Preprocess function for save_mfdataset

Is your feature request related to a problem? Please describe.
I would like to supply a preprocess argument to save_mfdataset that gets applied to each dataset before getting written to disk, similar to how open_mfdataset gives you such option. Specifically, have a dataset that I want to split by unique values along dimension, apply some further logic to each sub-dataset, then save each sub-dataset to a different file. Currently I'm able to split and save using the following code provided in the API docs:

years, datasets = zip(*ds.groupby("time.year"))
paths = ["%s.nc" % y for y in years]
xr.save_mfdataset(datasets, paths)

What's missing is the ability to insert further logic to each of the sub-datasets given by the groupby object. If I try iterating through datasets here and chain further operations to each element, the calculations begin to execute serially even though ds is a dask array:

save_mfdataset([ds.foo() for ds in datasets], paths)

Describe the solution you'd like
Instead, I'd like the ability to do:

xr.save_mfdataset(datasets, paths, preprocess=lambda ds: ds.foo())

Describe alternatives you've considered
Not sure.

Compare the save_mfdataset and open_mfdataset entry points to understand how preprocessing is exposed, then trace the save_mfdataset path used by the documented groupby example. Add support for the requested preprocess argument and verify that it is applied to each dataset without forcing the dask-backed calculations to execute serially.