SVI methods for Bayesian neural networks to deal with trainable sigma

[JaehwanJeon]

[Add SVI trainer for Bayesian neural networks]

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Description

1. Developed SVITrainer to enable generic SVI (src/UQpy/scientific_machine_learning/trainers/SVITrainer.py).

Related Issue

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If fixing a bug, there should be an issue describing it with steps to reproduce
Please link to the issue here: [https://github.com//issues/273]

Motivation and Context

The existing Bayes-by-backprop trainer in UQpy is tightly coupled to its custom mean-field implementation, which makes it hard to experiment SVI which allows us to handle aleatoric uncertainty. This PR introduces a SVI trainer that accepts Bayesian neural networks built with UQpy can be used to update inherent randomness.

How Has This Been Tested?

• Executed "docs/code/scientific_machine_learning/bbb_trainer/svitrainer_trig_sigma.py" to deal with trainable sigma.

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Types of changes

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• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to change)

Checklist:

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• My code follows the code style of this project.
• My change requires a change to the documentation.
• I have updated the documentation accordingly.
• I have read the CONTRIBUTING document.
• I have added tests to cover my changes.
• All new and existing tests passed.