code: def get_train_app(
      self,
      root_dir: str,
      precise_bn_num_batches: int = 0,
      fast_dev_run: bool = True,
      logger: bool = False,
  ) -> BaseTrainApp:
      create_small_kinetics_dataset(root_dir)
      overrides = [
          f"datamodule.dataloader.train.dataset.data_path={root_dir}/train.csv",
          f"datamodule.dataloader.val.dataset.data_path={root_dir}/val.csv",
          f"datamodule.dataloader.test.dataset.data_path={root_dir}/val.csv",
          f"datamodule.dataloader.train.dataset.video_path_prefix={root_dir}",
          f"datamodule.dataloader.val.dataset.video_path_prefix={root_dir}",
          f"datamodule.dataloader.test.dataset.video_path_prefix={root_dir}",
          "datamodule.dataloader.train.num_workers=0",
          "datamodule.dataloader.val.num_workers=0",
          "datamodule.dataloader.test.num_workers=0",
          "datamodule.dataloader.train.batch_size=2",
          "datamodule.dataloader.val.batch_size=2",
          "datamodule.dataloader.test.batch_size=2",
          "+module/lr_scheduler=cosine_with_warmup",
          "trainer.logger=false",
      ]
      if precise_bn_num_batches > 0:
          overrides.extend(
              [
                  "+callbacks=precise_bn",
                  f"callbacks.precise_bn.num_batches={precise_bn_num_batches}",
                  "datamodule.dataloader.train.batch_size=2",
                  "datamodule.dataloader.val.batch_size=2",
                  "datamodule.dataloader.test.batch_size=2",
              ]
          )
      app = self.create_app_from_hydra(
          config_module="pytorchvideo_trainer.conf",
          config_name="video_classification_train_app_conf",
          overrides=overrides,
      )
      trainer_overrides = {
          "fast_dev_run": fast_dev_run,
          "logger": logger,
      }
      self.mock_trainer_params(app, trainer_overrides)
      return app
  
file_path: pytorchvideo/pytorchvideo_trainer/tests/test_task_video_classification.py
imports: from torchrecipes.core.base_train_app import BaseTrainApp
  from util import BaseTrainAppTestCase, create_small_kinetics_dataset, run_locally, tempdir
label_desc: Use image node_id 1 for calling the TestVideoClassificationTrainApp obj's underlying member method code with example usage: obj.get_train_app(root_dir, precise_bn_num_batches, fast_dev_run, logger) and returns: app