code: def run_eval(model, model_type):
      if model_type == MODEL_TYPE_LIBRISPEECH:
          dataloader = model.test_dataloader()
          run_eval_subset(model, dataloader, "test")
      elif model_type == MODEL_TYPE_TEDLIUM3:
          dev_loader = model.dev_dataloader()
          test_loader = model.test_dataloader()
          run_eval_subset(model, dev_loader, "dev")
          run_eval_subset(model, test_loader, "test")
      elif model_type == MODEL_TYPE_MUSTC:
          dev_loader = model.dev_dataloader()
          test_common_loader = model.test_common_dataloader()
          test_he_loader = model.test_he_dataloader()
          run_eval_subset(model, dev_loader, "dev")
          run_eval_subset(model, test_common_loader, "tst-COMMON")
          run_eval_subset(model, test_he_loader, "tst-HE")
      else:
          raise ValueError(
              f"Encountered unsupported model type {model_type}."
          )
  
file_path: audio/examples/asr/emformer_rnnt/eval.py
imports: 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
  from librispeech.lightning import LibriSpeechRNNTModule
  from mustc.lightning import MuSTCRNNTModule
  from tedlium3.lightning import TEDLIUM3RNNTModule
label_desc: Use image node_id 3 for calling a global function with example usage: run_eval(model, model_type) without return types