code: def _test_dataset(
      self,
      rows_per_file: List[int],
      batch_size: int,
      world_size: int,
      stage: str = "train",
  ) -> None:
      with contextlib.ExitStack() as stack:
          num_rows = sum(rows_per_file)
          if stage == "train":
              dense, sparse, labels = None, None, None
          else:
              dense = np.mgrid[0:num_rows, 0:INT_FEATURE_COUNT][0]
              sparse = np.mgrid[0:num_rows, 0:CAT_FEATURE_COUNT][0]
              labels = np.ones((num_rows, 1))
          files = [
              stack.enter_context(
                  self._create_dataset_npys(
                      num_rows=num_rows,
                      dense=dense,
                      sparse=sparse,
                      labels=labels,
                  )
              )
              for num_rows in rows_per_file
          ]
          hashes = [i + 1 for i in range(CAT_FEATURE_COUNT)]
  
          if stage == "train":
              dataset_start = 0
              dataset_len = num_rows
          elif stage == "val":
              dataset_start = 0
              dataset_len = num_rows // 2 + num_rows % 2
          else:
              dataset_start = num_rows // 2 + num_rows % 2
              dataset_len = num_rows // 2
  
          lens = []
          remainder = dataset_len % (world_size * batch_size)
          total_samples_count = 0
          for rank in range(world_size):
              end_batch_size = remainder // world_size
              num_batches = math.ceil(
                  dataset_len / (world_size * batch_size)
              )
              if 0 < remainder < world_size:
                  num_batches -= 1
                  end_batch_size = batch_size
              if rank < (remainder % world_size):
                  end_batch_size += 1
              datapipe = InMemoryBinaryCriteoIterDataPipe(
                  stage=stage,
                  dense_paths=[f[0] for f in files],
                  sparse_paths=[f[1] for f in files],
                  labels_paths=[f[2] for f in files],
                  batch_size=batch_size,
                  rank=rank,
                  world_size=world_size,
                  hashes=hashes,
              )
              datapipe_len = len(datapipe)
              self.assertEqual(datapipe_len, num_batches)
  
              len_ = 0
              for batch in datapipe:
                  if (
                      stage in ["val", "test"]
                      and len_ == 0
                      and rank == 0
                  ):
                      self.assertEqual(
                          batch.dense_features[0, 0].item(),
                          dataset_start,
                      )
                  if len_ < num_batches - 1 or end_batch_size == 0:
                      self._validate_batch(batch, batch_size=batch_size)
                  else:
                      self._validate_batch(
                          batch, batch_size=end_batch_size
                      )
                  len_ += 1
                  total_samples_count += batch.dense_features.shape[0]
  
              # Check that dataset __len__ matches true length.
              self.assertEqual(datapipe_len, len_)
              lens.append(len_)
  
          # Ensure all ranks return the same number of batches.
          self.assertEqual(len(set(lens)), 1)
          # Ensure the number of samples read match the number of samples in the dataset
          self.assertEqual(dataset_len, total_samples_count)
  
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 2 for calling the TestInMemoryBinaryCriteoIterDataPipe obj's underlying member method code with example usage: obj._test_dataset(rows_per_file, batch_size, world_size, stage) without return types