code: def test_dataset_train_val_and_test_sets(self) -> None:
      for stage in ["train", "val", "test"]:
          # Test cases where batch_size evenly divides dataset_len.
          self._test_dataset(
              [100], batch_size=1, world_size=2, stage=stage
          )
          self._test_dataset(
              [101], batch_size=1, world_size=2, stage=stage
          )
          # Test cases where the first and only batch is an incomplete batch.
          self._test_dataset(
              [100], batch_size=32, world_size=8, stage=stage
          )
          self._test_dataset(
              [101], batch_size=32, world_size=8, stage=stage
          )
          # Test cases where batches are full size followed by a last batch that is incomplete.
          self._test_dataset(
              [10000], batch_size=128, world_size=8, stage=stage
          )
          self._test_dataset(
              [10001], batch_size=128, world_size=8, stage=stage
          )
  
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 6 for calling the TestInMemoryBinaryCriteoIterDataPipe obj's underlying member method code with example usage: obj.test_dataset_train_val_and_test_sets() without return types