code: def test_dataset_random_sized_files(self) -> None:
      random.seed(0)
      self._test_dataset(
          [random.randint(1, 100) for _ in range(100)], 16, 3
      )
      # Test case where the global batch size does not evenly divide
      # dataset_len but the local batch size does.
      self._test_dataset([352], batch_size=32, world_size=8)
  
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 5 for calling the TestInMemoryBinaryCriteoIterDataPipe obj's underlying member method code with example usage: obj.test_dataset_random_sized_files() without return types