code: def test_prefetch_iterator(
      multiple_iterables, dataset_cls, prefetch_batches
  ):
      fetcher = _PrefetchDataFetcher(prefetch_batches=prefetch_batches)
      assert fetcher.prefetch_batches == prefetch_batches
  
      if multiple_iterables:
          loader = CombinedLoader(
              [DataLoader(dataset_cls()), DataLoader(dataset_cls())]
          )
      else:
          loader = CombinedLoader(DataLoader(dataset_cls()))
      fetcher.setup(loader)
  
      def generate():
          generated = [
              (fetcher.fetched, data, fetcher.done)
              for data, batch_idx, dataloader_idx in fetcher
          ]
          assert fetcher.fetched == 3
          assert fetcher.done
          return generated
  
      # we can only know the last batch with sized iterables or when we prefetch
      is_last_batch = [
          False,
          False,
          prefetch_batches > 0 or dataset_cls is SizedDataset,
      ]
      fetched = (
          [1, 2, 3]
          if dataset_cls is SizedDataset
          else [1, 2, 3, 3, 3, 3, 3][
              prefetch_batches : prefetch_batches + 3
          ]
      )
      batches = (
          [[1, 1], [2, 2], [3, 3]] if multiple_iterables else [1, 2, 3]
      )
      expected = list(zip(fetched, batches, is_last_batch))
      assert len(expected) == 3
  
      assert generate() == expected
      # validate reset works properly.
      assert generate() == expected
      assert fetcher.fetched == 3
  
imports: from collections import Counter
  from typing import Any, Iterator
  import pytest
  import torch
  from lightning.pytorch import LightningDataModule, Trainer
  from lightning.pytorch.demos.boring_classes import BoringModel, RandomDataset
  from lightning.pytorch.loops.fetchers import _DataLoaderIterDataFetcher, _PrefetchDataFetcher
  from lightning.pytorch.profilers import SimpleProfiler
  from lightning.pytorch.utilities.combined_loader import CombinedLoader
  from lightning.pytorch.utilities.exceptions import MisconfigurationException
  from lightning.pytorch.utilities.types import STEP_OUTPUT
  from torch import Tensor
  from torch.utils.data import DataLoader, Dataset, IterableDataset
  from tests_pytorch.helpers.runif import RunIf
label_desc: Use image node_id 1 for calling a global function with example usage: test_prefetch_iterator(multiple_iterables, dataset_cls, prefetch_batches) and returns: generated