What is this PR about?

Closes #6047.

This PR did two things:

1. It makes the mutable dimension SharedVariable length have the name of the corresponding dimension.
2. Adds two arguments to compile_forward_sampling_function: constant_data and constant_coords.

These two arguments allow it to determine if a SharedVariable has changed after between a call to sample and sample_posterior_predictive. We have to note that constant_data is only knowable if sample_posterior_predictive is called supplying an InferenceData object, and constant_coords are only knowable if sample_posterior_predictive is called supplying an InferenceData or an xarray.Dataset object.

The way sample_posterior_predictive is able to determine if a data container changed is by doing the following. It first checks the InferenceData.constant_data group to find the values of data containers at inference time, and passes those into the constant_data argument of compile_forward_sampling_function. When a SharedVariable is found while walking the graph, it looks up the entry in constant_data, it it finds an entry, it checks whether the values in the dictionary match the values of the SharedVariable at run time. If they match, the SharedVariable is deemed not volatile, if they don't match, the SharedVariable is considered volatile.
To check if a dimension's coordinates changed, sample_posterior_predictive compares the model's coordinates to those found in the supplied trace (must be an InferenceData or xarray.Dataset to have this information). If the coordinates did not change, then the dimension name is added to the constant_coords set. Then, when compile_forward_sampling_function finds the dimension length's shared variable, it tries to see if its name is in constant_coords. If it is, then the dimension is not deemed volatile, if it isn't it is considered volatile.

Checklist

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Major / Breaking Changes

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Bugfixes / New features

• sample_posterior_predictive, when supplied with an InferenceData object, properly identifies if a MutableData or mutable dimension has changed between a call to pymc.sample and pymc.sample_posterior_predictive. If they have, then the descendant random variables are resampled, if they have not changed, then the descendant random variables are taken from the InferenceData.posterior.

Docs / Maintenance

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