code: def test_training_step_with_dataloader_iter(tmpdir) -> None:
      """A baseline functional test for `training_step` with dataloader access."""
      trainer = Trainer(
          max_epochs=1, default_root_dir=tmpdir, accelerator="cpu"
      )
      m = AsyncBoringModel()
      trainer.fit(m)
      assert (
          m.num_batches_processed == DATASET_LEN
      ), f"Expect all {DATASET_LEN} batches to be processed."
  
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 7 for calling a global function with example usage: test_training_step_with_dataloader_iter(tmpdir) without return types