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.ops.operations import Tile
  from mindspore.nn import Cell
  from mindspore.common.tensor import Tensor
  from mindspore.common.api import ms_function
  import mindspore.context as context
  from mindspore.common.initializer import initializer
  from mindspore.common.parameter import Parameter
  import numpy as np
  
  context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
  
  input_x0 = np.arange(2).reshape((2, 1, 1)).astype(np.float32)
  mul0 = (8, 1, 1)
  input_x1 = np.arange(32).reshape((2, 4, 4)).astype(np.float32)
  mul1 = (2, 2, 2)
  input_x2 = np.arange(1).reshape((1, 1, 1)).astype(np.float32)
  mul2 = (1, 1, 1)
  
  
  class Net(Cell):
      def __init__(self):
          super(Net, self).__init__()
          self.Tile = Tile()
  
          self.input_x0 = Parameter(initializer(Tensor(input_x0), input_x0.shape), name='x0')
          self.mul0 = mul0
          self.input_x1 = Parameter(initializer(Tensor(input_x1), input_x1.shape), name='x1')
          self.mul1 = mul1
          self.input_x2 = Parameter(initializer(Tensor(input_x2), input_x2.shape), name='x2')
          self.mul2 = mul2
  
      @ms_function
      def construct(self):
          output = (self.Tile(self.input_x0, self.mul0),
                    self.Tile(self.input_x1, self.mul1),
                    self.Tile(self.input_x2, self.mul2))
          return output
  
  
  @pytest.mark.level0
  @pytest.mark.platform_x86_gpu_training
  @pytest.mark.env_onecard
  def test_tile():
      net = Net()
      output = net()
  
      expect0 = np.tile(input_x0, mul0)
      diff0 = output[0].asnumpy() - expect0
      error0 = np.ones(shape=expect0.shape) * 1.0e-5
      assert np.all(diff0 < error0)
      assert (output[0].shape() == expect0.shape)
  
      expect1 = np.tile(input_x1, mul1)
      diff1 = output[1].asnumpy() - expect1
      error1 = np.ones(shape=expect1.shape) * 1.0e-5
      assert np.all(diff1 < error1)
      assert (output[1].shape() == expect1.shape)
  
      expect2 = np.tile(input_x2, mul2)
      diff2 = output[2].asnumpy() - expect2
      error2 = np.ones(shape=expect2.shape) * 1.0e-5
      assert np.all(diff2 < error2)
      assert (output[2].shape() == expect2.shape)
  