class_docstr: Continual Learning benchmark.
  
  A Continual Learning benchmark is a container for a set of streams of
  experiences. It may also contain other additional data useful for
  evaluation purposes or logging.
  
  The content of each experience depends on the underlying problem (data in a
  supervised problem, environments in reinforcement learning, and so on).
  
  NOTE: benchmarks should not be used by training strategies since they
  provide access to past, current, and future data.
class_docstr_tok: ["Continual","Learning","benchmark",".","A","Continual","Learning","benchmark","is","a","container","for","a","set","of","streams","of","experiences",".","It","may","also","contain","other","additional","data","useful","for","evaluation","purposes","or","logging",".","The","content","of","each","experience","depends","on","the","underlying","problem","(","data","in","a","supervised","problem",",","environments","in","reinforcement","learning",",","and","so","on",")",".","NOTE",":","benchmarks","should","not","be","used","by","training","strategies","since","they","provide","access","to","past",",","current",",","and","future","data","."]
code: def streams(self):
      # we don't want in-place modifications so we return a copy
      return copy(self._streams)
  
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 2 for calling the CLScenario obj's underlying member method code with example usage: obj.streams() and returns: copy