code: def create_dict_for_events_or_regressors(
      df: pd.DataFrame,
      other_df: Optional[pd.DataFrame],
      other_df_name: str,
  ) -> dict:  # Not sure about the naming of this function
      """Create a dict for events or regressors according to input df.
  
      Parameters
      ----------
          df : pd.DataFrame
              Dataframe with columns ``ds`` datestamps and ``y`` time series values
          other_df : pd.DataFrame
              Dataframe with events or regressors
          other_df_name : str
              Definition of other_df (i.e. 'events', 'regressors')
  
      Returns
      -------
          dict
              dictionary with events or regressors
      """
      df_names = list(df["ID"])
      if other_df is None:
          # if other_df is None, create dictionary with None for each ID
          return {df_name: None for df_name in df_names}
  
      other_df, received_ID_col, _, _ = prep_or_copy_df(other_df)
      # if other_df does not contain ID, create dictionary with original ID with the same other_df for each ID
      if not received_ID_col:
          other_df = other_df.drop("ID", axis=1)
          return {
              df_name: other_df.copy(deep=True) for df_name in df_names
          }
  
      # else, other_df does contain ID, create dict with respective IDs
      df_unique_names, other_df_unique_names = list(
          df["ID"].unique()
      ), list(other_df["ID"].unique())
      missing_names = [
          name
          for name in other_df_unique_names
          if name not in df_unique_names
      ]
  
      # check if other_df contains ID which does not exist in original df
      if len(missing_names) > 0:
          raise ValueError(
              f"ID(s) {missing_names} from {other_df_name} df is not valid - missing from original df ID column"
          )
  
      # create dict with existent IDs (non-referred IDs will be set to None in dict)
      df_other_dict = {}
      for df_name in df_unique_names:
          if df_name in other_df_unique_names:
              df_aux = (
                  other_df[other_df["ID"] == df_name]
                  .reset_index(drop=True)
                  .copy(deep=True)
              )
              df_aux.drop("ID", axis=1, inplace=True)
          else:
              df_aux = None
          df_other_dict[df_name] = df_aux
      log.debug(f"Original df and {other_df_name} df are compatible")
      return df_other_dict
  
imports: from __future__ import annotations
  import logging
  import math
  from collections import OrderedDict
  from dataclasses import dataclass
  from typing import TYPE_CHECKING, List, Optional, Tuple
  import numpy
  import pandas
label_desc: Use image node_id 32 for calling a global function with example usage: create_dict_for_events_or_regressors(df, other_df, other_df_name) and returns: df_other_dict, unknown, unknown