[Python] Why does PyTorch's Dataset abstract class not inherit from ABC?

When writing abstract base classes in Python, answers on Stackexchange (see here and here) recommend using the abc module and inheriting from abc.ABC: class BaseClass(abc.ABC):....

However, PyTorch's Dataset class, which is an abstract base class, does not use the ABC method and instead simply raises NotImplementedError:

class Dataset(Generic[_T_co]):
r"""An abstract class representing a :class:`Dataset`.

All datasets that represent a map from keys to data samples should subclass
it. All subclasses should overwrite :meth:`__getitem__`, supporting fetching a
data sample for a given key. Subclasses could also optionally overwrite
:meth:`__len__`, which is expected to return the size of the dataset by many
:class:`~torch.utils.data.Sampler` implementations and the default options
of :class:`~torch.utils.data.DataLoader`. Subclasses could also
optionally implement :meth:`__getitems__`, for speedup batched samples
loading. This method accepts list of indices of samples of batch and returns
list of samples.

.. note::
:class:`~torch.utils.data.DataLoader` by default constructs an index
sampler that yields integral indices. To make it work with a map-style
dataset with non-integral indices/keys, a custom sampler must be provided.
"""

def __getitem__(self, index) -> _T_co:
raise NotImplementedError("Subclasses of Dataset should implement __getitem__.")

# def __getitems__(self, indices: List) -> List[_T_co]:
# Not implemented to prevent false-positives in fetcher check in
# torch.utils.data._utils.fetch._MapDatasetFetcher

def __add__(self, other: "Dataset[_T_co]") -> "ConcatDataset[_T_co]":
return ConcatDataset([self, other])

# No `def __len__(self)` default?
# See NOTE [ Lack of Default `__len__` in Python Abstract Base Classes ]
# in pytorch/torch/utils/data/sampler.py


(link to Github source)

Why does PyTorch not use the ABC method?

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