Fixing some issues when we have a vector-valued time-varying parameter in the state-space class + started working on multivariate time series in the structural class.

[sangrey]

This PR fixes two issues in the PytensorRepresentation class that affect how time-varying parameters are validated and reshaped.

Changes

1. Corrected a bug in shape validation:
The check for mismatched static shape of time-varying inputs (when X.ndim == 2) incorrectly compared X.shape[1:] instead of shape[1:].  X.shape is a symbolic tensor. 

2.  X has two dimensions if it is time-varying & vector valued or if is not-tie-varying and not vector valued.  I added a check to ensure that we were in case two before adding additional dimensions. 

I also brought in a bunch of changes to structural.py to get them to work with a multivariate time series.

1. Generalized __init__ methods to accept endog_names and set k_endog dynamically.

2. Added ObsIntercept component for flexible, time-varying observation intercepts.

3. Extended populate_component_properties and make_symbolic_graph methods to handle multivariate shapes.

4. Parameter shapes, constraints, and dimensions now adapt to multivariate settings.

See the discussion on Discourse here: https://discourse.pymc.io/t/how-do-i-set-a-time-varying-observation-intercept-in-a-state-space-model/17000/8

[jessegrabowski]

Thanks for this @sangrey !

It would probably be best if we can split this up into three PRs: one for the bug fix, one for the ObservationIntercept component, and one for the k_endog argument in the components. Are you willing to do that? No is a valid answer :)

[sangrey]

Apologies for not getting back to you sooner,. I've been traveling, and busier than I expected beyond that. It could be a bit before I would get the chance to split this up. Also, it looks like you just merged a multivariate state-space model, correct?