code: def _test_dataloader(
      self,
      num_workers: int = 0,
      batch_size: int = 1,
      num_tsvs: int = 1,
      num_rows_per_tsv: int = 10,
      train: bool = True,
  ) -> None:
      with contextlib.ExitStack() as stack:
          pathnames = [
              stack.enter_context(
                  self._create_dataset_tsv(
                      num_rows=num_rows_per_tsv, train=train
                  )
              )
              for _ in range(num_tsvs)
          ]
          dataset = criteo_terabyte(pathnames)
          dataloader = DataLoader(
              dataset, batch_size=batch_size, num_workers=num_workers
          )
          total_len = 0
          for sample in dataloader:
              sample_len = self._sample_len(sample)
              total_len += sample_len
              self._validate_dataloader_sample(
                  sample, batch_size=batch_size, train=train
              )
          self.assertEqual(total_len, len(list(iter(dataset))))
  
imports: import contextlib
  import math
  import os
  import random
  import tempfile
  from typing import Any, Dict, List, Optional
  import numpy
  from torch.utils.data import DataLoader
  from torchrec.datasets.criteo import BinaryCriteoUtils, CAT_FEATURE_COUNT, criteo_kaggle, criteo_terabyte, InMemoryBinaryCriteoIterDataPipe, INT_FEATURE_COUNT
  from torchrec.datasets.test_utils.criteo_test_utils import CriteoTest
  from torchrec.datasets.utils import Batch
label_desc: Use image node_id 3 for calling the CriteoDataLoaderTest obj's underlying member method code with example usage: obj._test_dataloader(num_workers, batch_size, num_tsvs, num_rows_per_tsv, train) without return types