code: def test_stop_iteration_with_dataloader_iter(
      trigger_stop_iteration, tmpdir
  ):
      """Verify that StopIteration properly terminates the training when this is triggered from the current
      `dataloader_iter`"""
      EXPECT_NUM_BATCHES_PROCESSED = 2
  
      class TestModel(AsyncBoringModel):
          def __init__(self, trigger_stop_iteration) -> None:
              super().__init__()
              self.trigger_stop_iteration = trigger_stop_iteration
  
          def training_step(
              self, dataloader_iter: Iterator
          ) -> STEP_OUTPUT:
              output = super().training_step(dataloader_iter)
              batch_idx = self.trainer.fit_loop.epoch_loop.batch_idx
              if (
                  self.trigger_stop_iteration
                  and batch_idx == EXPECT_NUM_BATCHES_PROCESSED
              ):
                  raise StopIteration
              return output
  
          def train_dataloader(self):
              if self.trigger_stop_iteration:
                  return DataLoader(
                      RandomDataset(
                          BATCH_SIZE, 2 * EXPECT_NUM_BATCHES_PROCESSED
                      )
                  )
              return DataLoader(
                  RandomDataset(
                      BATCH_SIZE, EXPECT_NUM_BATCHES_PROCESSED
                  )
              )
  
      trainer = Trainer(
          max_epochs=1, default_root_dir=tmpdir, accelerator="cpu"
      )
      m = TestModel(trigger_stop_iteration)
      trainer.fit(m)
      expected = EXPECT_NUM_BATCHES_PROCESSED
      if trigger_stop_iteration:
          expected *= 2
      assert m.num_batches_processed == expected
  
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 9 for calling a global function with example usage: test_stop_iteration_with_dataloader_iter(trigger_stop_iteration, tmpdir) and returns: output, DataLoader, DataLoader