code: def nrmse(
      y_pred: npt.NDArray[np.float32], y_true: npt.NDArray[np.float32]
  ) -> npt.NDArray[np.float32]:
      """
      Computes the normalized root mean squared 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 NRMSE for each metric.
      """
      rmse = np.sqrt((y_pred - y_true) ** 2).mean(0)
      return rmse / np.abs(y_true).mean(0)
  
imports: import numpy
  import numpy.typing
  import scipy.stats
label_desc: Use image node_id 1 for calling a global function with example usage: nrmse(y_pred, y_true) and returns: unknown