parameters: (ary, indices_or_sections)
docstring: Split an array into multiple sub-arrays horizontally (column-wise).
  
      Please refer to the `split` documentation.  `hsplit` is equivalent
      to `split` with ``axis=1``, the array is always split along the second
      axis regardless of the array dimension.
  
      See Also
      --------
      split : Split an array into multiple sub-arrays of equal size.
  
      Examples
      --------
      >>> x = np.arange(16.0).reshape(4, 4)
      >>> x
      array([[ 0.,   1.,   2.,   3.],
             [ 4.,   5.,   6.,   7.],
             [ 8.,   9.,  10.,  11.],
             [12.,  13.,  14.,  15.]])
      >>> np.hsplit(x, 2)
      [array([[  0.,   1.],
             [  4.,   5.],
             [  8.,   9.],
             [12.,  13.]]),
       array([[  2.,   3.],
             [  6.,   7.],
             [10.,  11.],
             [14.,  15.]])]
      >>> np.hsplit(x, np.array([3, 6]))
      [array([[ 0.,   1.,   2.],
             [ 4.,   5.,   6.],
             [ 8.,   9.,  10.],
             [12.,  13.,  14.]]),
       array([[ 3.],
             [ 7.],
             [11.],
             [15.]]),
       array([], shape=(4, 0), dtype=float64)]
  
      With a higher dimensional array the split is still along the second axis.
  
      >>> x = np.arange(8.0).reshape(2, 2, 2)
      >>> x
      array([[[0.,  1.],
              [2.,  3.]],
             [[4.,  5.],
              [6.,  7.]]])
      >>> np.hsplit(x, 2)
      [array([[[0.,  1.]],
             [[4.,  5.]]]),
       array([[[2.,  3.]],
             [[6.,  7.]]])]
docstring_summary: Split an array into multiple sub-arrays horizontally (column-wise).
docstring_tokens: Split an array into multiple sub - arrays horizontally ( column - wise ) .
function: def hsplit(ary, indices_or_sections):
      """
      Split an array into multiple sub-arrays horizontally (column-wise).
  
      Please refer to the `split` documentation.  `hsplit` is equivalent
      to `split` with ``axis=1``, the array is always split along the second
      axis regardless of the array dimension.
  
      See Also
      --------
      split : Split an array into multiple sub-arrays of equal size.
  
      Examples
      --------
      >>> x = np.arange(16.0).reshape(4, 4)
      >>> x
      array([[ 0.,   1.,   2.,   3.],
             [ 4.,   5.,   6.,   7.],
             [ 8.,   9.,  10.,  11.],
             [12.,  13.,  14.,  15.]])
      >>> np.hsplit(x, 2)
      [array([[  0.,   1.],
             [  4.,   5.],
             [  8.,   9.],
             [12.,  13.]]),
       array([[  2.,   3.],
             [  6.,   7.],
             [10.,  11.],
             [14.,  15.]])]
      >>> np.hsplit(x, np.array([3, 6]))
      [array([[ 0.,   1.,   2.],
             [ 4.,   5.,   6.],
             [ 8.,   9.,  10.],
             [12.,  13.,  14.]]),
       array([[ 3.],
             [ 7.],
             [11.],
             [15.]]),
       array([], shape=(4, 0), dtype=float64)]
  
      With a higher dimensional array the split is still along the second axis.
  
      >>> x = np.arange(8.0).reshape(2, 2, 2)
      >>> x
      array([[[0.,  1.],
              [2.,  3.]],
             [[4.,  5.],
              [6.,  7.]]])
      >>> np.hsplit(x, 2)
      [array([[[0.,  1.]],
             [[4.,  5.]]]),
       array([[[2.,  3.]],
             [[6.,  7.]]])]
  
      """
      if _nx.ndim(ary) == 0:
          raise ValueError('hsplit only works on arrays of 1 or more dimensions')
      if ary.ndim > 1:
          return split(ary, indices_or_sections, 1)
      else:
          return split(ary, indices_or_sections, 0)
function_tokens: def hsplit ( ary , indices_or_sections ) : if _nx . ndim ( ary ) == 0 : raise ValueError ( 'hsplit only works on arrays of 1 or more dimensions' ) if ary . ndim > 1 : return split ( ary , indices_or_sections , 1 ) else : return split ( ary , indices_or_sections , 0 )