code: def rmse(x1, x2, axis=0):
      """root mean squared 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
      -------
      rmse : ndarray or float
         root mean squared error 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, for example
      numpy matrices will silently produce an incorrect result.
      """
      x1 = np.asanyarray(x1)
      x2 = np.asanyarray(x2)
      return np.sqrt(mse(x1, x2, axis=axis))
  
imports: import numpy
  from statsmodels.tools.validation import array_like
label_desc: Use image node_id 2 for calling a global function with example usage: rmse(x1, x2, axis) and returns: np