class_docstr: This class implements the :class:`torch_geometric.data.FeatureStore`
  interface to act as a local feature store for distributed training.
code: def has_edge_attr(self) -> bool:
      has_edge_attr = False
      for k in [key for key in self._feat.keys() if "edge_attr" in key]:
          try:
              self.get_tensor(k[0], "edge_attr")
              has_edge_attr = True
          except KeyError:
              pass
      return has_edge_attr
  
imports: import copy
  import json
  import os.path
  from dataclasses import dataclass
  from typing import Any, Dict, List, Optional, Tuple, Union
  import torch
  from torch import Tensor
  from torch_geometric.data import FeatureStore, TensorAttr
  from torch_geometric.data.feature_store import _FieldStatus
  from torch_geometric.distributed.rpc import RPCCallBase, RPCRouter, rpc_async, rpc_register
  from torch_geometric.typing import EdgeType, NodeOrEdgeType, NodeType
label_desc: Use image node_id 14 for calling the LocalFeatureStore obj's underlying member method code with example usage: obj.has_edge_attr() and returns: has_edge_attr