code: def print_normalized_rmse(
      actuals: np.array, predicted: np.array, start_from: int = 0
  ):
      """
      This utility calculates rmse between actuals and predicted. However, it does one more.
      If the original is given, it calculates Normalized RMSE using the original array's std deviation.
      """
      actuals = actuals[start_from:]
      predicted = predicted[start_from:]
      rmse = np.sqrt(np.mean(mean_squared_error(actuals, predicted)))
      norm_rmse = rmse / actuals.std()
      print("RMSE = {:,.2f}".format(rmse))
      print("Std Deviation of Actuals = {:,.2f}".format(actuals.std()))
      print("Normalized RMSE = %0.0f%%" % (100 * norm_rmse))
      return rmse, norm_rmse
  
file_path: Auto_TS/auto_ts/utils/metrics.py
imports: from typing import Tuple
  import numpy
  import pandas
  import matplotlib.pyplot
  from sklearn.metrics import mean_absolute_error, mean_squared_error
  import pdb
label_desc: Use image node_id 3 for calling a global function with example usage: print_normalized_rmse(actuals, predicted, start_from) and returns: rmse,  norm_rmse