code: def print_static_rmse(
      actual: np.array,
      predicted: np.array,
      start_from: int = 0,
      verbose: int = 0,
  ):
      """
      this calculates the ratio of the rmse error to the standard deviation of the actuals.
      This ratio should be below 1 for a model to be considered useful.
      The comparison starts from the row indicated in the "start_from" variable.
      """
      rmse = np.sqrt(
          mean_squared_error(
              actual[start_from:], predicted[start_from:]
          )
      )
      std_dev = actual[start_from:].std()
      if verbose >= 1:
          print("    RMSE = %0.2f" % rmse)
          print("    Std Deviation of Actuals = %0.2f" % (std_dev))
          print(
              "    Normalized RMSE (as pct of std dev) = %0.1f%%"
              % (rmse * 100 / std_dev)
          )
      return rmse, rmse / std_dev
  
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 1 for calling a global function with example usage: print_static_rmse(actual, predicted, start_from, verbose) and returns: rmse,  unknown