parameters: (x, row_axis, col_axis, op)
return_statement: return result
docstring: Compute a function of the singular values of the 2-D matrices in `x`.
  
      This is a private utility function used by `numpy.linalg.norm()`.
  
      Parameters
      ----------
      x : ndarray
      row_axis, col_axis : int
          The axes of `x` that hold the 2-D matrices.
      op : callable
          This should be either numpy.amin or `numpy.amax` or `numpy.sum`.
  
      Returns
      -------
      result : float or ndarray
          If `x` is 2-D, the return values is a float.
          Otherwise, it is an array with ``x.ndim - 2`` dimensions.
          The return values are either the minimum or maximum or sum of the
          singular values of the matrices, depending on whether `op`
          is `numpy.amin` or `numpy.amax` or `numpy.sum`.
docstring_summary: Compute a function of the singular values of the 2-D matrices in `x`.
docstring_tokens: Compute a function of the singular values of the 2 - D matrices in x .
function: def _multi_svd_norm(x, row_axis, col_axis, op):
      """Compute a function of the singular values of the 2-D matrices in `x`.
  
      This is a private utility function used by `numpy.linalg.norm()`.
  
      Parameters
      ----------
      x : ndarray
      row_axis, col_axis : int
          The axes of `x` that hold the 2-D matrices.
      op : callable
          This should be either numpy.amin or `numpy.amax` or `numpy.sum`.
  
      Returns
      -------
      result : float or ndarray
          If `x` is 2-D, the return values is a float.
          Otherwise, it is an array with ``x.ndim - 2`` dimensions.
          The return values are either the minimum or maximum or sum of the
          singular values of the matrices, depending on whether `op`
          is `numpy.amin` or `numpy.amax` or `numpy.sum`.
  
      """
      y = moveaxis(x, (row_axis, col_axis), (-2, -1))
      result = op(svd(y, compute_uv=False), axis=-1)
      return result
function_tokens: def _multi_svd_norm ( x , row_axis , col_axis , op ) : y = moveaxis ( x , ( row_axis , col_axis ) , ( - 2 , - 1 ) ) result = op ( svd ( y , compute_uv = False ) , axis = - 1 ) return result