parameters: (*args)
return_statement: return res
docstring: Broadcasts any number of arrays against each other.
  
      Note:
          Numpy argument `subok` is not supported.
          In graph mode, returns a tuple of Tensor instead of a list
          of Tensor.
  
      Args:
          *args (Tensor): The arrays to broadcast.
  
      Returns:
          List of Tensor.
  
      Raises:
          ValueError: If arrays cannot be broadcast.
  
      Supported Platforms:
          ``Ascend`` ``GPU`` ``CPU``
  
      Example:
          >>> import mindspore.numpy as np
          >>> x = np.array([[1,2,3]])
          >>> y = np.array([[4],[5]])
          >>> output = np.broadcast_arrays(x, y)
          >>> print(output)
          [Tensor(shape=[2, 3], dtype=Int32, value=
          [[1, 2, 3],
          [1, 2, 3]]), Tensor(shape=[2, 3], dtype=Int32, value=
          [[4, 4, 4],
          [5, 5, 5]])]
docstring_summary: Broadcasts any number of arrays against each other.
docstring_tokens: Broadcasts any number of arrays against each other .
function: def broadcast_arrays(*args):
      """
      Broadcasts any number of arrays against each other.
  
      Note:
          Numpy argument `subok` is not supported.
          In graph mode, returns a tuple of Tensor instead of a list
          of Tensor.
  
      Args:
          *args (Tensor): The arrays to broadcast.
  
      Returns:
          List of Tensor.
  
      Raises:
          ValueError: If arrays cannot be broadcast.
  
      Supported Platforms:
          ``Ascend`` ``GPU`` ``CPU``
  
      Example:
          >>> import mindspore.numpy as np
          >>> x = np.array([[1,2,3]])
          >>> y = np.array([[4],[5]])
          >>> output = np.broadcast_arrays(x, y)
          >>> print(output)
          [Tensor(shape=[2, 3], dtype=Int32, value=
          [[1, 2, 3],
          [1, 2, 3]]), Tensor(shape=[2, 3], dtype=Int32, value=
          [[4, 4, 4],
          [5, 5, 5]])]
      """
      shapes = map(F.shape, args)
      out_shape = _infer_out_shape(*shapes)
      res = []
      for arr in args:
          res.append(broadcast_to(arr, out_shape))
      return res
function_tokens: def broadcast_arrays ( * args ) : shapes = map ( F . shape , args ) out_shape = _infer_out_shape ( * shapes ) res = [ ] for arr in args : res . append ( broadcast_to ( arr , out_shape ) ) return res