code: def _taskaware_classification_subset(
      dataset: SupportedDataset,
      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[
      TaskAwareClassificationDataset,
      TaskAwareSupervisedClassificationDataset,
  ]:
      """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 target (Y) value,
          contains its corresponding remapped value. Can be None.
          Beware that setting this parameter will force the final
          dataset type to be CLASSIFICATION or UNDEFINED.
      :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 label 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 labels for the
          original dataset or the list of target 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 target labels 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 will be used.
      """
  
      is_supervised = isinstance(
          dataset, TaskAwareSupervisedClassificationDataset
      )
  
      if isinstance(dataset, TaskAwareClassificationDataset):
          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)
  
      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:
              tgs = [class_mapping[el] for el in dataset.targets]  # type: ignore
          else:
              tgs = [class_mapping[el] for el in targets_data]
  
          targets_data = DataAttribute(tgs, "targets")
  
      if class_mapping is not None:
          frozen_transform_groups = DefaultTransformGroups(
              (None, partial(lookup, class_mapping))
          )
      else:
          frozen_transform_groups = None
  
      das = []
      if targets_data is not None:
          das.append(targets_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 task_labels_data is not None:
          # special treatment for task labels depending on length for
          # backward compatibility
          if len(task_labels_data) != len(dataset):
              # task labels are already subsampled
              dataset = TaskAwareClassificationDataset(
                  [dataset],
                  indices=list(indices)
                  if indices is not None
                  else None,
                  data_attributes=das,
                  transform_groups=transform_gs,
                  frozen_transform_groups=frozen_transform_groups,
                  collate_fn=collate_fn,
              )
              # now add task labels
              if is_supervised:
                  return TaskAwareSupervisedClassificationDataset(
                      [dataset],
                      data_attributes=[dataset.targets, task_labels_data],  # type: ignore
                  )
              else:
                  return TaskAwareClassificationDataset(
                      [dataset],
                      data_attributes=[dataset.targets, task_labels_data],  # type: ignore
                  )
          else:
              das.append(task_labels_data)
  
      if is_supervised:
          return TaskAwareSupervisedClassificationDataset(
              [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 TaskAwareClassificationDataset(
              [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
  import torch
  from torch.utils.data.dataset import Subset, ConcatDataset, TensorDataset
  from avalanche.benchmarks.utils.utils import TaskSet, _count_unique, find_common_transforms_group, _init_task_labels, _init_transform_groups, _split_user_def_targets, _split_user_def_task_label, _traverse_supported_dataset
  from avalanche.benchmarks.utils.data import AvalancheDataset
  from avalanche.benchmarks.utils.transform_groups import TransformGroupDef, DefaultTransformGroups, XTransform, YTransform
  from avalanche.benchmarks.utils.data_attribute import DataAttribute
  from avalanche.benchmarks.utils.dataset_utils import SubSequence
  from avalanche.benchmarks.utils.flat_data import ConstantSequence
  from avalanche.benchmarks.utils.dataset_definitions import ISupportedClassificationDataset, ITensorDataset, IDatasetWithTargets
  from typing import List, Any, Sequence, Union, Optional, TypeVar, Callable, Dict, Tuple, Mapping, overload
label_desc: Use image node_id 10 for calling a global function with example usage: _taskaware_classification_subset(dataset, indices) and returns: TaskAwareSupervisedClassificationDataset, TaskAwareClassificationDataset, dataset, TaskAwareSupervisedClassificationDataset, TaskAwareClassificationDataset