docstr: A dictionary mapping each stream (by name) to a list.
  
  Each element of the list is a set describing the classes included in
  that experience (identified by its index).
  
  In previous releases this field contained the list of sets for the
  training stream (that is, there was no way to obtain the list for other
  streams). That behavior is deprecated and support for that usage way
  will be removed in the future.
code: def classes_in_experience(
      self,
  ) -> Mapping[str, Sequence[Optional[Set[int]]]]:
      """
      A dictionary mapping each stream (by name) to a list.
  
      Each element of the list is a set describing the classes included in
      that experience (identified by its index).
  
      In previous releases this field contained the list of sets for the
      training stream (that is, there was no way to obtain the list for other
      streams). That behavior is deprecated and support for that usage way
      will be removed in the future.
      """
      pass
  
imports: from abc import ABC, abstractmethod
  from dataclasses import dataclass
  import re
  from typing import Generic, TypeVar, Union, Sequence, Callable, Optional, Dict, Any, Iterable, List, Set, Tuple, Mapping, TYPE_CHECKING
  from .None import DatasetExperience
  from ..generic_scenario import CLScenario, CLExperience, CLStream, SequenceCLStream
  from .lazy_dataset_sequence import LazyDatasetSequence
  from avalanche.benchmarks.utils import AvalancheDataset
  from torch.utils.data.dataset import Dataset
label_desc: Use image node_id 1 for calling the ClassesTimelineCLScenario obj's underlying member method code with example usage: obj.classes_in_experience() without return types