code: def do_compare_training(
      args: argparse.Namespace,
      story_file: Text,
      additional_arguments: Optional[Dict] = None,
  ) -> None:
      """Train multiple models for comparison of policies and dumps the result."""
      train_comparison_models(
          story_file=story_file,
          domain=args.domain,
          output_path=args.out,
          exclusion_percentages=args.percentages,
          policy_configs=args.config,
          runs=args.runs,
          additional_arguments=additional_arguments,
      )
      no_stories = get_no_of_stories(args.stories, args.domain)
  
      # store the list of the number of stories present at each exclusion
      # percentage
      story_range = [
          no_stories - round((x / 100.0) * no_stories)
          for x in args.percentages
      ]
  
      training_stories_per_model_file = os.path.join(
          args.out, NUMBER_OF_TRAINING_STORIES_FILE
      )
      rasa.shared.utils.io.dump_obj_as_json_to_file(
          training_stories_per_model_file, story_range
      )
  
file_path: rasa/rasa/core/train.py
imports: import argparse
  import logging
  import os
  from pathlib import Path
  from typing import Dict, Optional, Text, List
  import rasa.shared.utils.io
  import rasa.utils.io
  from rasa.constants import NUMBER_OF_TRAINING_STORIES_FILE, PERCENTAGE_KEY
  from rasa.shared.importers.importer import TrainingDataImporter
label_desc: Use image node_id 3 for calling a global function with example usage: do_compare_training(args, story_file, additional_arguments) without return types