code: def _equal_item(d1: Any, d2: Any) -> bool:
      if not isinstance(d1, type(d2)):
          return False
      equality = d1 == d2
      if isinstance(equality, torch.Tensor):
          return bool(equality.all().item())
      if equality is True:
          return True
      return False
  
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 2 for calling a global function with example usage: _equal_item(d1, d2) and returns: False, False, bool, True