code: def test_fetching_is_profiled():
      """Test that fetching is profiled."""
  
      class MyModel(BoringModel):
          def validation_step(self, batch, batch_idx, dataloader_idx=0):
              return super().validation_step(batch, batch_idx)
  
          def val_dataloader(self):
              return [
                  super().val_dataloader(),
                  super().val_dataloader(),
              ]
  
      model = MyModel()
      fast_dev_run = 2
      trainer = Trainer(
          fast_dev_run=fast_dev_run,
          profiler="simple",
          enable_model_summary=False,
          enable_checkpointing=False,
          enable_progress_bar=False,
          logger=False,
          accelerator="cpu",
      )
      trainer.fit(model)
      trainer.test(model)
      trainer.predict(model)
  
      profiler = trainer.profiler
      assert isinstance(profiler, SimpleProfiler)
  
      # validation
      key = "[_EvaluationLoop].val_next"
      assert key in profiler.recorded_durations
      durations = profiler.recorded_durations[key]
      assert len(durations) == 2 * fast_dev_run
      assert all(d > 0 for d in durations)
      # training
      key = "[_TrainingEpochLoop].train_dataloader_next"
      assert key in profiler.recorded_durations
      durations = profiler.recorded_durations[key]
      assert len(durations) == fast_dev_run
      assert all(d > 0 for d in durations)
      # test
      key = "[_EvaluationLoop].test_next"
      assert key in profiler.recorded_durations
      durations = profiler.recorded_durations[key]
      assert len(durations) == fast_dev_run
      assert all(d > 0 for d in durations)
      # predict
      key = "[_PredictionLoop].predict_next"
      assert key in profiler.recorded_durations
      durations = profiler.recorded_durations[key]
      assert len(durations) == fast_dev_run
      assert all(d > 0 for d in durations)
  
      # now test profiling when the dataloader_iter is polled manually
      class MyModel(BoringModel):
          def training_step(self, dataloader_iter):
              _ = next(dataloader_iter)
              batch, _, _ = next(dataloader_iter)
              return super().training_step(batch, 0)
  
      model = MyModel()
      trainer = Trainer(
          fast_dev_run=2,
          profiler="simple",
          limit_val_batches=0,
          enable_model_summary=False,
          enable_checkpointing=False,
          enable_progress_bar=False,
          logger=False,
          accelerator="cpu",
      )
      trainer.fit(model)
  
      profiler = trainer.profiler
      assert isinstance(profiler, SimpleProfiler)
  
      key = "[_TrainingEpochLoop].train_dataloader_next"
      assert key in profiler.recorded_durations
      durations = profiler.recorded_durations[key]
      assert len(durations) == 2  # 2 polls in training_step
      assert all(d > 0 for d in durations)
  
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 11 for calling a global function with example usage: test_fetching_is_profiled() and returns: super, list, super