code: def __init__(self, loader: DataLoader) -> None:
      self._cache = loader._cache
      self._num_workers = loader.num_workers
      # Patch PyTorch worker loop to call the `cache.done()` method.
      from torch.utils.data._utils import worker
  
      worker._worker_loop = WorkerLoop(
          loader._global_rank, loader._profile
      )
      super().__init__(loader)
  
imports: import asyncio
  import inspect
  import logging
  import os
  from importlib import reload
  from typing import Any, Callable, Dict, List, Optional, Union
  import torch
  from torch.utils.data import Dataset, IterableDataset
  from torch.utils.data._utils.collate import default_collate
  from torch.utils.data._utils.fetch import _BaseDatasetFetcher
  from torch.utils.data.dataloader import DataLoader, _BaseDataLoaderIter, _DatasetKind, _MultiProcessingDataLoaderIter, _SingleProcessDataLoaderIter
  from torch.utils.data.sampler import BatchSampler, Sampler
  from lightning.data.streaming import Cache
  from lightning.data.streaming.combined import CombinedStreamingDataset
  from lightning.data.streaming.constants import _DEFAULT_CHUNK_BYTES, _TORCH_GREATER_EQUAL_2_1_0, _VIZ_TRACKER_AVAILABLE
  from lightning.data.streaming.dataset import StreamingDataset
  from lightning.data.streaming.sampler import CacheBatchSampler
  from lightning.data.utilities.env import _DistributedEnv
label_desc: Use image node_id 1 to create a new _MultiProcessingDataLoaderIterPatch object from inherited base classes: _MultiProcessingDataLoaderIter with example: obj = _MultiProcessingDataLoaderIterPatch(loader)