class_docstr: FlatData is a dataset optimized for efficient repeated concatenation
  and subset operations.
  
  The class combines concatentation and subsampling operations in a single
  class.
  
  Class for internal use only. Users shuold use `AvalancheDataset` for data
  or `DataAttribute` for attributes such as class and task labels.
  
  *Notes for subclassing*
  
  Cat/Sub operations are "flattened" if possible, which means that they will
  take the datasets and indices from their arguments and create a new dataset
  with them, avoiding the creation of large trees of dataset (what would
  happen with PyTorch datasets). Flattening is not always possible, for
  example if the data contains additional info (e.g. custom transformation
  groups), so subclasses MUST set `can_flatten` properly in order to avoid
  nasty bugs.
code: def __getitem__(self: TFlatData, item: slice) -> TFlatData:
      ...
  
imports: import bisect
  import sys
  import numpy
  from avalanche.benchmarks.utils.dataset_utils import slice_alike_object_to_indices
  from typing import Iterable, List, Optional, Sequence, Tuple, TypeVar, Union, overload
  from torch.utils.data import ConcatDataset
  import itertools
  from avalanche.benchmarks.utils.dataset_definitions import IDataset
label_desc: Use image node_id 7 for calling the FlatData obj's underlying member method code with example usage: obj.__getitem__(item) without return types