class_docstr: An abstract base class to access features from a remote feature store.
  
  Args:
      tensor_attr_cls (TensorAttr, optional): A user-defined
          :class:`TensorAttr` class to customize the required attributes and
          their ordering to unique identify tensor values.
          (default: :obj:`None`)
class_docstr_tok: ["An","abstract","base","class","to","access","features","from","a","remote","feature","store",".","Args",":","tensor_attr_cls","(","TensorAttr",",","optional",")",":","A","user-defined",":","class",":","`","TensorAttr","`","class","to","customize","the","required","attributes","and","their","ordering","to","unique","identify","tensor","values",".","(","default",":",":","obj",":","`","None","`",")"]
code: def __repr__(self) -> str:
      return f"{self.__class__.__name__}()"
  
imports: import copy
  from abc import abstractmethod
  from dataclasses import dataclass
  from enum import Enum
  from typing import Any, List, Optional, Tuple, Union
  import numpy
  import torch
  from torch_geometric.typing import FeatureTensorType, NodeType
  from torch_geometric.utils.mixin import CastMixin
label_desc: Use image node_id 22 for calling the FeatureStore obj's underlying member method code with example usage: obj.__repr__() and returns: self, str