issue_comments

Or use Indexer objects to group labels + options? This is slightly different than what you suggest:

```python class Dataset:

def sel(
    self,
    indexers: Mapping[Any, Any] | Indexer | Iterable[Indexer],
    **indexers_kwargs: Any,
):
    ...

class Indexer: def init(self, labels=None, options=None, **label_kwargs): ... ```

Let's assume a Dataset with lat / lon coordinates both sharing the same geographic index + another time dimension coordinate, then we could write:

```python indexers = [ Indexer(lon=[2, 15], lat=[45, 48], options={"foo": "bar"}), Indexer(time="2022-01-01"), ]

ds.sel(indexers) ```

This could also be used to avoid code duplication when using common selection options for different indexes.

another option would be to allow passing a custom object, like ```python class Indexer: def init(self, indexer, **options): ...

ds.sel(x=Indexer([0, 2], method="nearest")) ```

I think we wanted to have something like that, anyways, to be able to specify other behaviors of a slice, like right-exclusive?

Or we could simply decide that .sel() should not accept arbitrary options and handle special cases, e.g., via accessors.

It would actually make sense to have something like .my_accessor.sel_k_neighbors(). Not so great to have a separate method just for an optimization option, though.

Another difficulty regarding multi-coordinate indexes: ideally options should be set per index, not per coordinate.