code: def _validate_dataloader_sample(
      self,
      sample: Dict[str, List[Any]],  # pyre-ignore[2]
      batch_size: int,
      train: bool = True,
  ) -> None:
      unbatched_samples = [{} for _ in range(self._sample_len(sample))]
      for k, batched_values in sample.items():
          for idx, value in enumerate(batched_values):
              unbatched_samples[idx][k] = value
      for sample in unbatched_samples:
          self._validate_sample(sample, train=train)
  
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 1 for calling the CriteoDataLoaderTest obj's underlying member method code with example usage: obj._validate_dataloader_sample(sample, batch_size, train) without return types