code: def _create_event_offset_features(
      event, config, feature, additive_events, multiplicative_events
  ):
      """
      Create event offset features for the given event, config and feature
      Parameters
      ----------
          event : str
              Name of the event
          config : configure.ConfigEvents
              User specified events, holidays, and country specific holidays
          feature : pd.Series
              Feature for the event
          additive_events : pd.DataFrame
              Dataframe of additive events
          multiplicative_events : pd.DataFrame
              Dataframe of multiplicative events
      Returns
      -------
          tuple
              Tuple of additive_events and multiplicative_events
      """
      lw = config.lower_window
      uw = config.upper_window
      mode = config.mode
      for offset in range(lw, uw + 1):
          key = utils.create_event_names_for_offsets(event, offset)
          offset_feature = feature.shift(periods=offset, fill_value=0.0)
          if mode == "additive":
              additive_events[key] = offset_feature
          else:
              multiplicative_events[key] = offset_feature
  
imports: import logging
  from collections import OrderedDict, defaultdict
  from datetime import datetime
  from typing import Optional
  import numpy
  import pandas
  import torch
  from torch.utils.data.dataset import Dataset
  from neuralprophet import configure, utils
  from neuralprophet.df_utils import get_max_num_lags
  from neuralprophet.hdays_utils import get_country_holidays
label_desc: Use image node_id 5 for calling a global function with example usage: _create_event_offset_features(event, config, feature, additive_events, multiplicative_events) without return types