code: def _concat_taskaware_classification_datasets(
      datasets: Sequence[SupportedDataset],
      *,
      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], Sequence[Sequence[int]]]
      ] = None,
      targets: Optional[
          Union[Sequence[TTargetType], Sequence[Sequence[TTargetType]]]
      ] = None,
      collate_fn: Optional[Callable[[List], Any]] = None,
  ) -> Union[
      TaskAwareClassificationDataset,
      TaskAwareSupervisedClassificationDataset,
  ]:
      """Creates a ``AvalancheConcatDataset`` instance.
  
      For simple subset operations you should use the method
      `dataset.concat(other)` or
      `concat_datasets` from `avalanche.benchmarks.utils.utils`.
      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.ConcatDataset`. However, this Dataset also supports
      transformations, slicing, advanced indexing and the targets field and all
      the other goodies listed in :class:`AvalancheDataset`.
  
      This dataset guarantees that the operations involving the transformations
      and transformations groups are consistent across the concatenated dataset
      (if they are subclasses of :class:`AvalancheDataset`).
  
      :param datasets: A collection of datasets.
      :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 if all
          AvalancheDatasets in the input datasets list agree on a common
          group (the "current group" is the same for all datasets), then that
          group will be used as the initial one. If the list of input datasets
          does not contain an AvalancheDataset or if the AvalancheDatasets
          do not agree on a common group, then 'train' will be used.
      :param targets: The label of each pattern. Can either be a sequence of
          labels or, alternatively, a sequence containing sequences of labels
          (one for each dataset to be concatenated). Defaults to None, which
          means that the targets will be retrieved from the datasets (if
          possible).
      :param task_labels: The task labels for each pattern. Must be a sequence
          of ints, one for each pattern in the dataset. Alternatively, task
          labels can be expressed as a sequence containing sequences of ints
          (one for each dataset to be concatenated) or even a single int,
          in which case that value will be used as the task label for all
          instances. Defaults to None, which means that the dataset will try
          to obtain the task labels from the original datasets. If no task
          labels could be found for a dataset, a default task label 0 will
          be applied to all patterns of that dataset.
      :param collate_fn: The function to use when slicing to merge single
          patterns. In the future this function may become the function
          used in the data loading process, too. If None, the constructor
          will check if a `collate_fn` field exists in the first dataset. If
          no such field exists, the default collate function will be used.
          Beware that the chosen collate function will be applied to all
          the concatenated datasets even if a different collate is defined
          in different datasets.
      """
      dds = []
      per_dataset_task_labels = _split_user_def_task_label(
          datasets, task_labels
      )
  
      per_dataset_targets = _split_user_def_targets(
          datasets, targets, lambda x: isinstance(x, int)
      )
  
      # Find common "current_group" or use "train"
      if initial_transform_group is None:
          initial_transform_group = find_common_transforms_group(
              datasets, default_group="train"
          )
  
      supervised = True
      for dd, dataset_task_labels, dataset_targets in zip(
          datasets, per_dataset_task_labels, per_dataset_targets
      ):
          dd = _make_taskaware_classification_dataset(
              dd,
              transform=transform,
              target_transform=target_transform,
              transform_groups=transform_groups,
              initial_transform_group=initial_transform_group,
              task_labels=dataset_task_labels,
              targets=dataset_targets,
              collate_fn=collate_fn,
          )
  
          if not isinstance(
              dd, TaskAwareSupervisedClassificationDataset
          ):
              supervised = False
  
          dds.append(dd)
  
      if len(dds) > 0:
          transform_groups_obj = _init_transform_groups(
              transform_groups,
              transform,
              target_transform,
              initial_transform_group,
              dds[0],
          )
      else:
          transform_groups_obj = None
  
      supervised = supervised and (
          (len(dds) > 0)
          or (targets is not None and task_labels is not None)
      )
  
      data: Union[
          TaskAwareSupervisedClassificationDataset,
          TaskAwareClassificationDataset,
      ]
      if supervised:
          data = TaskAwareSupervisedClassificationDataset(
              dds, transform_groups=transform_groups_obj
          )
      else:
          data = TaskAwareClassificationDataset(
              dds, transform_groups=transform_groups_obj
          )
      return data.with_transforms(initial_transform_group)
  
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 17 for calling a global function with example usage: _concat_taskaware_classification_datasets(datasets) and returns: data