code: def train_model(opt: Opt) -> Tuple[Dict[str, Any], Dict[str, Any]]:
      """
      Run through a TrainLoop.
  
      If model_file is not in opt, then this helper will create a temporary
      directory to store the model, dict, etc.
  
      :return: (stdout, valid_results, test_results)
      :rtype: (str, dict, dict)
      """
      import parlai.scripts.train_model as tms
  
      with tempdir() as tmpdir:
          if "model_file" not in opt:
              opt["model_file"] = os.path.join(tmpdir, "model")
          if "dict_file" not in opt:
              opt["dict_file"] = os.path.join(tmpdir, "model.dict")
          # Parse verification
          valid, test = tms.TrainModel.main(**opt)
  
      return valid, test
  
imports: import os
  import unittest
  import contextlib
  import tempfile
  import shutil
  import io
  import signal
  from typing import Tuple, Dict, Any
  from parlai.core.opt import Opt
  import parlai.utils.logging
  from parlai.utils.io import PathManager
  from pytest_regressions.data_regression import DataRegressionFixture
label_desc: Use image node_id 21 for calling a global function with example usage: train_model(opt) and returns: valid,  test