code: # 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
  import secrets
  from tests.common.tensorio import compare_tensor
  from akg.utils import kernel_exec as utils
  from tests.common.test_op import round
  from tests.common.gen_random import random_gaussian
  secretsGenerator = secrets.SystemRandom()
  def round_run(shape, dtype, attrs):
      in_shape = [shape]
      in_dtype = [dtype]
  
      if 'tuning' in attrs.keys():
          t = attrs.get("tuning", False)
          kernel_name = attrs.get("kernel_name", False)
          mod = utils.op_build_test(round.round_value, in_shape, in_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:
          mod = utils.op_build_test(round.round_value, in_shape, in_dtype, kernel_name='round', attrs=attrs)
          expect, input, output = gen_data(dtype, shape)
          output = utils.mod_launch(mod, (input, output), expect=expect)
          return input, output, expect, compare_tensor(output, expect, rtol=5e-03, equal_nan=True)
  
  
  def gen_data(dtype, shape):
      input = random_gaussian(shape, miu=1, sigma=10).astype(dtype)
      a = secretsGenerator.randint(0, 9)
      if a % 2 == 0:
          input = input.astype('int32') + 0.5
          input = input.astype(dtype)
      input_f16 = input.astype(np.float16)
      expect = np.round(input_f16).astype("int32")
      output = np.full(shape, np.nan, "int32")
      return expect, input, output
  
apis: numpy.round numpy.full