class_docstr: we want class labels to be integers, not tensors.
code: def __getitem__(self, item):
      elem = list(super().__getitem__(item))
      elem[1] = elem[1].item()
      return tuple(elem)
  
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 1 for calling the _TensorClassificationDataset obj's underlying member method code with example usage: obj.__getitem__(item) and returns: tuple