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 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="Ascend")
  
  
  class Grad(nn.Cell):
      def __init__(self, network):
          super(Grad, self).__init__()
          self.grad = GradOperation(get_all=True)
          self.network = network
  
      @ms_function
      def construct(self, input_):
          return self.grad(self.network)(input_)
  
  
  class Net(nn.Cell):
      def __init__(self):
          super(Net, self).__init__()
          self.relu_v2 = P.ReLUV2()
  
      def construct(self, x):
          return self.relu_v2(x)
  
  
  def test_net():
      x = Tensor(np.ones((2, 3, 3, 4)).astype(np.float32))
      relu_net = Net()
      relu_output = relu_net(x)
      net = Grad(Net())
      output_grad = net(x)
      print(relu_output[0].asnumpy())
      print(relu_output[1].asnumpy())
      print(len(output_grad))
      print(output_grad[0].asnumpy())
  