code: def update_additional_scorer_kwargs(
      initial_kwargs: Dict[str, Any],
      y_train: pd.Series,
      lower: pd.Series,
      upper: pd.Series,
  ) -> Dict[str, Any]:
      """Updates the initial kwargs with additional scorer kwargs
      NOTE: Initial kwargs are obtained from experiment, e.g. {"sp": 12} and
      are common to all folds.
      The additional kwargs such as y_train, lower, upper are specific to each
      fold and must be updated dynamically as such.
  
      Parameters
      ----------
      initial_kwargs : Dict[str, Any]
          Initial kwargs are obtained from experiment, e.g. {"sp": 12} and
          are common to all folds
      y_train : pd.Series
          Training Data. Used in metrics such as MASE
      lower : pd.Series
          Lower Limits of Predictions. Used in metrics such as INPI
      upper : pd.Series
          Upper Limits of Predictions. Used in metrics such as INPI
  
      Returns
      -------
      Dict[str, Any]
          Updated kwargs dictionary
      """
      additional_scorer_kwargs = initial_kwargs.copy()
      additional_scorer_kwargs.update(
          {"y_train": y_train, "lower": lower, "upper": upper}
      )
      return additional_scorer_kwargs
  
imports: from typing import Any, Dict, List, Optional, Tuple, Union
  import numpy
  import pandas
  from sktime.forecasting.base import ForecastingHorizon
label_desc: Use image node_id 3 for calling a global function with example usage: update_additional_scorer_kwargs(initial_kwargs, y_train, lower, upper) and returns: additional_scorer_kwargs