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 tests.common.test_op import squeeze
  
  from tests.common.tensorio import compare_tensor
  from akg import tvm
  
  def squeeze_run(shape, axis, dtype, kernel_name="squeeze", attrs=None):
      op_attrs = [axis]
      if attrs is not None and attrs.get("dynamic"):
          build_shape = []
          for i in range(len(shape)):
              build_shape.append(tvm.var("I" + str(i)))
      else:
          build_shape = shape
      if 'tuning' in attrs.keys():
          t = attrs.get("tuning", False)
          kernel_name = attrs.get("kernel_name", False)
          mod = utils.op_build_test(squeeze.squeeze, [build_shape], [dtype], op_attrs, kernel_name=kernel_name, attrs=attrs, tuning=t)
          if t:
              expect, input, output = gen_data(axis, dtype, shape)
              return mod, expect, (input, output)
          else:
              return mod
      else:
          expect, input, output = gen_data(axis, dtype, shape)
          mod = utils.op_build_test(squeeze.squeeze, [build_shape], [dtype], op_attrs, kernel_name=kernel_name, attrs=attrs)
          args = [input, output]
          if attrs is not None and attrs.get("dynamic"):
              for i in range(len(shape)):
                  args.append(shape[i])
          output = utils.mod_launch(mod, args, outputs=(1,), expect=expect)
          return input, output, expect, compare_tensor(output, expect, rtol=5e-03, equal_nan=True)
  
  
  def gen_data(axis, dtype, shape):
      input = np.random.randint(100, size=shape).astype(dtype)
      expect = np.squeeze(input, axis=axis)
      output = np.full(expect.shape, np.nan, dtype)
      return expect, input, output
  