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
  from mindspore import Tensor
  from mindspore.nn import Cell
  import mindspore.ops.operations as P
  
  context.set_context(mode=context.GRAPH_MODE, enable_graph_kernel=True, device_target="GPU")
  
  
  class Net(Cell):
      def __init__(self):
          super(Net, self).__init__()
          self.add = P.TensorAdd()
          self.sub = P.Sub()
          self.mul = P.Mul()
          self.div = P.RealDiv()
          self.sqrt = P.Sqrt()
          self.pow = P.Pow()
          self.neg = P.Neg()
  
      def construct(self, x, y):
          add_res1 = self.add(x, 4)
          add_res2 = self.add(add_res1, 5)
          sub_res = self.sub(y, 3)
          mul_res = self.mul(self.sqrt(add_res2), self.sqrt(sub_res))
          div_res = self.div(mul_res, self.sqrt(mul_res))
          pow_res = self.pow(y, 2)
          neg_res = self.neg(self.neg(pow_res))
          return self.add(div_res, neg_res)
  
  
  @pytest.mark.level0
  @pytest.mark.platform_x86_gpu_training
  @pytest.mark.env_onecard
  def test_basic():
      input_x = np.random.normal(0, 1, [2, 3, 4, 3]).astype(np.float32)
      input_y = np.random.normal(0, 1, [2, 3, 4, 3]).astype(np.float32)
      input_y = np.abs(input_y) + 3
      add_res = input_x + 9
      sub_res = input_y + (-3)
      mul_res = np.sqrt(add_res * sub_res)
      div_res = np.sqrt(mul_res)
      pow_res = input_y * input_y
      neg_res = pow_res
      expect = div_res + neg_res
  
      net = Net()
      result = net(Tensor(input_x), Tensor(input_y))
  
      res = np.allclose(expect, result.asnumpy(), rtol=1.e-4, atol=1.e-7, equal_nan=True)
      assert res
  