code: def smape(
      y_pred: npt.NDArray[np.float32], y_true: npt.NDArray[np.float32]
  ) -> npt.NDArray[np.float32]:
      """
      Computes the symmetric mean absolute percentage error of the predictions.
  
      Args:
          y_pred: Array of shape [N, D] with the predictions (N: number of elements, D: number of
              metrics).
          y_true: Array of shape [N, D] with the true metrics.
  
      Returns:
          Array of shape [D] with the average sMAPE for each metric.
      """
      num = np.abs(y_pred - y_true)
      denom = (np.abs(y_pred) + np.abs(y_true)) / 2
      return 100 * (num / denom).mean(0)
  
imports: import numpy
  import numpy.typing
  import scipy.stats
label_desc: Use image node_id 2 for calling a global function with example usage: smape(y_pred, y_true) and returns: unknown