class_docstr: Experience attributes are used to define data belonging to an
  experience which may only be available at train or eval time.
  
  For example, experiences often keep a reference to the entire stream,
  which should be accessible only by the loggers and evaluation system,
  but should never be used by the strategy in the train/eval loops.
class_docstr_tok: ["Experience","attributes","are","used","to","define","data","belonging","to","an","experience","which","may","only","be","available","at","train","or","eval","time",".","For","example",",","experiences","often","keep","a","reference","to","the","entire","stream",",","which","should","be","accessible","only","by","the","loggers","and","evaluation","system",",","but","should","never","be","used","by","the","strategy","in","the","train\/eval","loops","."]
docstr: Init.
  
  :param value: attribute value.
  :param use_in_train: if True the attribute is available at training
      time.
  :param use_in_eval: if True the attribute is available at evaluation
      time.
docstr_tok: ["Init",".",":","param","value",":","attribute","value",".",":","param","use_in_train",":","if","True","the","attribute","is","available","at","training","time",".",":","param","use_in_eval",":","if","True","the","attribute","is","available","at","evaluation","time","."]
code: def __init__(
      self,
      value: TCov,
      use_in_train: bool = False,
      use_in_eval: bool = False,
  ):
      """Init.
  
      :param value: attribute value.
      :param use_in_train: if True the attribute is available at training
          time.
      :param use_in_eval: if True the attribute is available at evaluation
          time.
      """
      self.value: TCov = value
      self.use_in_train: bool = use_in_train
      self.use_in_eval: bool = use_in_eval
  
imports: from abc import abstractmethod, ABC
  from contextlib import contextmanager
  from copy import copy
  from enum import Enum
  from types import GeneratorType
  from typing import Any, Dict, Generator, Iterator, List, Iterable, Optional, Sequence, Tuple, TypeVar, Union, Generic, overload
  from typing_extensions import final
  import numpy
  from avalanche.benchmarks.utils.dataset_utils import slice_alike_object_to_indices
label_desc: Use image node_id 1 to create a new ExperienceAttribute object from inherited base classes: Generic with example: obj = ExperienceAttribute(value, use_in_train, use_in_eval)