text: # Copyright 2019 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.
  # ============================================================================
  from mindspore import Tensor
  from mindspore.ops import operations as P
  import mindspore.nn as nn
  from mindspore.common.api import ms_function
  import numpy as np
  import mindspore.context as context
  from mindspore.ops.composite import GradOperation
  
  context.set_context(device_target="Ascend")
  
  
  class Grad(nn.Cell):
      def __init__(self, network):
          super(Grad, self).__init__()
          self.grad = GradOperation(name="get_all", 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.maxpool = P.MaxPool(pad_mode="SAME", window=3, stride=2)
  
      @ms_function
      def construct(self, x):
          output = self.maxpool(x)
          return output
  
  
  def test_net():
      x = np.random.randn(32, 64, 112, 112).astype(np.float32)
      output_grad = np.random.randn(32, 64, 56, 56).astype(np.float32)
      net = Grad(Net())
      output = net(Tensor(x), Tensor(output_grad))
      if isinstance(output, (tuple, list)):
          output = output[0]
      print(output.asnumpy())
  