parameters: (x, indices_or_sections, axis=0)
return_statement: return _split(x, indices_or_sections, opname="split", axis=axis)
docstring: Splits a tensor into multiple sub-tensors along the given axis.
  
      Args:
          x (Tensor): A Tensor to be divided.
          indices_or_sections (Union[int, tuple(int), list(int)]):
              If integer, :math:`N`, the tensor will be divided into
              :math:`N` equal tensors along axis.
              If tuple(int), list(int) or of sorted integers,
              the entries indicate where along axis the array is split.
              For example, :math:`[2, 3]` would, for :math:`axis=0`, result in
              three sub-tensors :math:`x[:2]`, :math:`x[2:3]`and :math:`x[3:]`.
              If an index exceeds the dimension of the array along axis,
              an empty sub-array is returned correspondingly.
          axis (int): The axis along which to split. Default: 0.
  
      Returns:
          A list of sub-tensors.
  
      Raises:
          TypeError: If argument `indices_or_sections` is not integer,
              tuple(int) or list(int) or argument `axis` is not integer.
          ValueError: If argument `axis` is out of range of :math:`[-x.ndim, x.ndim)`.
  
      Supported Platforms:
          ``Ascend`` ``GPU`` ``CPU``
  
      Examples:
          >>> import mindspore.numpy as np
          >>> input_x = np.arange(9).astype("float32")
          >>> output = np.split(input_x, 3)
          >>> print(output)
          (Tensor(shape=[3], dtype=Float32,
            value= [ 0.00000000e+00,  1.00000000e+00,  2.00000000e+00]),
           Tensor(shape=[3], dtype=Float32,
            value= [ 3.00000000e+00,  4.00000000e+00,  5.00000000e+00]),
           Tensor(shape=[3], dtype=Float32,
            value= [ 6.00000000e+00,  7.00000000e+00,  8.00000000e+00]))
docstring_summary: Splits a tensor into multiple sub-tensors along the given axis.
docstring_tokens: Splits a tensor into multiple sub - tensors along the given axis .
function: def split(x, indices_or_sections, axis=0):
      """
      Splits a tensor into multiple sub-tensors along the given axis.
  
      Args:
          x (Tensor): A Tensor to be divided.
          indices_or_sections (Union[int, tuple(int), list(int)]):
              If integer, :math:`N`, the tensor will be divided into
              :math:`N` equal tensors along axis.
              If tuple(int), list(int) or of sorted integers,
              the entries indicate where along axis the array is split.
              For example, :math:`[2, 3]` would, for :math:`axis=0`, result in
              three sub-tensors :math:`x[:2]`, :math:`x[2:3]`and :math:`x[3:]`.
              If an index exceeds the dimension of the array along axis,
              an empty sub-array is returned correspondingly.
          axis (int): The axis along which to split. Default: 0.
  
      Returns:
          A list of sub-tensors.
  
      Raises:
          TypeError: If argument `indices_or_sections` is not integer,
              tuple(int) or list(int) or argument `axis` is not integer.
          ValueError: If argument `axis` is out of range of :math:`[-x.ndim, x.ndim)`.
  
      Supported Platforms:
          ``Ascend`` ``GPU`` ``CPU``
  
      Examples:
          >>> import mindspore.numpy as np
          >>> input_x = np.arange(9).astype("float32")
          >>> output = np.split(input_x, 3)
          >>> print(output)
          (Tensor(shape=[3], dtype=Float32,
            value= [ 0.00000000e+00,  1.00000000e+00,  2.00000000e+00]),
           Tensor(shape=[3], dtype=Float32,
            value= [ 3.00000000e+00,  4.00000000e+00,  5.00000000e+00]),
           Tensor(shape=[3], dtype=Float32,
            value= [ 6.00000000e+00,  7.00000000e+00,  8.00000000e+00]))
      """
      return _split(x, indices_or_sections, opname="split", axis=axis)
function_tokens: def split ( x , indices_or_sections , axis = 0 ) : return _split ( x , indices_or_sections , opname = "split" , axis = axis )