class_docstr: This class implements the :class:`torch_geometric.data.GraphStore`
  interface to act as a local graph store for distributed training.
code: def from_partition(cls, root: str, pid: int) -> "LocalGraphStore":
      with open(osp.join(root, "META.json"), "r") as f:
          meta = json.load(f)
  
      part_dir = osp.join(root, f"part_{pid}")
      assert osp.exists(part_dir)
  
      graph_data = torch.load(osp.join(part_dir, "graph.pt"))
      graph_store = cls()
      graph_store.is_sorted = meta["is_sorted"]
  
      if not meta["is_hetero"]:
          edge_index = torch.stack(
              (graph_data["row"], graph_data["col"]), dim=0
          )
          edge_id = graph_data["edge_id"]
          if not graph_store.is_sorted:
              edge_index, edge_id = sort_edge_index(
                  edge_index, edge_id, sort_by_row=False
              )
  
          attr = dict(
              edge_type=None,
              layout="coo",
              size=graph_data["size"],
              is_sorted=True,
          )
          graph_store.put_edge_index(edge_index, **attr)
          graph_store.put_edge_id(edge_id, **attr)
  
      if meta["is_hetero"]:
          for edge_type, data in graph_data.items():
              attr = dict(
                  edge_type=edge_type,
                  layout="coo",
                  size=data["size"],
                  is_sorted=True,
              )
              edge_index = torch.stack(
                  (data["row"], data["col"]), dim=0
              )
              edge_id = data["edge_id"]
  
              if not graph_store.is_sorted:
                  edge_index, edge_id = sort_edge_index(
                      edge_index, edge_id, sort_by_row=False
                  )
              graph_store.put_edge_index(edge_index, **attr)
              graph_store.put_edge_id(edge_id, **attr)
  
      return graph_store
  
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 14 for calling the LocalGraphStore obj's underlying member method code with example usage: obj.from_partition(cls, root, pid) and returns: graph_store