code: def concat_datasets(datasets):
      """Concatenates a list of datasets."""
      if len(datasets) == 0:
          return AvalancheDataset([])
      res = datasets[0]
      if not isinstance(res, AvalancheDataset):
          res = AvalancheDataset([res])
  
      for d in datasets[1:]:
          if not isinstance(d, AvalancheDataset):
              d = AvalancheDataset([d])
          res = res.concat(d)
      return res
  
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 8 for calling a global function with example usage: concat_datasets(datasets) and returns: res, AvalancheDataset