code: def stde(x1, x2, ddof=0, axis=0):
      """standard deviation of error
  
      Parameters
      ----------
      x1, x2 : array_like
         The performance measure depends on the difference between these two
         arrays.
      axis : int
         axis along which the summary statistic is calculated
  
      Returns
      -------
      stde : ndarray or float
         standard deviation of difference along given axis.
  
      Notes
      -----
      If ``x1`` and ``x2`` have different shapes, then they need to broadcast.
      This uses ``numpy.asanyarray`` to convert the input. Whether this is the
      desired result or not depends on the array subclass.
      """
      x1 = np.asanyarray(x1)
      x2 = np.asanyarray(x2)
      return np.std(x1 - x2, ddof=ddof, axis=axis)
  
imports: import numpy
  from statsmodels.tools.validation import array_like
label_desc: Use image node_id 10 for calling a global function with example usage: stde(x1, x2, ddof, axis) and returns: np