allow computing just a small number of variables

Is your feature request related to a problem?

I frequently find myself computing a handful of variables of a dataset (typically coordinates) and assigning them back to the dataset, and wishing we had a method / function that allowed that.

Describe the solution you'd like

I'd imagine something like

ds.compute(variables=variable_names)

but I'm undecided on whether that's a good idea (it might make .compute more complex?)

Describe alternatives you've considered

So far I've been using something like

ds.assign_coords({k: lambda ds: ds[k].compute() for k in variable_names})
ds.pipe(lambda ds: ds.merge(ds[variable_names].compute()))

but both are not easy to type / understand (though having .merge take a callable would make this much easier). Also, the first option computes variables separately, which may not be ideal?

Additional context

No response

Start with Dataset.compute and compare it with the assign_coords, pipe, and merge patterns shown in the issue. Resolve the API and computation semantics for selecting variables, then add coverage showing the selected variables are computed and assigned back without unnecessary separate computation.