Add support for parsing simple brevitas layers as part of pytorch models

[JanFSchulte]

This PR adds support for the parsing of simple brevitas layers (QuantLinear, QuantActivation, QuantConv1D, QuantConv2D) to the pytorch parser. More complex models will still have to go through QONNX, but simple cases can be handled easily within the pytorch parser itself. To this end, this PR adds a new quantizer which only propagates the desired precision to the hsl4ml model, as brevitas already provides the quantized tensors which we pick up directly.

Type of change

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• New feature (non-breaking change which adds functionality)

Tests

Tested locally with simple model and added pytests.

Checklist

• I have read the guidelines for contributing.
• I have commented my code, particularly in hard-to-understand areas.
• I have made corresponding changes to the documentation.
• My changes generate no new warnings.
• I have installed and run pre-commit on the files I edited or added.
• I have added tests that prove my fix is effective or that my feature works.

[JanFSchulte]

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[jmitrevs]

Do we need to add brevitas to our test environment docker image? Or can we just add brevitas as a testing dependency? I think that's why the test failed.

[JanFSchulte]

pre-commit.ci autofix

[JanFSchulte]

pre-commit.ci autofix

[JanFSchulte]

pre-commit.ci autofix

[JanFSchulte]

Turns out in the latest version of brevitas there are significant changes to the interface on how to access information about the quantized layers and tensors. I'm not sure right now when I will have time to rework this PR, so I'm converting it to draft for the moment.