code: def as_classification_dataset(
      dataset: ISupportedClassificationDataset[T_co],
      transform_groups: Optional[TransformGroups] = None,
  ) -> "TaskAwareClassificationDataset":
      """Converts a dataset with a `targets` field into an Avalanche ClassificationDataset."""
      from avalanche.benchmarks.utils.classification_dataset import (
          ClassificationDataset,
      )
  
      if isinstance(dataset, ClassificationDataset):
          return dataset
      da = DataAttribute(dataset.targets, "targets")
      return ClassificationDataset(
          [dataset],
          transform_groups=transform_groups,
          data_attributes=[da],
      )
  
imports: from collections import OrderedDict, defaultdict, deque
  from typing import TYPE_CHECKING, Any, Callable, Generic, Iterator, List, Iterable, Mapping, Optional, Sequence, TypeVar, Union, Dict, SupportsInt
  import warnings
  import torch
  from torch import Tensor
  from torch.utils.data import Subset, ConcatDataset
  from .data import AvalancheDataset
  from .data_attribute import DataAttribute
  from .dataset_definitions import ISupportedClassificationDataset
  from .dataset_utils import SubSequence, find_list_from_index
  from .flat_data import ConstantSequence
  from .transform_groups import TransformGroupDef, TransformGroups, XTransform, YTransform
label_desc: Use image node_id 5 for calling a global function with example usage: as_classification_dataset(dataset, transform_groups) and returns: ClassificationDataset, dataset