code: def rmse(y, p):
      r"""Root Mean Square Error.
  
      .. math::
  
          RMSE(\mathbf{y}, \mathbf{p}) = \sqrt{MSE(\mathbf{y}, \mathbf{p})},
  
      with
  
      .. math::
  
          MSE(\mathbf{y}, \mathbf{p}) = |S| \sum_{i \in S} (y_i - p_i)^2
  
      Parameters
      ----------
      y : array-like of shape [n_samples, ]
          ground truth.
  
      p : array-like of shape [n_samples, ]
          predicted labels.
  
      Returns
      -------
      z: float
          root mean squared error.
      """
      z = y - p
      return np.sqrt(np.mean(np.multiply(z, z)))
  
imports: from __future__ import division
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: rmse(y, p) and returns: np