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.
  # ============================================================================
  
  import pytest
  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
  
  context.set_context(device_target='GPU')
  
  
  class Net(nn.Cell):
      def __init__(self):
          super(Net, self).__init__()
          self.add = P.AddN()
  
      @ms_function
      def construct(self, x, y, z):
          return self.add((x, y, z))
  
  @pytest.mark.level0
  @pytest.mark.platform_x86_gpu_training
  @pytest.mark.env_onecard
  def test_net():
      x = np.arange(1 * 3 * 3 * 4).reshape(1, 3, 3, 4).astype(np.float32)
      y = np.arange(1 * 3 * 3 * 4).reshape(1, 3, 3, 4).astype(np.float32)
      z = np.arange(1 * 3 * 3 * 4).reshape(1, 3, 3, 4).astype(np.float32)
      add = Net()
      output = add(Tensor(x), Tensor(y), Tensor(z))
      expect_result = [[[[0., 3., 6., 9.],
                         [12., 15., 18., 21.],
                         [24., 27., 30., 33.]],
                        [[36., 39., 42., 45.],
                         [48., 51., 54., 57.],
                         [60., 63., 66., 69.]],
                        [[72., 75., 78., 81.],
                         [84., 87., 90., 93.],
                         [96., 99., 102., 105.]]]]
  
      assert (output.asnumpy() == expect_result).all()
  