parameters: (x1, x2, dtype=None)
return_statement: return _apply_tensor_op(F.bitwise_xor, x1, x2, dtype=dtype)
docstring: Computes the bit-wise XOR of two arrays element-wise.
      Computes the bit-wise XOR of the underlying binary representation of the integers in
      the input arrays. This ufunc implements the C/Python operator ^.
  
      Note:
          Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
          not supported.
  
      Args:
          x1 (Tensor): Input array.
          x2 (Tensor): Input array. Only integer and boolean types are handled. If
              ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes
              the shape of the output).
          dtype (:class:`mindspore.dtype`, optional): Defaults to None. Overrides the dtype of the
              output Tensor.
  
      Returns:
          Tensor or scalar, this is a scalar if both x1 and x2 are scalars.
  
      Supported Platforms:
          ``Ascend``
  
      Examples:
          >>> import mindspore.numpy as np
          >>> print(np.bitwise_xor(13, 17))
          28
docstring_summary: Computes the bit-wise XOR of two arrays element-wise.
      Computes the bit-wise XOR of the underlying binary representation of the integers in
      the input arrays. This ufunc implements the C/Python operator ^.
docstring_tokens: Computes the bit - wise XOR of two arrays element - wise . Computes the bit - wise XOR of the underlying binary representation of the integers in the input arrays . This ufunc implements the C / Python operator ^ .
function: def bitwise_xor(x1, x2, dtype=None):
      """
      Computes the bit-wise XOR of two arrays element-wise.
      Computes the bit-wise XOR of the underlying binary representation of the integers in
      the input arrays. This ufunc implements the C/Python operator ^.
  
      Note:
          Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
          not supported.
  
      Args:
          x1 (Tensor): Input array.
          x2 (Tensor): Input array. Only integer and boolean types are handled. If
              ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes
              the shape of the output).
          dtype (:class:`mindspore.dtype`, optional): Defaults to None. Overrides the dtype of the
              output Tensor.
  
      Returns:
          Tensor or scalar, this is a scalar if both x1 and x2 are scalars.
  
      Supported Platforms:
          ``Ascend``
  
      Examples:
          >>> import mindspore.numpy as np
          >>> print(np.bitwise_xor(13, 17))
          28
      """
      return _apply_tensor_op(F.bitwise_xor, x1, x2, dtype=dtype)
function_tokens: def bitwise_xor ( x1 , x2 , dtype = None ) : return _apply_tensor_op ( F . bitwise_xor , x1 , x2 , dtype = dtype )