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 numpy as np
  from akg.utils import kernel_exec as utils
  from test_op import greater_equal
  from gen_random import random_gaussian
  
  
  def greater_equal_run(shapes, dtype, kernel_name, attrs, cce_path="./"):
      if 'tuning' in attrs.keys():
          t = attrs.get("tuning", False)
          kernel_name = attrs.get("kernel_name", False)
          mod = utils.op_build_test(greater_equal.greater_equal, shapes, [dtype, dtype], kernel_name=kernel_name, attrs=attrs, tuning=t)
          if t:
              benchMark, inputs, output = gen_data(shapes)
              return mod, benchMark, inputs + [output]
          else:
              return mod
      else:
          mod = utils.op_build_test(greater_equal.greater_equal, shapes, [dtype, dtype], kernel_name=kernel_name, attrs=attrs)
          benchMark, inputs, output = gen_data(shapes)
          output = utils.mod_launch(mod, inputs + [output], expect=benchMark)
  
          return inputs, output, benchMark, np.array_equal(output, benchMark)
  
  
  def gen_data(shapes):
      inputs = []
      for i in range(len(shapes)):
          shape = shapes[i]
          input = random_gaussian(shape, miu=1, sigma=0.1).astype(np.float16)
          inputs.append(input)
  
      if len(inputs) != 2:
          raise RuntimeError("inputs num should be 2")
      benchMark = np.greater_equal(inputs[0], inputs[1])
      output = np.full(benchMark.shape, 0, bool)
      return benchMark, inputs, output
  