code: def print_train_or_instructions(args: argparse.Namespace) -> None:
      """Train a model if the user wants to."""
      import questionary
      import rasa
  
      print_success("Finished creating project structure.")
  
      should_train = (
          questionary.confirm(
              "Do you want to train an initial model? 💪🏽"
          )
          .skip_if(args.no_prompt, default=True)
          .ask()
      )
  
      if should_train:
          print_success("Training an initial model...")
          training_result = rasa.train(
              DEFAULT_DOMAIN_PATH,
              DEFAULT_CONFIG_PATH,
              DEFAULT_DATA_PATH,
              DEFAULT_MODELS_PATH,
          )
          args.model = training_result.model
  
          print_run_or_instructions(args)
  
      else:
          print_success(
              "No problem 👍🏼. You can also train a model later by going "
              "to the project directory and running 'rasa train'."
          )
  
file_path: rasa/rasa/cli/scaffold.py
imports: import argparse
  import os
  import sys
  from typing import List, Text
  from rasa import telemetry
  from rasa.cli import SubParsersAction
  from rasa.cli.shell import shell
  from rasa.shared.utils.cli import print_success, print_error_and_exit
  from rasa.shared.constants import DOCS_BASE_URL, DEFAULT_CONFIG_PATH, DEFAULT_DOMAIN_PATH, DEFAULT_DATA_PATH, DEFAULT_MODELS_PATH
label_desc: Use image node_id 2 for calling a global function with example usage: print_train_or_instructions(args) without return types