code: def create_dist_data(tmp_path: str, rank: int):
      graph_store = LocalGraphStore.from_partition(tmp_path, pid=rank)
      feat_store = LocalFeatureStore.from_partition(tmp_path, pid=rank)
      (
          meta,
          num_partitions,
          partition_idx,
          node_pb,
          edge_pb,
      ) = load_partition_info(tmp_path, rank)
      graph_store.partition_idx = partition_idx
      graph_store.num_partitions = num_partitions
      graph_store.node_pb = node_pb
      graph_store.edge_pb = edge_pb
      graph_store.meta = meta
  
      feat_store.partition_idx = partition_idx
      feat_store.num_partitions = num_partitions
      feat_store.node_feat_pb = node_pb
      feat_store.edge_feat_pb = edge_pb
      feat_store.meta = meta
  
      return feat_store, graph_store
  
file_path: pytorch_geometric/test/distributed/test_dist_neighbor_loader.py
imports: import socket
  import pytest
  import torch
  import torch.multiprocessing
  from torch_geometric.data import Data, HeteroData
  from torch_geometric.datasets import FakeDataset, FakeHeteroDataset
  from torch_geometric.distributed import DistContext, DistNeighborLoader, DistNeighborSampler, LocalFeatureStore, LocalGraphStore, Partitioner
  from torch_geometric.distributed.partition import load_partition_info
  from torch_geometric.testing import onlyLinux, withPackage
label_desc: Use image node_id 1 for calling a global function with example usage: create_dist_data(tmp_path, rank) and returns: feat_store,  graph_store