Bidirectional RNN layer support for Keras frontend and Vitis backend

[enlupi]

Description

This PR adds support for Bidirectional RNN layers using Keras with the Vitis backend in io_parallel mode. The forward and backward layer can be either LSTM or GRU, and their architecture and independent one from the other.

Type of change

• New feature

Tests

Unit test in test/pytest/test_rnn.py was updated to also check parsing and accuracy for a Bidirectional layer.

Test Configuration:

The new tests are carried out using only Vivado or Vitis backend and io_parallel mode.

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.