code: def make_detection_dataset(
      dataset: SupportedDetectionDataset,
      *,
      transform: Optional[XTransform] = None,
      target_transform: Optional[YTransform] = None,
      transform_groups: Optional[
          Mapping[str, TransformGroupDef]
      ] = None,
      initial_transform_group: Optional[str] = None,
      task_labels: Optional[Union[int, Sequence[int]]] = None,
      targets: Optional[Sequence[TTargetType]] = None,
      collate_fn: Optional[Callable[[List], Any]] = None,
  ) -> Union[DetectionDataset, SupervisedDetectionDataset]:
      """Avalanche Detection Dataset.
  
      Supervised continual learning benchmarks in Avalanche return instances of
      this dataset, but it can also be used in a completely standalone manner.
  
      This dataset applies input/target transformations, it supports
      slicing and advanced indexing and it also contains useful fields as
      `targets`, which contains the pattern dictionaries, and
      `targets_task_labels`, which contains the pattern task labels.
      The `task_set` field can be used to obtain a the subset of patterns
      labeled with a given task label.
  
      This dataset can also be used to apply several advanced operations involving
      transformations. For instance, it allows the user to add and replace
      transformations, freeze them so that they can't be changed, etc.
  
      This dataset also allows the user to keep distinct transformations groups.
      Simply put, a transformation group is a pair of transform+target_transform
      (exactly as in torchvision datasets). This dataset natively supports keeping
      two transformation groups: the first, 'train', contains transformations
      applied to training patterns. Those transformations usually involve some
      kind of data augmentation. The second one is 'eval', that will contain
      transformations applied to test patterns. Having both groups can be
      useful when, for instance, in need to test on the training data (as this
      process usually involves removing data augmentation operations). Switching
      between transformations can be easily achieved by using the
      :func:`train` and :func:`eval` methods.
  
      Moreover, arbitrary transformation groups can be added and used. For more
      info see the constructor and the :func:`with_transforms` method.
  
      This dataset will try to inherit the task labels from the input
      dataset. If none are available and none are given via the `task_labels`
      parameter, each pattern will be assigned a default task label 0.
  
      Creates a ``AvalancheDataset`` instance.
  
      :param dataset: The dataset to decorate. Beware that
          AvalancheDataset will not overwrite transformations already
          applied by this dataset.
      :param transform: A function/transform that takes the X value of a
          pattern from the original dataset and returns a transformed version.
      :param target_transform: A function/transform that takes in the target
          and transforms it.
      :param transform_groups: A dictionary containing the transform groups.
          Transform groups are used to quickly switch between training and
          eval (test) transformations. This becomes useful when in need to
          test on the training dataset as test transformations usually don't
          contain random augmentations. ``AvalancheDataset`` natively supports
          the 'train' and 'eval' groups by calling the ``train()`` and
          ``eval()`` methods. When using custom groups one can use the
          ``with_transforms(group_name)`` method instead. Defaults to None,
          which means that the current transforms will be used to
          handle both 'train' and 'eval' groups (just like in standard
          ``torchvision`` datasets).
      :param initial_transform_group: The name of the initial transform group
          to be used. Defaults to None, which means that the current group of
          the input dataset will be used (if an AvalancheDataset). If the
          input dataset is not an AvalancheDataset, then 'train' will be
          used.
      :param task_labels: The task label of each instance. Must be a sequence
          of ints, one for each instance in the dataset. Alternatively can be
          a single int value, in which case that value will be used as the
          task label for all the instances. Defaults to None, which means that
          the dataset will try to obtain the task labels from the original
          dataset. If no task labels could be found, a default task label
          0 will be applied to all instances.
      :param targets: The dictionary of detection boxes of each pattern.
          Defaults to None, which means that the targets will be retrieved from
          the dataset (if possible).
      :param collate_fn: The function to use when slicing to merge single
          patterns. This function is the function used in the data loading
          process, too. If None, the constructor will check if a
          `collate_fn` field exists in the dataset. If no such field exists,
          the default collate function for detection will be used.
      """
  
      is_supervised = isinstance(dataset, SupervisedDetectionDataset)
  
      transform_gs = _init_transform_groups(
          transform_groups,
          transform,
          target_transform,
          initial_transform_group,
          dataset,
      )
      targets_data: Optional[
          DataAttribute[TTargetType]
      ] = _init_targets(dataset, targets)
      task_labels_data: Optional[
          DataAttribute[int]
      ] = _init_task_labels(dataset, task_labels)
  
      das: List[DataAttribute] = []
      if targets_data is not None:
          das.append(targets_data)
      if task_labels_data is not None:
          das.append(task_labels_data)
  
      # Check if supervision data has been added
      is_supervised = is_supervised or (
          targets_data is not None and task_labels_data is not None
      )
  
      if collate_fn is None:
          collate_fn = getattr(
              dataset, "collate_fn", detection_collate_fn
          )
  
      data: Union[DetectionDataset, SupervisedDetectionDataset]
      if is_supervised:
          data = SupervisedDetectionDataset(
              [dataset],
              data_attributes=das if len(das) > 0 else None,
              transform_groups=transform_gs,
              collate_fn=collate_fn,
          )
      else:
          data = DetectionDataset(
              [dataset],
              data_attributes=das if len(das) > 0 else None,
              transform_groups=transform_gs,
              collate_fn=collate_fn,
          )
  
      if initial_transform_group is not None:
          return data.with_transforms(initial_transform_group)
      else:
          return data
  
imports: from functools import partial
  from typing import List, Any, Sequence, Union, Optional, TypeVar, Callable, Dict, Tuple, Mapping, overload
  import torch
  from torch import Tensor
  from torch.utils.data.dataset import Subset, ConcatDataset
  from avalanche.benchmarks.utils.utils import TaskSet, _init_task_labels, _init_transform_groups, _split_user_def_targets, _split_user_def_task_label, _traverse_supported_dataset
  from .collate_functions import detection_collate_fn
  from .data import AvalancheDataset
  from .data_attribute import DataAttribute
  from .dataset_definitions import IDataset, IDatasetWithTargets
  from .dataset_utils import SubSequence
  from .flat_data import ConstantSequence
  from .transform_groups import TransformGroupDef, DefaultTransformGroups, TransformGroups, XTransform, YTransform
label_desc: Use image node_id 4 for calling a global function with example usage: make_detection_dataset(dataset) and returns: data, data