Fixed #86 : Added an end-to-end DP Transformer training example #102
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This PR adds a comprehensive, self-contained example of training a Transformer decoder using the JAX Privacy Core API (Issue #86).
Key Features:
Architecture: Implements a Transformer Decoder in Flax Linen for character-level language modeling.
Bare-Metal Core API: Explicitly uses jax_privacy.clipped_grad and noise_addition.gaussian_privatizer without high-level wrappers.
Configurability: Fully integrated with ABSL flags for epsilon, batch size, learning rate, and clipping norm.
Verification: Confirmed model convergence (Loss decreased from ~2.68 to ~1.87 in 50 steps).
Quality Checks:
Pylint Score: 9.91/10
Flake8: Passed
Fixes #86