class_docstr: Experience decorator that provides info about classes occurrence over time.
class_docstr_tok: ["Experience","decorator","that","provides","info","about","classes","occurrence","over","time","."]
docstr: The list of classes of next experiences.
docstr_tok: ["The","list","of","classes","of","next","experiences","."]
code: def future_classes(self) -> List[int]:
      """The list of classes of next experiences."""
      ...
  
imports: import warnings
  from copy import copy
  from typing import Iterable, Sequence, Optional, Dict, List, Protocol
  import torch
  from avalanche.benchmarks.utils.classification_dataset import ClassificationDataset, _as_taskaware_supervised_classification_dataset, TaskAwareSupervisedClassificationDataset
  from avalanche.benchmarks.utils.data import AvalancheDataset
  from avalanche.benchmarks.utils.data_attribute import DataAttribute
  from .dataset_scenario import _split_dataset_by_attribute, DatasetExperience
  from ..None import CLScenario, CLStream, EagerCLStream
label_desc: Use image node_id 4 for calling the ClassesTimeline obj's underlying member method code with example usage: obj.future_classes() without return types