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`)
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docstr: Updates a :obj:`tensor` in the :class:`FeatureStore` with a new
  value. Returns whether the update was succesful.
  
  .. note::
      Implementor classes can choose to define more efficient update
      methods; the default performs a removal and insertion.
  
  Args:
      tensor (torch.Tensor or np.ndarray): The feature tensor to be
          updated.
      *args: Arguments passed to :class:`TensorAttr`.
      **kwargs: Keyword arguments passed to :class:`TensorAttr`.
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code: def update_tensor(
      self, tensor: FeatureTensorType, *args, **kwargs
  ) -> bool:
      r"""Updates a :obj:`tensor` in the :class:`FeatureStore` with a new
      value. Returns whether the update was succesful.
  
      .. note::
          Implementor classes can choose to define more efficient update
          methods; the default performs a removal and insertion.
  
      Args:
          tensor (torch.Tensor or np.ndarray): The feature tensor to be
              updated.
          *args: Arguments passed to :class:`TensorAttr`.
          **kwargs: Keyword arguments passed to :class:`TensorAttr`.
      """
      attr = self._tensor_attr_cls.cast(*args, **kwargs)
      self.remove_tensor(attr)
      return self.put_tensor(tensor, attr)
  
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 10 for calling the FeatureStore obj's underlying member method code with example usage: obj.update_tensor(tensor) and returns: self