Add LayerNorm support for Vivado

[rianbrooksflynn]

Description

This PR adds support for Layer Normalization using either Keras or PyTorch with the Vivado backend in io_parallel mode.

This implementation uses a lookup table for inverse square root; the inputs to the lookup table follow a logarithmic distribution for better accuracy.

Tests have been added for both Keras and Pytorch parsing.

Credit is due to @Ethan0Jiang and @LostEcho365 (Zhixing Jiang and Dennis Yin) for their Vivado implementation and Keras parsing support; my contributions were making a change to the inverse square root lookup table implementation, implementing PyTorch support, and adding unit tests. (Here's a link to their pre-print.) The original code authors have given permission for their code to be merged into hls4ml.

Linked issue: https://github.com/fastmachinelearning/hls4ml/issues/1109

Type of change

• [x] New feature (non-breaking change which adds functionality)
• [x] A new research paper code implementation

Tests

Two unit tests added: test/pytest/test_layernorm.py and test/pytest/test_layernorm_pytorch.py

Checklist

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

[JanFSchulte]

The pytest failure is the QKeras tests timing out. There's 299 tests being run in that batch, which I guess is too many. Is there a why to reshuffle the batches to avoid the timeout?