code: def horizoned_msis(y, yhat_lower, yhat_upper, y_obs, m, a=0.025):
      """Return the mean scaled interval score over all horizons.
  
      Args:
        y: An array containing the true values.
        yhat_lower: An array containing the a% quantile of the predicted
          distribution.
        yhat_upper: An array containing the (1-a)% quantile of the
          predicted distribution.
        y_obs: An array containing the training values.
        m: The season length.
        a: A scalar in [0, 1] specifying the quantile window to evaluate.
  
      Returns:
        A list a, with a[i] containing the MSIS over y[0] .. y[i].
      """
      assert len(yhat_lower) == len(yhat_upper)
      return [
          msis(
              y[: i + 1],
              yhat_lower[: i + 1],
              yhat_upper[: i + 1],
              y_obs,
              m,
              a,
          )
          for i in range(len(yhat_lower))
      ]
  
imports: import numpy
label_desc: Use image node_id 6 for calling a global function with example usage: horizoned_msis(y, yhat_lower, yhat_upper, y_obs, m, a) and returns: unknown