code: def test_empty_prefetch_iterator(dataset_cls, prefetch_batches):
      loader = CombinedLoader(DataLoader(dataset_cls()))
      fetcher = _PrefetchDataFetcher(prefetch_batches=prefetch_batches)
      fetcher.setup(loader)
      iter(fetcher)
  
      if dataset_cls is EmptySizedDataset:
          assert (
              fetcher.done
          )  # for 0 length sized datasets we know we're done already
      else:
          # if we're prefetching, we can know in advance if the dataset is empty
          assert fetcher.done == (prefetch_batches > 0)
      assert not list(fetcher)
      assert fetcher.done
  
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 3 for calling a global function with example usage: test_empty_prefetch_iterator(dataset_cls, prefetch_batches) without return types