class_docstr: A CL stream build from a pre-initialized list of experience.
  
  NOTE: streams should not be used by training strategies since they
  provide access to past, current, and future data.
class_docstr_tok: ["A","CL","stream","build","from","a","pre-initialized","list","of","experience",".","NOTE",":","streams","should","not","be","used","by","training","strategies","since","they","provide","access","to","past",",","current",",","and","future","data","."]
code: def exps(self) -> Tuple[TCLExperience, ...]:
      return tuple(self.exps_iter)
  
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 EagerCLStream obj's underlying member method code with example usage: obj.exps() and returns: tuple