fix: properly seed DataLoader workers for reproducibility

[GiGiKoneti]

Describe your changes

This PR addresses the DataLoader worker seeding part of #265 (and #267). As suggested by the maintainers in #267, PyTorch Lightning natively handles proper DataLoaders worker seeding (worker_init_fn) automatically if we simply pass workers=True to seed_everything.

• Modified neural_lam/train_model.py to use seed.seed_everything(args.seed, workers=True). This resolves the issue where multiple workers could end up with correlated random states.

(Note: The CUBLAS_WORKSPACE_CONFIG environment variable from the original issue is omitted from this PR, adhering to the maintainer's decision in #267 that imposing environment variables for maximum determinism falls outside the scope of neural-lam and is better left to user execution scripts.)

Issue Link

Resolves #265

Type of change

• 🐛 Bug fix (non-breaking change that fixes an issue)
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