[Misc] Auto-format tests. (#5313)

* [Misc] Auto-format tests.

* more

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

tests/python/pytorch/sparse/test_sparse_matrix.py

@@ -264,7 +264,6 @@ def test_csc_to_csr(dense_dim, indptr, indices, shape):
 @pytest.mark.parametrize("col", [(0, 1, 2, 2), (1, 3, 3, 4)])
 @pytest.mark.parametrize("shape", [None, (5, 5), (5, 6)])
 def test_coo_to_csc(dense_dim, row, col, shape):
-
     val_shape = (len(row),)
     if dense_dim is not None:
         val_shape += (dense_dim,)

tests/python/pytorch/test_ffi-stream.py

@@ -2,12 +2,12 @@ import unittest
 from statistics import mean
 
 import backend as F
-import numpy as np
-import torch
 
 import dgl
 import dgl.ndarray as nd
 import dgl.ops as OPS
+import numpy as np
+import torch
 from dgl import rand_graph
 from dgl._ffi.streams import _dgl_get_stream, to_dgl_stream_handle
 from dgl.utils import to_dgl_context
@@ -138,6 +138,7 @@ def test_record_stream_graph_positive():
 
     stream = torch.cuda.Stream()
     results2 = torch.zeros_like(result)
+
     # Performs the computing in a background stream
     def perform_computing():
         with torch.cuda.stream(stream):
@@ -172,6 +173,7 @@ def test_record_stream_graph_negative():
 
     stream = torch.cuda.Stream()
     results2 = torch.zeros_like(result)
+
     # Performs the computing in a background stream
     def perform_computing():
         with torch.cuda.stream(stream):

tests/python/pytorch/test_heterograph-pickle.py

 import io
 import pickle
 
+import dgl
+
 import networkx as nx
 import torch
 
-import dgl
-
 
 def _reconstruct_pickle(obj):
     f = io.BytesIO()

tests/python/pytorch/test_multiprocessing-ipc.py

 import os
 import unittest
 
+import dgl
+
 import torch as th
 import torch.multiprocessing as mp
 
-import dgl
-
 
 def sub_ipc(g):
     print(g)

tests/python/pytorch/utils/test_pin_memory.py

 import backend as F
-import pytest
-import torch
 
 import dgl
+import pytest
+import torch
 
 
 @pytest.mark.skipif(

tests/python/tensorflow/test_nn.py

 from copy import deepcopy
 
 import backend as F
+
+import dgl
+import dgl.function as fn
+import dgl.nn.tensorflow as nn
 import networkx as nx
 import numpy as np
 import pytest
@@ -15,10 +19,6 @@ from test_utils.graph_cases import (
     random_graph,
 )
 
-import dgl
-import dgl.function as fn
-import dgl.nn.tensorflow as nn
-
 
 def _AXWb(A, X, W, b):
     X = tf.matmul(X, W)