code: def test_fetching_dataloader_iter_opt(automatic_optimization, tmpdir):
      class TestModel(BoringModel):
          def __init__(
              self,
              *args,
              automatic_optimization: bool = False,
              **kwargs,
          ):
              super().__init__(*args, **kwargs)
              self.automatic_optimization = automatic_optimization
              self.count = 0
              self.batches = []
  
          def training_step(self, dataloader_iter):
              assert isinstance(
                  self.trainer.fit_loop._data_fetcher,
                  _DataLoaderIterDataFetcher,
              )
              # fetch 2 batches
              batch, batch_idx, _ = next(dataloader_iter)
              self.batches.append(batch)
              batch, batch_idx, _ = next(dataloader_iter)
              self.batches.append(batch)
  
              batch = self.batches.pop(0)
              assert isinstance(batch, Tensor) or batch is None
              self.count = batch_idx + 1
              if self.automatic_optimization:
                  loss = super().training_step(batch, 0)
                  with pytest.raises(
                      MisconfigurationException, match="dataloader_iter"
                  ):
                      self.log("train_loss", loss["loss"])
                  self.log("train_loss", loss["loss"], batch_size=1)
              else:
                  opt = self.optimizers()
                  loss = self.step(batch)
                  opt.zero_grad()
                  loss.backward()
                  opt.step()
  
          def on_train_epoch_end(self):
              # since the dataset is sized, the loop stops at the limit even though the training_step controls the
              # consumption of batches
              assert (
                  self.trainer.fit_loop.epoch_loop.batch_progress.current.ready
                  == 32
              )
              assert self.trainer.fit_loop._data_fetcher.fetched == 64
              assert self.count == 64
  
      model = TestModel(automatic_optimization=automatic_optimization)
      trainer = Trainer(
          default_root_dir=tmpdir, max_epochs=1, accelerator="cpu"
      )
      trainer.fit(model)
  
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 5 for calling a global function with example usage: test_fetching_dataloader_iter_opt(automatic_optimization, tmpdir) without return types