Enhance the autoupdates macro

[bvdmitri]

This PR reimplements the @autoupdates macro by

• Allowing for more detailed expressions within the autoupdate specification. For example, the specification can include complex functions with constants and multiple latent states.
• Enabling the use of full function definitions with the macro, allowing for dynamic construction of autoupdates.
• Adding a thorough set of tests.
• Improving the documentation.
• Adding a possibility to broadcast a function over a collection of variables

This PR fixes https://github.com/ReactiveBayes/RxInfer.jl/issues/61

[codecov[bot]]

Codecov Report

Attention: Patch coverage is 90.36145% with 24 lines in your changes are missing coverage. Please review.

Project coverage is 84.51%. Comparing base (29061e5) to head (10c6648).

Files	Patch %	Lines
src/inference/autoupdates.jl	90.82%	21 Missing :warning:
src/model/model.jl	66.66%	3 Missing :warning:

Additional details and impacted files

```diff @@ Coverage Diff @@ ## main #306 +/- ## ========================================== + Coverage 83.19% 84.51% +1.32% ========================================== Files 18 18 Lines 1297 1466 +169 ========================================== + Hits 1079 1239 +160 - Misses 218 227 +9 ```

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[wouterwln]

@bvdmitri what if we make the autoupdates macro recursive such as constraints, meta and init? Might be nice to also update prior distributions for variables hidden in some submodel.

[bvdmitri]

I like the idea @wouterwln , I think we can accomplish this in a separate PR, since we really need a generic code for those

for _something_ in submodel 

end

pattern. As far as I understand now its mostly copy-pasted for all macros, lets not do that again. Also, this would require to extend the VarDict, which now looks only at the top-level.

[bvdmitri]

I've also come to realize that to support this feature in submodels, we'll need a more integrated approach with GraphPPL. This is because the autoupdates requires that certain arguments remain unspecified. However, I don't believe we can create a submodel with certain arguments underspecified.