code: def get_lightning_module(args):
      if args.model_type == MODEL_TYPE_LIBRISPEECH:
          return LibriSpeechRNNTModule(
              librispeech_path=str(args.dataset_path),
              sp_model_path=str(args.sp_model_path),
              global_stats_path=str(args.global_stats_path),
          )
      elif args.model_type == MODEL_TYPE_TEDLIUM3:
          return TEDLIUM3RNNTModule(
              tedlium_path=str(args.dataset_path),
              sp_model_path=str(args.sp_model_path),
              global_stats_path=str(args.global_stats_path),
          )
      elif args.model_type == MODEL_TYPE_MUSTC:
          return MuSTCRNNTModule(
              mustc_path=str(args.dataset_path),
              sp_model_path=str(args.sp_model_path),
              global_stats_path=str(args.global_stats_path),
          )
      else:
          raise ValueError(
              f"Encountered unsupported model type {args.model_type}."
          )
  
file_path: audio/examples/asr/emformer_rnnt/train.py
imports: import logging
  import pathlib
  from argparse import ArgumentParser
  from common import MODEL_TYPE_LIBRISPEECH, MODEL_TYPE_MUSTC, MODEL_TYPE_TEDLIUM3
  from librispeech.lightning import LibriSpeechRNNTModule
  from mustc.lightning import MuSTCRNNTModule
  from pytorch_lightning import Trainer
  from pytorch_lightning.callbacks import ModelCheckpoint
  from tedlium3.lightning import TEDLIUM3RNNTModule
label_desc: Use image node_id 2 for calling a global function with example usage: get_lightning_module(args) and returns: LibriSpeechRNNTModule, TEDLIUM3RNNTModule, MuSTCRNNTModule