Complete deterministic training by adding CUBLAS workspace config and DataLoader worker seeding

The Trainer already sets deterministic=True (train_model.py#L319), but two things are still missing for runs to be truly reproducible with the same seed.

1. CUBLAS_WORKSPACE_CONFIG is not set Without this env var, cuBLAS matrix multiplications on GPU can still be non-deterministic even with deterministic=True. PyTorch documents this explicitly. Fix is one line at the top of main():

os.environ.setdefault("CUBLAS_WORKSPACE_CONFIG", ":4096:8")

2. No worker_init_fn on the DataLoaders With num_workers=16, each worker gets a copy of the random state. Without a worker_init_fn, workers across runs can end up with correlated seeds. PyTorch recommends seeding each worker independently:

def _worker_init_fn(worker_id):
    worker_seed = torch.initial_seed() % 2**32
    np.random.seed(worker_seed)
    random.seed(worker_seed)

Then pass worker_init_fn=_worker_init_fn to all three DataLoaders in WeatherDataModule.