class_docstr: Base implementation of a Continual Learning object detection benchmark.
  
  For more info, please refer to the base class :class:`DatasetScenario`.
docstr: Creates an instance a Continual Learning object detection benchmark.
  
  :param stream_definitions: The definition of the streams. For a more
      precise description, please refer to :class:`DatasetScenario`
  :param n_classes: The number of classes in the scenario. Defaults to
      None.
  :param stream_factory: A callable that, given the name of the
      stream and the benchmark instance, returns a stream instance.
      Defaults to the constructor of :class:`DetectionStream`.
  :param experience_factory: A callable that, given the
      stream instance and the experience ID, returns an experience
      instance.
      Defaults to the constructor of :class:`DetectionExperience`.
  :param complete_test_set_only: If True, the test stream will contain
      a single experience containing the complete test set. This also
      means that the definition for the test stream must contain the
      definition for a single experience.
code: def __init__(
      self: TDetectionScenario,
      stream_definitions: TStreamsUserDict,
      n_classes: Optional[int] = None,
      stream_factory: Callable[
          [str, TDetectionScenario], TDetectionStream
      ] = _default_detection_stream_factory,
      experience_factory: Callable[
          [TDetectionStream, int], TDetectionExperience
      ] = _default_detection_experience_factory,
      complete_test_set_only: bool = False,
  ):
      """
      Creates an instance a Continual Learning object detection benchmark.
  
      :param stream_definitions: The definition of the streams. For a more
          precise description, please refer to :class:`DatasetScenario`
      :param n_classes: The number of classes in the scenario. Defaults to
          None.
      :param stream_factory: A callable that, given the name of the
          stream and the benchmark instance, returns a stream instance.
          Defaults to the constructor of :class:`DetectionStream`.
      :param experience_factory: A callable that, given the
          stream instance and the experience ID, returns an experience
          instance.
          Defaults to the constructor of :class:`DetectionExperience`.
      :param complete_test_set_only: If True, the test stream will contain
          a single experience containing the complete test set. This also
          means that the definition for the test stream must contain the
          definition for a single experience.
      """
  
      super().__init__(
          stream_definitions=stream_definitions,
          stream_factory=stream_factory,
          experience_factory=experience_factory,
          complete_test_set_only=complete_test_set_only,
      )
  
      self.n_classes: Optional[int] = n_classes
      """
          The number of classes in the scenario.
  
          May be None if unknown.
          """
  
imports: from typing import Iterable, Mapping, Optional, Sequence, Set, Tuple, TypeVar, List, Callable, Union, overload
  import warnings
  from avalanche.benchmarks.scenarios.deprecated.dataset_scenario import ClassesTimelineCLScenario, DatasetScenario, FactoryBasedStream, TStreamsUserDict
  from avalanche.benchmarks.scenarios.deprecated import AbstractClassTimelineExperience
  from avalanche.benchmarks.utils.data import AvalancheDataset
  from avalanche.benchmarks.utils.dataset_utils import manage_advanced_indexing
  from avalanche.benchmarks.utils.detection_dataset import DetectionDataset
label_desc: Use image node_id 1 to create a new DetectionScenario object from inherited base classes: ClassesTimelineCLScenario with example: obj = DetectionScenario(stream_definitions, n_classes, stream_factory, experience_factory, complete_test_set_only)