code: def test_in_memory_training_set_shuffle_driver(self) -> None:
      self._test_in_memory_training_set_shuffle(
          [100] * 10, 32, 4, random_seed=0
      )
      self._test_in_memory_training_set_shuffle(
          [100] * 10, 32, 4, random_seed=100
      )
      self._test_in_memory_training_set_shuffle(
          [10000], 128, 8, random_seed=0
      )
      self._test_in_memory_training_set_shuffle(
          [10000], 128, 8, random_seed=100
      )
  
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 7 for calling the TestInMemoryBinaryCriteoIterDataPipe obj's underlying member method code with example usage: obj.test_in_memory_training_set_shuffle_driver() without return types