class_docstr: Base implementation of a Continual Learning benchmark instance.
  A Continual Learning benchmark instance is defined by a set of streams of
  experiences (batches or tasks depending on the terminology). Each
  experience contains the training (or test, or validation, ...) data that
  becomes available at a certain time instant.
  
  Experiences are usually defined in children classes, with this class
  serving as the more general implementation. This class handles the most
  simple type of assignment: each stream is defined as a list of experiences,
  each experience is defined by a dataset.
  
  Defining the "train" and "test" streams is mandatory. This class supports
  custom streams as well. Custom streams can be accessed by using the
  `streamname_stream` field of the created instance.
  
  The name of custom streams can only contain letters, numbers or the "_"
  character and must not start with a number.
docstr: Gets the data needed to reproduce this experiment.
  
  This data can be stored using the pickle module or some other mechanism.
  It can then be loaded by passing it as the ``reproducibility_data``
  parameter in the constructor.
  
  Child classes should create their own reproducibility dictionary.
  This means that the implementation found in :class:`GenericCLScenario`
  will return an empty dictionary, which is meaningless.
  
  In order to obtain the same benchmark instance, the reproducibility
  data must be passed to the constructor along with the exact same
  input datasets.
  
  :return: A dictionary containing the data needed to reproduce the
      experiment.
code: def get_reproducibility_data(self) -> Dict[str, Any]:
      """
      Gets the data needed to reproduce this experiment.
  
      This data can be stored using the pickle module or some other mechanism.
      It can then be loaded by passing it as the ``reproducibility_data``
      parameter in the constructor.
  
      Child classes should create their own reproducibility dictionary.
      This means that the implementation found in :class:`GenericCLScenario`
      will return an empty dictionary, which is meaningless.
  
      In order to obtain the same benchmark instance, the reproducibility
      data must be passed to the constructor along with the exact same
      input datasets.
  
      :return: A dictionary containing the data needed to reproduce the
          experiment.
      """
  
      return dict()
  
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 4 for calling the DatasetScenario obj's underlying member method code with example usage: obj.get_reproducibility_data() and returns: dict