Improve documentation on InferenceData.map

1. Is InferenceData.map always done elementwise or is some sort of reduction applied?
2. What is the function signature (particularly input shape and output shape) expected for a function passed to map? Currently, the docs just say “fun: callable / Function to be applied to each group.”

For example, suppose you want to evaluate a new function of the model parameters (iterated for each step in each chain). Often, one might use pymc3.Deterministic to add those to the compute graph during model sampling, but I imagine that InferenceData.map might be a nice way to run such calculations without having to build it into the original compute graph at training time. I think a couple of examples and some guardrails on what fun ought to be might improve the usability here.

[kyleabeauchamp]

FYI, I was able to figure out my use case combining arviz + xarray + sklearn metrics. I spent several hours staring at the docs for wrap_xarray_ufunc and eventually was able to identify the following pattern:

xr.apply_ufunc(f, y, yhat, input_core_dims=[[dim], [dim]], vectorize=True)

[AlexAndorra]

Closing, as the doc improvement has been merged and released in 0.9.0, but feel free to reopen if needed 😉