PQuant🌶️ integration

[enlupi]

Description

This draft PR is intended to start a discussion on how to best integrate the PQuant🌶️ library with hls4ml.
The current state of the PR supports layers pruned and quantized using fixed quantizers. Support for high granularity quantization is still in development.

⚠️⚠️ In order for the PQuant layers to be parsed correctly, the code expects the user to run some custom functions that add the quantization parameters as layer attributes (see the test file for examples). In the future these functions should be added to PQuant🌶️. ⚠️⚠️

Type of change

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

Tests

Unit test test/pytest/test_pquant.py was added to test correct parsing of the pruned/quantized layers and that the precision is set correctly.
⚠️⚠️ THE TEST IS CURRENTLY BROKEN! The file needs to be split into two, to test PyTorch and Keras frontend separately ⚠️⚠️

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.