[Python] Can we provide an example and motivation on when to use covariant and contravariant?

I have some troubles finding real use cases of covariant/contravariant. I would like a concrete motivation and example on when covariant/contravariant is used, in particular, I would appreciate the example to be such that if covariant/contravariant is not applied, then the application would be buggy/not type safe.

To start, I know PyTorch's Dataset and DataLoader is parametrized by a covariant type:

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 a 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


I wonder if someone can come up with convincing example of why a Dataset needs to be covariant.

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