code: def __getitem__(self, index: int) -> Any:
      data_1 = (
          self._cache[index]
          if self._cache.filled
          else self._dataset[index]
      )
      if not self._cache.filled:
          if not self._is_deterministic:
              data2 = self._dataset[index]
              if not _equal_items(data_1, data2):
                  raise ValueError(
                      f"Your dataset items aren't deterministic. Found {data_1} and {data2} for index {index}."
                      " HINT: Use the `lightning.data.cache.Cache` directly within your dataset."
                  )
              self._is_deterministic = True
          self._cache[index] = data_1
      return data_1
  
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 3 for calling the CacheDataset obj's underlying member method code with example usage: obj.__getitem__(index) and returns: data_1