code: # 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 akg
  import numpy as np
  from akg.utils import kernel_exec as utils
  from akg.ops.math import Sqrt
  from tests.common.tensorio import compare_tensor
  from tests.common.gen_random import random_gaussian
  from akg.utils.result_analysis import target_profiling
  from akg.utils.format_transform import to_tvm_nd_array
  
  def sqrt_run(shape, dtype, attrs):
      if 'tuning' in attrs.keys():
          t = attrs.get("tuning", False)
          kernel_name = attrs.get("kernel_name", False)
          mod = utils.op_build_test(Sqrt, [shape], [dtype], kernel_name=kernel_name, attrs=attrs, tuning=t)
          if t:
              expect, input, output = gen_data(dtype, shape)
              return mod, expect, (input, output)
          else:
              return mod
      else:
          expect, input, output = gen_data(dtype, shape)
          mod = utils.op_build_test(Sqrt, [shape], [dtype], kernel_name='sqrt', attrs=attrs)
          output = utils.mod_launch(mod, (input, output), expect=expect)
          if attrs.get("profiling", False):
              target_name = attrs["target"].split()[0]
              args_list = to_tvm_nd_array([input, output], akg.tvm.context(target_name, 0))
              target_profiling(mod, *args_list, target=target_name, repeat_time=attrs["repeat_times"])
          return input, output, expect, compare_tensor(output, expect, rtol=5e-03, equal_nan=True)
  
  
  def gen_data(dtype, shape):
      # Generate data for testing the op
      input = random_gaussian(shape, miu=1, sigma=0.1).astype(dtype)
      input = np.abs(input)
      expect = np.sqrt(input)
      output = np.full(expect.shape, np.nan, dtype)
      return expect, input, output
  
apis: numpy.sqrt numpy.full numpy.abs