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.
  
  import numpy as np
  from akg.utils import kernel_exec as utils
  from tests.common.test_op.ascend.slice import slice as op_slice
  from tests.common.tensorio import compare_tensor
  from tests.common.gen_random import random_gaussian
  
  def slice_run(shape, begin, size, dtype, attrs):
      op_attr = [begin, size]
  
      if 'tuning' in attrs.keys():
          t = attrs.get("tuning", False)
          kernel_name = attrs.get("kernel_name", False)
          mod = utils.op_build_test(op_slice, [shape], [dtype], op_attr, kernel_name=kernel_name, attrs=attrs, tuning=t)
          if t:
              expect, input, output = gen_data(begin, dtype, shape, size)
              return mod, expect, (input, output)
          else:
              return mod
      else:
          mod = utils.op_build_test(op_slice, [shape], [dtype], op_attr, kernel_name='slice', attrs=attrs)
          expect, input, output = gen_data(begin, dtype, shape, size)
          output = utils.mod_launch(mod, (input, output), expect=expect)  # unified launch
          return input, output, expect, compare_tensor(output, expect, rtol=5e-03, equal_nan=True)
  
  
  def gen_data(begin, dtype, shape, size):
      input = random_gaussian(shape, miu=1, sigma=0.3).astype(dtype)
      out_shape = [size[i] if size[i] > 0 else shape[i] - begin[i] for i in range(len(shape))]
      slice_range = [slice(begin[i], begin[i] + out_shape[i]) for i in range(len(out_shape))]
      expect = input[tuple(slice_range)]
      output = np.full(out_shape, np.nan, dtype)
      return expect, input, output
  