text: # Copyright 2019-2021 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.
  
  """expm1_run"""
  
  import numpy as np
  from akg.utils import kernel_exec as utils
  from tests.common.test_op.ascend import expm1
  from tests.common.tensorio import compare_tensor
  from tests.common.gen_random import random_gaussian
  
  def expm1_run(shape, dtype, attrs):
      """expm1_run"""
      if 'tuning' in attrs.keys():
          t = attrs.get("tuning", False)
          kernel_name = attrs.get("kernel_name", False)
          mod = utils.op_build_test(expm1.expm1, [shape], [dtype], kernel_name=kernel_name, attrs=attrs, tuning=t)
          if t:
              expect, inputs, output = gen_data(dtype, shape)
              return mod, expect, (inputs, output)
          return mod
      else:
          mod = utils.op_build_test(expm1.expm1, [shape], [dtype], kernel_name='expm1', attrs=attrs)
          expect, inputs, output = gen_data(dtype, shape)
          output = utils.mod_launch(mod, (inputs, output), expect=expect)
  
          return inputs, output, expect, compare_tensor(output, expect, rtol=5e-03, equal_nan=True)
  
  
  def gen_data(dtype, shape):
      """gen_data"""
      inputs = random_gaussian(shape, miu=1, sigma=0.1).astype(dtype)
      expect = np.expm1(inputs)
      output = np.full(expect.shape, np.nan, dtype)
      return expect, inputs, output
  