text: """
  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 oneflow as flow
  from oneflow.framework.tensor import register_tensor_op
  
  
  def masked_select_op(input, mask):
      """
  
      Returns a new 1-D tensor which indexes the input tensor according to the boolean mask mask which is a BoolTensor(In oneFlow BoolTensor is replaced by Int8Tensor).
  
      The shapes of the mask tensor and the input tensor don’t need to match, but they must be broadcastable.
  
      Args:
          input (Tensor): the input tensor.
          mask (Tensor): the tensor containing the binary mask to index with
  
      For example:
  
      .. code-block:: python
  
          >>> import oneflow as flow
          >>> import numpy as np
          
          >>> input = flow.tensor(np.array([[-0.4620, 0.3139], [0.3898, -0.7197], [0.0478, -0.1657]]), dtype=flow.float32)
          >>> mask = input.gt(0.05)
          >>> out = flow.masked_select(input, mask)
          >>> out
          tensor([0.3139, 0.3898], dtype=oneflow.float32)
      """
  
      assert len(input.shape) == len(
          mask.shape
      ), f"The dim of masked_select module's inputs can not match, please check!"
      broadcast_like_shape = []
      broadcast_x_axes = []
      broadcast_mask_axes = []
      for i in range(len(input.shape)):
          max_dim = max(input.shape[i], mask.shape[i])
          broadcast_like_shape.append(max_dim)
          if max_dim != input.shape[i]:
              broadcast_x_axes.append(i)
          if max_dim != mask.shape[i]:
              broadcast_mask_axes.append(i)
      broadcast_like_tensor = flow.zeros(
          tuple(broadcast_like_shape), dtype=flow.float32, device=input.device
      )
      broadcast_like_tensor.requires_grad = input.requires_grad or mask.requires_grad
      if len(broadcast_x_axes) != 0:
          input = flow.broadcast_like(
              input, broadcast_like_tensor, broadcast_axes=tuple(broadcast_x_axes)
          )
      if len(broadcast_mask_axes) != 0:
          mask = flow.broadcast_like(
              mask, broadcast_like_tensor, broadcast_axes=tuple(broadcast_mask_axes)
          )
      mask = mask.to(dtype=input.dtype)
      res = flow._C.mul(input, mask)
      indices = flow.argwhere(res)
      gather_res = flow._C.gather_nd(res, indices)
      return gather_res.flatten()
  
  
  @register_tensor_op("masked_select")
  def tensor_masked_select_op(input, mask):
      """
  
      See :func:`oneflow.masked_select`
  
      """
      return masked_select_op(input, mask)
  
  
  if __name__ == "__main__":
      import doctest
  
      doctest.testmod(raise_on_error=True)
  