code: def __init__(self, fetches_per_step):
      super().__init__()
      self.fetches_per_step = fetches_per_step
      self.record = {
          "training": Counter(),
          "validation": Counter(),
          "sanity_validation": Counter(),
          "test": Counter(),
          "predict": Counter(),
      }
  
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 1 to create a new DataLoaderIterMonitorModel object from inherited base classes: BoringModel with example: obj = DataLoaderIterMonitorModel(fetches_per_step)