Fixing some tiny faults about Pylint in my code(ops)

tests/st/ops/ascend/test_autocast.py

@@ -14,14 +14,13 @@
 # ============================================================================
 """multitype_ops directory test case"""
 import numpy as np
-from functools import partial, reduce
+import pytest
 
 import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore import dtype as mstype
-from mindspore.ops import functional as F, composite as C
+from mindspore.ops import functional as F
 import mindspore.context as context
-import pytest
 
 
 class TensorIntAutoCast(nn.Cell):
@@ -65,7 +64,7 @@ class TensorAutoCast(nn.Cell):
 
 def test_tensor_auto_cast():
     context.set_context(mode=context.GRAPH_MODE)
-    t0 = Tensor([True, False], mstype.bool_)
+    Tensor([True, False], mstype.bool_)
     t_uint8 = Tensor(np.ones([2, 1, 2, 2]), mstype.uint8)
     t_int8 = Tensor(np.ones([2, 1, 2, 2]), mstype.int8)
     t_int16 = Tensor(np.ones([2, 1, 2, 2]), mstype.int16)

tests/st/ops/ascend/test_ops_infer.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 # ============================================================================
 """ test nn ops """
-import functools
 import numpy as np
 import mindspore.nn as nn
 import mindspore.common.dtype as mstype

tests/st/ops/cpu/test_lstm_op.py

@@ -14,10 +14,10 @@
 # ============================================================================
 
 import pytest
-import mindspore.nn as nn
-from mindspore.common.api import ms_function
 import numpy as np
+import mindspore.nn as nn
 import mindspore.context as context
+from mindspore.common.api import ms_function
 from mindspore.common.initializer import initializer
 from mindspore.ops import composite as C
 from mindspore.ops import operations as P
@@ -196,10 +196,6 @@ def test_multi_layer_bilstm():
     bidirectional = True
     dropout = 0.0
 
-    num_directions = 1
-    if bidirectional:
-        num_directions = 2
-
     net = MultiLayerBiLstmNet(seq_len, batch_size, input_size, hidden_size, num_layers, has_bias, bidirectional,
                               dropout)
     y, h, c, _, _ = net()
@@ -305,9 +301,6 @@ def test_grad():
     has_bias = True
     bidirectional = False
     dropout = 0.0
-    num_directions = 1
-    if bidirectional:
-        num_directions = 2
     net = Grad(Net(seq_len, batch_size, input_size, hidden_size, num_layers, has_bias, bidirectional, dropout))
     dy = np.array([[[-3.5471e-01, 7.0540e-01],
                     [2.7161e-01, 1.0865e+00]],

tests/ut/python/ops/test_layer_switch.py

@@ -78,4 +78,4 @@ def test_layer_switch():
     net = MySwitchNet()
     x = Tensor(np.ones((3, 3, 24, 24)), mindspore.float32)
     index = Tensor(0, dtype=mindspore.int32)
-    y = net(x, index)
+    net(x, index)