code: def get_dataset(args):
      if args.model_type == MODEL_TYPE_LIBRISPEECH:
          return torch.utils.data.ConcatDataset(
              [
                  torchaudio.datasets.LIBRISPEECH(
                      args.dataset_path, url="train-clean-360"
                  ),
                  torchaudio.datasets.LIBRISPEECH(
                      args.dataset_path, url="train-clean-100"
                  ),
                  torchaudio.datasets.LIBRISPEECH(
                      args.dataset_path, url="train-other-500"
                  ),
              ]
          )
      elif args.model_type == MODEL_TYPE_TEDLIUM3:
          return torchaudio.datasets.TEDLIUM(
              args.dataset_path, release="release3", subset="train"
          )
      elif args.model_type == MODEL_TYPE_MUSTC:
          return MUSTC(args.dataset_path, subset="train")
      else:
          raise ValueError(
              f"Encountered unsupported model type {args.model_type}."
          )
  
file_path: audio/examples/asr/emformer_rnnt/global_stats.py
imports: import json
  import logging
  import pathlib
  from argparse import ArgumentParser, RawTextHelpFormatter
  import torch
  import torchaudio
  from common import MODEL_TYPE_LIBRISPEECH, MODEL_TYPE_MUSTC, MODEL_TYPE_TEDLIUM3, piecewise_linear_log, spectrogram_transform
  from mustc.dataset import MUSTC
label_desc: Use image node_id 3 for calling a global function with example usage: get_dataset(args) and returns: torch, torchaudio, MUSTC