class_docstr: Definition of a learning experience based on a :class:`GenericCLScenario`
  instance.
  
  This experience implementation uses the generic experience-patterns
  assignment defined in the :class:`GenericCLScenario` instance. Instances of
  this class are usually obtained from a benchmark stream.
docstr: Creates an instance of a generic experience given the stream from this
  experience was taken and the current experience ID.
  
  :param origin_stream: The stream from which this experience was
      obtained.
  :param current_experience: The current experience ID, as an integer.
docstr_tok: ["Creates","an","instance","of","a","generic","experience","given","the","stream","from","this","experience","was","taken","and","the","current","experience","ID",".",":","param","origin_stream",":","The","stream","from","which","this","experience","was","obtained",".",":","param","current_experience",":","The","current","experience","ID",",","as","an","integer","."]
code: def __init__(
      self: TClassificationExperience,
      origin_stream: ClassificationStream[TClassificationExperience],
      current_experience: int,
  ):
      """
      Creates an instance of a generic experience given the stream from this
      experience was taken and the current experience ID.
  
      :param origin_stream: The stream from which this experience was
          obtained.
      :param current_experience: The current experience ID, as an integer.
      """
  
      self._benchmark: ClassificationScenario = origin_stream.benchmark
  
      dataset: TClassificationDataset = (
          origin_stream.benchmark.stream_definitions[
              origin_stream.name
          ].exps_data[current_experience]
      )
  
      (
          classes_in_this_exp,
          previous_classes,
          classes_seen_so_far,
          future_classes,
      ) = origin_stream.benchmark.get_classes_timeline(
          current_experience, stream=origin_stream.name
      )
  
      super().__init__(
          origin_stream,
          dataset,
          current_experience,
          classes_in_this_exp,
          previous_classes,
          classes_seen_so_far,
          future_classes,
      )
  
imports: from typing import Callable, TypeVar, Union, Sequence, Optional, Iterable, List, Set, Tuple, Mapping, overload, TYPE_CHECKING
  import warnings
  from avalanche.benchmarks.scenarios.deprecated import AbstractClassTimelineExperience
  from avalanche.benchmarks.scenarios.deprecated.dataset_scenario import ClassesTimelineCLScenario, FactoryBasedStream, TStreamsUserDict
  from avalanche.benchmarks.utils.dataset_utils import manage_advanced_indexing
label_desc: Use image node_id 1 to create a new ClassificationExperience object from inherited base classes: AbstractClassTimelineExperience with example: obj = ClassificationExperience(origin_stream, current_experience)