code: def detection_subset(
      dataset: SupportedDetectionDataset,
      indices: Optional[Sequence[int]] = None,
      *,
      class_mapping: Optional[Sequence[int]] = None,
      transform: Optional[XTransform] = None,
      target_transform: Optional[YTransform] = None,
      transform_groups: Optional[
          Mapping[str, Tuple[XTransform, YTransform]]
      ] = 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]:
      """Creates an ``AvalancheSubset`` instance.
  
      For simple subset operations you should use the method
      `dataset.subset(indices)`.
      Use this constructor only if you need to redefine transformation or
      class/task labels.
  
      A Dataset that behaves like a PyTorch :class:`torch.utils.data.Subset`.
      This Dataset also supports transformations, slicing, advanced indexing,
      the targets field, class mapping and all the other goodies listed in
      :class:`AvalancheDataset`.
  
      :param dataset: The whole dataset.
      :param indices: Indices in the whole set selected for subset. Can
          be None, which means that the whole dataset will be returned.
      :param class_mapping: A list that, for each possible class label value,
          contains its corresponding remapped value. Can be None.
      :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 for each instance. Must be a sequence
          of ints, one for each instance in the dataset. This can either be a
          list of task labels for the original dataset or the list of task
          labels for the instances of the subset (an automatic detection will
          be made). In the unfortunate case in which the original dataset and
          the subset contain the same amount of instances, then this parameter
          is considered to contain the task labels of the subset.
          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 target dictionary of each pattern. Defaults to None,
          which means that the targets will be retrieved from the dataset (if
          possible). This can either be a list of target dictionaries for the
          original dataset or the list of target dictionaries for the instances
          of the subset (an automatic detection will be made). In the
          unfortunate case in which the original dataset and the subset contain
          the same amount of instances, then this parameter is considered to
          contain the target dictionaries of the subset.
      :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)
  
      if isinstance(dataset, DetectionDataset):
          if (
              class_mapping is None
              and transform is None
              and target_transform is None
              and transform_groups is None
              and initial_transform_group is None
              and task_labels is None
              and targets is None
              and collate_fn is None
          ):
              return dataset.subset(indices)
  
      targets_data: Optional[
          DataAttribute[TTargetType]
      ] = _init_targets(dataset, targets, check_shape=False)
      task_labels_data: Optional[
          DataAttribute[int]
      ] = _init_task_labels(dataset, task_labels, check_shape=False)
  
      del task_labels
      del targets
  
      transform_gs = _init_transform_groups(
          transform_groups,
          transform,
          target_transform,
          initial_transform_group,
          dataset,
      )
  
      if initial_transform_group is not None and isinstance(
          dataset, AvalancheDataset
      ):
          dataset = dataset.with_transforms(initial_transform_group)
  
      if class_mapping is not None:  # update targets
          if targets_data is None:
              # Should not happen
              # The following line usually fails
              targets_data = dataset.targets  # type: ignore
  
          assert (
              targets_data is not None
          ), "To execute the class mapping, a list of targets is required."
  
          tgs = [
              _detection_class_mapping_transform(
                  class_mapping, example_target_dict
              )
              for example_target_dict in targets_data
          ]
  
          targets_data = DataAttribute(tgs, "targets")
  
      if class_mapping is not None:
          mapping_fn = partial(
              _detection_class_mapping_transform, class_mapping
          )
          frozen_transform_groups = DefaultTransformGroups(
              (None, mapping_fn)
          )
      else:
          frozen_transform_groups = None
  
      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 = detection_collate_fn
  
      if is_supervised:
          return SupervisedDetectionDataset(
              [dataset],
              indices=list(indices) if indices is not None else None,
              data_attributes=das if len(das) > 0 else None,
              transform_groups=transform_gs,
              frozen_transform_groups=frozen_transform_groups,
              collate_fn=collate_fn,
          )
      else:
          return DetectionDataset(
              [dataset],
              indices=list(indices) if indices is not None else None,
              data_attributes=das if len(das) > 0 else None,
              transform_groups=transform_gs,
              frozen_transform_groups=frozen_transform_groups,
              collate_fn=collate_fn,
          )
  
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 10 for calling a global function with example usage: detection_subset(dataset, indices) and returns: SupervisedDetectionDataset, DetectionDataset, dataset