code: def train(self, cfg: VideoClassificationTrainAppConf) -> None:
      print(OmegaConf.to_yaml(cfg))
      test_module = hydra.utils.instantiate(
          cfg.module, _recursive_=False
      )
      test_data_module = self.get_datamodule(cfg.datamodule)
      # pyre-fixme[6]: Expected `SupportsKeysAndGetItem[Variable[_KT],
      #  Variable[_VT]]` for 1st param but got `TrainerConf`.
      trainer_params = dict(cfg.trainer)
      trainer_params["logger"] = True
      trainer_params["checkpoint_callback"] = False
      trainer_params["fast_dev_run"] = True
      pl_trainer = Trainer(**trainer_params)
      pl_trainer.fit(model=test_module, datamodule=test_data_module)
  
file_path: pytorchvideo/pytorchvideo_trainer/tests/test_task_module_all.py
imports: import unittest
  from typing import Any
  import hydra
  from hydra import compose, initialize_config_module
  from hydra.utils import instantiate
  from omegaconf import OmegaConf
  from pytorch_lightning import Trainer
  from pytorchvideo_trainer.datamodule.datamodule import VideoClassificationDataModuleConf
  from pytorchvideo_trainer.train_app import VideoClassificationTrainAppConf
  from util import create_small_kinetics_dataset, run_locally, tempdir
label_desc: Use image node_id 2 for calling the TestMain obj's underlying member method code with example usage: obj.train(cfg) without return types