code: def single_flat_dataset(
      dataset, include_leaf_transforms: bool = True
  ):
      """
      Obtains the single leaf dataset of a Dataset.
  
      A dataset can be traversed and flattened to a single leaf dataset
      if all subset and dataset concatenations point to a single leaf
      dataset and if transformations are the same across all paths.
  
      :param dataset: The dataset to traverse.
      :param include_leaf_transforms: If True, include the transformations
          found in the leaf dataset in the transforms list. Defaults to True.
      :return: A tuple containing three elements: the dataset, the list of
          indices, and the list of transformations. If the dataset cannot
          be flattened to a single dataset, None is returned.
      """
      leaves_dict = leaf_datasets(
          dataset, include_leaf_transforms=include_leaf_transforms
      )
      if len(leaves_dict) != 1:
          return None
  
      # Obtain the single dataset element
      dataset = list(leaves_dict.keys())[0]
      indices_and_transforms = list(leaves_dict.values())[0]
  
      # Check that all transforms are the same
      first_transform = indices_and_transforms[0][1]
      same_transforms = all(
          [first_transform == t for _, t in indices_and_transforms]
      )
  
      if not same_transforms:
          return None
  
      flat_indices = [i for i, _ in indices_and_transforms]
  
      return dataset, flat_indices, first_transform
  
file_path: avalanche/avalanche/benchmarks/utils/dataset_traversal_utils.py
imports: from collections import OrderedDict, defaultdict, deque
  from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, TypeVar, Union
  from avalanche.benchmarks.scenarios.generic_scenario import CLScenario
  from avalanche.benchmarks.utils.data import _FlatDataWithTransform, AvalancheDataset
  from avalanche.benchmarks.utils.dataset_definitions import IDataset
  from avalanche.benchmarks.utils.dataset_utils import find_list_from_index
  from avalanche.benchmarks.utils.flat_data import FlatData
  from torch.utils.data import Subset, ConcatDataset, Dataset
  from avalanche.benchmarks.utils.transform_groups import EmptyTransformGroups
  from avalanche.benchmarks.utils.transforms import TupleTransform
  from torchvision.datasets.vision import StandardTransform
label_desc: Use image node_id 6 for calling a global function with example usage: single_flat_dataset(dataset, include_leaf_transforms) and returns: dataset,  flat_indices,  first_transform, None, None