code: def _equal_items(data_1: Any, data_2: Any) -> bool:
      data_1_flattened, _ = tree_flatten(data_1)
      data_2_flattened, _ = tree_flatten(data_2)
  
      if len(data_1_flattened) != len(data_2_flattened):
          return False
  
      return all(
          _equal_item(d1, d2)
          for d1, d2 in zip(data_1_flattened, data_2_flattened)
      )
  
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 for calling a global function with example usage: _equal_items(data_1, data_2) and returns: all, False