code: def _validate_batch(
      self,
      batch: Batch,
      batch_size: int,
      hashes: Optional[List[int]] = None,
  ) -> None:
      self.assertEqual(
          tuple(batch.dense_features.size()),
          (batch_size, INT_FEATURE_COUNT),
      )
      self.assertEqual(
          tuple(batch.sparse_features.values().size()),
          (batch_size * CAT_FEATURE_COUNT,),
      )
      self.assertEqual(tuple(batch.labels.size()), (batch_size,))
      if hashes is not None:
          hashes_np = np.array(hashes).reshape((CAT_FEATURE_COUNT, 1))
          self.assertTrue(
              np.all(
                  batch.sparse_features.values().reshape(
                      (CAT_FEATURE_COUNT, batch_size)
                  )
                  < hashes_np
              )
          )
  
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 TestInMemoryBinaryCriteoIterDataPipe obj's underlying member method code with example usage: obj._validate_batch(batch, batch_size, hashes) without return types