text: # Copyright 2020 Huawei Technologies Co., Ltd
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  # http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ============================================================================
  import numpy as np
  import pytest
  
  import mindspore.context as context
  import mindspore.nn as nn
  from mindspore import Tensor
  from mindspore.common.api import ms_function
  from mindspore.ops import operations as P
  from mindspore.ops.composite import GradOperation
  
  context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
  
  
  class Grad(nn.Cell):
      def __init__(self, network):
          super(Grad, self).__init__()
          self.grad = GradOperation(get_all=True, sens_param=True)
          self.network = network
  
      @ms_function
      def construct(self, input_, output_grad):
          return self.grad(self.network)(input_, output_grad)
  
  
  class Net(nn.Cell):
      def __init__(self):
          super(Net, self).__init__()
          self.ops = P.Abs()
  
      def construct(self, x):
          return self.ops(x)
  
  @pytest.mark.level0
  @pytest.mark.platform_x86_cpu
  @pytest.mark.env_onecard
  @pytest.mark.parametrize('dtype', [np.int, np.float32, np.float64])
  def test_abs(dtype):
      """
      Feature: ALL To ALL
      Description: test cases for Abs
      Expectation: the result match to numpy
      """
      x = np.random.randn(2, 3, 3, 4).astype(dtype)
      y_expect = np.abs(x)
      net = Net()
      out = net(Tensor(x))
      assert (out.asnumpy() == y_expect).all()
      sens = np.random.randn(2, 3, 3, 4).astype(dtype)
      backword_net = Grad(Net())
      output = backword_net(Tensor(x), Tensor(sens))
      print(len(output))
      print(output[0].asnumpy())
  