code: def mase(y, yhat, y_obs, m):
      """Return the mean absolute scaled error.
  
      Args:
          y: An array containing the true values.
          yhat: An array containing the predicted values.
          y_obs: An array containing the training values.
          m: The season length.
  
      Returns:
        The scalar MASE.
      """
      # https://en.wikipedia.org/wiki/Mean_absolute_scaled_error
      assert len(yhat) == len(y)
      n = len(y_obs)
      h = len(y)
      assert 0 < m < len(y_obs)
      numer = np.sum(np.abs(y - yhat))
      denom = np.sum(np.abs(y_obs[m:] - y_obs[:-m])) / (n - m)
      return (1 / h) * (numer / denom)
  
imports: import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: mase(y, yhat, y_obs, m) and returns: unknown