bench_heterograph_construction.py

 import time
 
-import numpy as np
-import torch
-
 import dgl
 import dgl.function as fn
 
+import numpy as np
+import torch
+
 from .. import utils
 
 

benchmarks/benchmarks/api/bench_homograph_edge_construction.py

 import time
 
-import numpy as np
-import torch
-
 import dgl
 import dgl.function as fn
 
+import numpy as np
+import torch
+
 from .. import utils
 
 

benchmarks/benchmarks/api/bench_homograph_scipy_construction.py

 import time
 
-import numpy as np
-import torch
-
 import dgl
 import dgl.function as fn
 
+import numpy as np
+import torch
+
 from .. import utils
 
 

benchmarks/benchmarks/api/bench_in_degrees.py

 import time
 
+import dgl
+
 import numpy as np
 import torch
 
-import dgl
-
 from .. import utils
 
 

benchmarks/benchmarks/api/bench_in_edges.py

 import time
 
+import dgl
+
 import numpy as np
 import torch
 
-import dgl
-
 from .. import utils
 
 

benchmarks/benchmarks/api/bench_in_subgraph.py

 import time
 
-import numpy as np
-import torch
-
 import dgl
 import dgl.function as fn
 
+import numpy as np
+import torch
+
 from .. import utils
 
 

benchmarks/benchmarks/api/bench_khop.py

 import time
 
+import dgl
+
 import numpy as np
 import torch
 
-import dgl
-
 from .. import utils
 
 

benchmarks/benchmarks/api/bench_knn_graph.py

 import time
 
+import dgl
+
 import numpy as np
 import torch
 
-import dgl
-
 from .. import utils
 
 

benchmarks/benchmarks/api/bench_metis_partition.py

 import time
 
+import dgl
+
 import numpy as np
 import torch
 
-import dgl
-
 from .. import utils