class_docstr: Defines the attributes of a :class:`FeatureStore` tensor.
  It holds all the parameters necessary to uniquely identify a tensor from
  the :class:`FeatureStore`.
  
  Note that the order of the attributes is important; this is the order in
  which attributes must be provided for indexing calls. :class:`FeatureStore`
  implementations can define a different ordering by overriding
  :meth:`TensorAttr.__init__`.
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docstr: Updates an :class:`TensorAttr` with set attributes from another
  :class:`TensorAttr`.
docstr_tok: ["Updates","an",":","class",":","`","TensorAttr","`","with","set","attributes","from","another",":","class",":","`","TensorAttr","`","."]
code: def update(self, attr: "TensorAttr") -> "TensorAttr":
      r"""Updates an :class:`TensorAttr` with set attributes from another
      :class:`TensorAttr`.
      """
      for key in self.__dataclass_fields__:
          if attr.is_set(key):
              setattr(self, key, getattr(attr, key))
      return self
  
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 4 for calling the TensorAttr obj's underlying member method code with example usage: obj.update(attr) and returns: self