class_docstr: Definition of a learning experience. A learning experience contains a set of
  patterns which has become available at a particular time instant. The
  content and size of an Experience is defined by the specific benchmark that
  creates the experience.
  
  For instance, an experience of a New Classes scenario will contain all
  patterns belonging to a subset of classes of the original training set. An
  experience of a New Instance scenario will contain patterns from previously
  seen classes.
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docstr: Creates an instance of an experience given the benchmark
  stream, the current experience ID and data about the classes timeline.
  
  :param origin_stream: The stream from which this experience was
      obtained.
  :param current_experience: The current experience ID, as an integer.
  :param classes_in_this_exp: The list of classes in this experience.
  :param previous_classes: The list of classes in previous experiences.
  :param classes_seen_so_far: List of classes of current and previous
      experiences.
  :param future_classes: The list of classes of next experiences.
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code: def __init__(
      self: TDatasetExperience,
      origin_stream: "CLStream[TDatasetExperience]",
      dataset: TCLDataset,
      current_experience: int,
      classes_in_this_exp: Optional[Sequence[int]],
      previous_classes: Optional[Sequence[int]],
      classes_seen_so_far: Optional[Sequence[int]],
      future_classes: Optional[Sequence[int]],
  ):
      """
      Creates an instance of an experience given the benchmark
      stream, the current experience ID and data about the classes timeline.
  
      :param origin_stream: The stream from which this experience was
          obtained.
      :param current_experience: The current experience ID, as an integer.
      :param classes_in_this_exp: The list of classes in this experience.
      :param previous_classes: The list of classes in previous experiences.
      :param classes_seen_so_far: List of classes of current and previous
          experiences.
      :param future_classes: The list of classes of next experiences.
      """
  
      self.classes_in_this_experience: Optional[
          Sequence[int]
      ] = classes_in_this_exp
      """ The list of classes in this experience """
  
      self.previous_classes: Optional[Sequence[int]] = previous_classes
      """ The list of classes in previous experiences """
  
      self.classes_seen_so_far: Optional[
          Sequence[int]
      ] = classes_seen_so_far
      """ List of classes of current and previous experiences """
  
      self.future_classes: Optional[Sequence[int]] = future_classes
      """ The list of classes of next experiences """
  
      super().__init__(
          current_experience=current_experience,
          origin_stream=origin_stream,
          benchmark=origin_stream.benchmark,  # type: ignore
          dataset=dataset,
      )
  
file_path: avalanche/avalanche/benchmarks/scenarios/deprecated/__init__.py
imports: from abc import ABC
  from typing import Generic, List, Optional, Sequence, TypeVar
  from ..None import CLExperience, CLStream, CLScenario
  from typing import TYPE_CHECKING
label_desc: Use image node_id 1 to create a new AbstractClassTimelineExperience object from inherited base classes: DatasetExperience, ABC with example: obj = AbstractClassTimelineExperience(origin_stream, dataset, current_experience, classes_in_this_exp, previous_classes, classes_seen_so_far, future_classes)