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: Removes a tensor from the :class:`FeatureStore`.
  Returns whether deletion was successful.
  
  Args:
      *args: Arguments passed to :class:`TensorAttr`.
      **kwargs: Keyword arguments passed to :class:`TensorAttr`.
  
  Raises:
      ValueError: If the input :class:`TensorAttr` is not fully
          specified.
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code: def remove_tensor(self, *args, **kwargs) -> bool:
      r"""Removes a tensor from the :class:`FeatureStore`.
      Returns whether deletion was successful.
  
      Args:
          *args: Arguments passed to :class:`TensorAttr`.
          **kwargs: Keyword arguments passed to :class:`TensorAttr`.
  
      Raises:
          ValueError: If the input :class:`TensorAttr` is not fully
              specified.
      """
      attr = self._tensor_attr_cls.cast(*args, **kwargs)
      if not attr.is_fully_specified():
          raise ValueError(
              f"The input TensorAttr '{attr}' is not fully "
              f"specified. Please fully-specify the input by "
              f"specifying all 'UNSET' fields."
          )
      return self._remove_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 9 for calling the FeatureStore obj's underlying member method code with example usage: obj.remove_tensor() and returns: self