class_docstr: The `StreamingDataLoader` keeps track of the number of samples fetched in order to enable resumability of the
  dataset.
class_docstr_tok: ["The","`","StreamingDataLoader","`","keeps","track","of","the","number","of","samples","fetched","in","order","to","enable","resumability","of","the","dataset","."]
code: def __init__(
      self,
      dataset: Union[StreamingDataset, CombinedStreamingDataset],
      *args: Any,
      batch_size: int = 1,
      num_workers: int = 0,
      **kwargs: Any,
  ) -> None:  # pyright: ignore
      self.batch_size = batch_size
      self.num_workers = num_workers
      self.num_samples_yielded = 0
      super().__init__(
          dataset,
          *args,
          batch_size=batch_size,
          num_workers=num_workers,
          **kwargs,
      )
  
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 StreamingDataLoader object from inherited base classes: DataLoader with example: obj = StreamingDataLoader(dataset)