class_docstr: Wrap the PyTorch DataLoader WorkerLoop to perform caching and profiling.
class_docstr_tok: ["Wrap","the","PyTorch","DataLoader","WorkerLoop","to","perform","caching","and","profiling","."]
code: def __init__(self, global_rank: int, profile: bool = False) -> None:
      self._global_rank = global_rank
      self._profile = profile
  
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 WorkerLoop object  with example: obj = WorkerLoop(global_rank, profile)