code: """
  Copyright 2020 The OneFlow Authors. All rights reserved.
  
  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 unittest
  from collections import OrderedDict
  
  import numpy as np
  from test_util import GenArgList
  
  import oneflow as flow
  import oneflow.unittest
  
  from oneflow.test_utils.automated_test_util import *
  
  
  def _test_ne(test_case, shape, device):
      arr1 = np.random.randn(*shape)
      arr2 = np.random.randn(*shape)
      input = flow.tensor(arr1, dtype=flow.float32, device=flow.device(device))
      other = flow.tensor(arr2, dtype=flow.float32, device=flow.device(device))
      of_out = flow.ne(input, other)
      of_out2 = flow.not_equal(input, other)
      np_out = np.not_equal(arr1, arr2)
      test_case.assertTrue(np.array_equal(of_out.numpy(), np_out))
      test_case.assertTrue(np.array_equal(of_out2.numpy(), np_out))
  
  
  def _test_tensor_ne_operator(test_case, shape, device):
      arr1 = np.random.randn(*shape)
      arr2 = np.random.randn(*shape)
      input = flow.tensor(arr1, dtype=flow.float32, device=flow.device(device))
      other = flow.tensor(arr2, dtype=flow.float32, device=flow.device(device))
      of_out = input.ne(other)
      np_out = np.not_equal(arr1, arr2)
      test_case.assertTrue(np.array_equal(of_out.numpy(), np_out))
  
  
  def _test_ne_int(test_case, shape, device):
      arr = np.random.randn(*shape)
      input = flow.tensor(arr, dtype=flow.float32, device=flow.device(device))
      num = 1
      of_out = flow.ne(input, num)
      np_out = np.not_equal(arr, num)
      test_case.assertTrue(np.array_equal(of_out.numpy(), np_out))
  
  
  def _test_tensor_ne_operator_int(test_case, shape, device):
      arr = np.random.randn(*shape)
      input = flow.tensor(arr, dtype=flow.float32, device=flow.device(device))
      num = 1
      of_out = input.ne(num)
      np_out = np.not_equal(arr, num)
      test_case.assertTrue(np.array_equal(of_out.numpy(), np_out))
  
  
  def _test_ne_float(test_case, shape, device):
      arr = np.random.randn(*shape)
      input = flow.tensor(arr, dtype=flow.float32, device=flow.device(device))
      num = 1.0
      of_out = flow.ne(input, num)
      np_out = np.not_equal(arr, num)
      test_case.assertTrue(np.array_equal(of_out.numpy(), np_out))
  
  
  def _test_tensor_ne_operator_float(test_case, shape, device):
      arr = np.random.randn(*shape)
      input = flow.tensor(arr, dtype=flow.float32, device=flow.device(device))
      num = 1.0
      of_out = input.ne(num)
      np_out = np.not_equal(arr, num)
      test_case.assertTrue(np.array_equal(of_out.numpy(), np_out))
  
  
  @flow.unittest.skip_unless_1n1d()
  class TestNe(flow.unittest.TestCase):
      def test_ne(test_case):
          arg_dict = OrderedDict()
          arg_dict["test_func"] = [
              _test_ne,
              _test_tensor_ne_operator,
              _test_ne_int,
              _test_tensor_ne_operator_int,
              _test_ne_float,
              _test_tensor_ne_operator_float,
          ]
          arg_dict["shape"] = [(2, 3), (2, 3, 4), (2, 4, 5, 6)]
          arg_dict["device"] = ["cpu", "cuda"]
          for arg in GenArgList(arg_dict):
              arg[0](test_case, *arg[1:])
  
      @autotest(auto_backward=False, check_graph=False)
      def test_ne_with_0shape_data(test_case):
          device = random_device()
          x1 = random_pytorch_tensor(4, 2, 3, 0, 5).to(device)
          x2 = random_pytorch_tensor(4, 2, 3, 0, 5).to(device)
          y1 = torch.ne(x1, x2)
          y2 = torch.ne(x1, 2)
          y3 = torch.ne(x1, 2.0)
          return (y1, y2, y3)
  
  
  if __name__ == "__main__":
      unittest.main()
  
apis: numpy.not_equal numpy.random.randn