class_docstr: This class implements the :class:`torch_geometric.data.GraphStore`
  interface to act as a local graph store for distributed training.
code: def __init__(self):
      super().__init__()
      self._edge_index: Dict[Tuple, EdgeTensorType] = {}
      self._edge_attr: Dict[Tuple, EdgeAttr] = {}
      self._edge_id: Dict[Tuple, Tensor] = {}
  
      self.num_partitions = 1
      self.partition_idx = 0
      # Mapping between node ID and partition ID
      self.node_pb: Union[Tensor, Dict[NodeType, Tensor]] = None
      # Mapping between edge ID and partition ID
      self.edge_pb: Union[Tensor, Dict[EdgeType, Tensor]] = None
      # Meta information related to partition and graph store info
      self.meta: Optional[Dict[Any, Any]] = None
      # If data is sorted based on destination nodes (CSC format):
      self.is_sorted: Optional[bool] = None
  
imports: import json
  import os.path
  from typing import Any, Dict, List, Optional, Tuple, Union
  import torch
  from torch import Tensor
  from torch_geometric.data import EdgeAttr, GraphStore
  from torch_geometric.typing import EdgeTensorType, EdgeType, NodeType
  from torch_geometric.utils import sort_edge_index
label_desc: Use image node_id 1 to create a new LocalGraphStore object from inherited base classes: GraphStore with example: obj = LocalGraphStore()