code: def test_step_methods_with_dataloader_iter(
      limit_sanity_val_batches,
      limit_train_batches,
      limit_eval_batches,
      tmp_path,
  ):
      global_batch_size = 4
      micro_batch_size = 2
      fetches_per_step = global_batch_size // micro_batch_size
      data = DataLoader(
          RandomDataset(32, length=16), batch_size=micro_batch_size
      )
      assert len(data) == 8
  
      limit_sanity_val_batches = (
          2
          if limit_sanity_val_batches is None
          else limit_sanity_val_batches
      )
      limit_train_batches = limit_train_batches
      limit_val_batches = limit_eval_batches
      limit_test_batches = limit_eval_batches
      limit_predict_batches = limit_eval_batches
      model = DataLoaderIterMonitorModel(fetches_per_step)
      trainer = Trainer(
          default_root_dir=tmp_path,
          limit_train_batches=limit_train_batches,
          limit_val_batches=limit_val_batches,
          limit_test_batches=limit_test_batches,
          limit_predict_batches=limit_predict_batches,
          num_sanity_val_steps=limit_sanity_val_batches,
          max_epochs=1,
          accelerator="cpu",
          logger=False,
          enable_checkpointing=False,
          enable_progress_bar=False,
          enable_model_summary=False,
      )
      trainer.fit(model, data, data)
  
      def length(iterable, limit):
          return (
              len(iterable) if limit is None else min(limit, len(data))
          )
  
      assert (
          model.record["sanity_validation"]["entered"]
          == length(data, limit_sanity_val_batches) // fetches_per_step
      )
      assert model.record["sanity_validation"]["fetched"] == length(
          data, limit_sanity_val_batches
      )
      assert model.record["sanity_validation"]["raised"] == 0
      assert (
          model.record["training"]["entered"]
          == length(data, limit_train_batches) // fetches_per_step
      )
      assert model.record["training"]["fetched"] == length(
          data, limit_train_batches
      )
      assert model.record["training"]["raised"] == 0
      assert (
          model.record["validation"]["entered"]
          == length(data, limit_eval_batches) // fetches_per_step
      )
      assert model.record["validation"]["fetched"] == length(
          data, limit_eval_batches
      )
      assert model.record["validation"]["raised"] == 0
  
      model = DataLoaderIterMonitorModel(fetches_per_step)
      trainer.validate(model, data)
      assert (
          model.record["validation"]["entered"]
          == length(data, limit_eval_batches) // fetches_per_step
      )
      assert model.record["validation"]["fetched"] == length(
          data, limit_eval_batches
      )
      assert model.record["validation"]["raised"] == 0
  
      model = DataLoaderIterMonitorModel(fetches_per_step)
      trainer.test(model, data)
      assert (
          model.record["test"]["entered"]
          == length(data, limit_eval_batches) // fetches_per_step
      )
      assert model.record["test"]["fetched"] == length(
          data, limit_eval_batches
      )
      assert model.record["test"]["raised"] == 0
  
      model = DataLoaderIterMonitorModel(fetches_per_step)
      trainer.predict(model, data)
      assert (
          model.record["predict"]["entered"]
          == length(data, limit_eval_batches) // fetches_per_step
      )
      assert model.record["predict"]["fetched"] == length(
          data, limit_eval_batches
      )
      assert model.record["predict"]["raised"] == 0
  
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 8 for calling a global function with example usage: test_step_methods_with_dataloader_iter(limit_sanity_val_batches, limit_train_batches, limit_eval_batches, tmp_path) and returns: unknown