Synthetic diffusion MGN example.

[YohannDudouit]

Summary
This PR adds a pure-Torch MGN-like graph diffusion validation workflow for AMS homogeneous graph surrogates. It trains a small synthetic diffusion model, exports an AMS-compatible TorchScript model, writes runtime fixtures, and validates C++ AMS inference against Python TorchScript reference output.

What Changed

• Added generate_mgn_graph_diffusion.py with separate modes:
  • feasibility: eager/script/reload checks on dynamic graph sizes
  • train: trains the synthetic graph diffusion surrogate and saves checkpoint/metrics
  • fixtures: exports TorchScript and writes runtime fixture binaries plus fixtures.json
• Added test_graph_mgn_surrogate.cpp to load runtime fixtures and compare AMS node:delta_u output against Python TorchScript references for N=24 and N=73.
• Added MGN_DIFFUSION_* CTest workflow entries with dependency ordering:
  • feasibility → train → fixtures → AMS parity

Testing

python3 -m py_compile tests/AMSlib/models/generate_mgn_graph_diffusion.py
git diff --check
ctest --test-dir build -R MGN_DIFFUSION -j 8 --output-on-failure

Normal CTest can skip this training workflow with:

ctest --test-dir build -LE MGN_DIFFUSION --output-on-failure

Note: This PR is built on top of #194 and therefore include the graph construction and unit tests, until #194 is merged.