code: def _check_box_overlap(
      box0: ChunkStorageMetadata, box1: ChunkStorageMetadata
  ) -> bool:
      """Check if two boxes overlap. Tuples are (offset, lengths)."""
      # For each dim of each shard, check if one shard resides on the other
      # end of second shard with respect to that dim. As an example for a 2D
      # shard, we would check if one shard is above or on the left of the
      # other shard.
      ndims = len(box0.offsets)
      for i in range(ndims):
          if box0.offsets[i] >= box1.offsets[i] + box1.sizes[i]:
              return False
          if box1.offsets[i] >= box0.offsets[i] + box0.sizes[i]:
              return False
  
      return True
  
imports: import dataclasses
  import io
  import logging
  import operator
  from collections import ChainMap
  from functools import reduce
  from typing import Any, cast, Dict, List, Tuple, Union
  import torch
  from torch.distributed._shard._utils import narrow_tensor_by_index
  from torch.distributed._tensor import DTensor
  from torch.distributed.checkpoint._dedup_tensors import dedup_tensors
  from torch.distributed.checkpoint._nested_dict import FLATTEN_MAPPING, flatten_state_dict
  from torch.distributed.checkpoint._sharded_tensor_utils import _flatten_sharded_tensors
  from torch.distributed.checkpoint._traverse import set_element
  from torch.distributed.checkpoint.metadata import BytesStorageMetadata, ChunkStorageMetadata, Metadata, MetadataIndex, STATE_DICT_TYPE, STORAGE_TYPES, TensorStorageMetadata
  from torch.distributed.checkpoint.planner import LoadPlan, LoadPlanner, ReadItem, SavePlan, SavePlanner, WriteItem, WriteItemType
  from torch.distributed.checkpoint.planner_helpers import _create_default_metadata_only_plan, _create_read_items, _create_write_items, _init_state_dict
  from torch.distributed.checkpoint.utils import find_state_dict_object
label_desc: Use image node_id 6 for calling a global function with example usage: _check_box_overlap(box0, box1) and returns: True, False, False