class_docstr: The streaming dataset can be used once your data have been optimised using the DatasetOptimiser class.
class_docstr_tok: ["The","streaming","dataset","can","be","used","once","your","data","have","been","optimised","using","the","DatasetOptimiser","class","."]
code: def __getitem__(self, index: Union[ChunkedIndex, int]) -> Any:
      if self.cache is None:
          self.worker_env = _WorkerEnv.detect()
          self.cache = self._create_cache(worker_env=self.worker_env)
          self.shuffler = self._create_shuffler(self.cache)
      if isinstance(index, int):
          index = ChunkedIndex(
              index, self.cache._get_chunk_index_from_index(index)
          )
      return self.cache[index]
  
imports: import hashlib
  import os
  from dataclasses import dataclass
  from time import time
  from typing import Any, Dict, List, Optional, Tuple, Union
  import numpy
  from torch.utils.data import IterableDataset, get_worker_info
  from lightning.data.streaming import Cache
  from lightning.data.streaming.constants import _DEFAULT_CACHE_DIR, _INDEX_FILENAME, _LIGHTNING_CLOUD_LATEST
  from lightning.data.streaming.item_loader import BaseItemLoader
  from lightning.data.streaming.sampler import ChunkedIndex
  from lightning.data.streaming.serializers import Serializer
  from lightning.data.streaming.shuffle import FullShuffle, NoShuffle, Shuffle
  from lightning.data.utilities.env import Environment, _DistributedEnv, _WorkerEnv
label_desc: Use image node_id 7 for calling the StreamingDataset obj's underlying member method code with example usage: obj.__getitem__(index) and returns: self