Issue in reproducibility

I am unable to get the exact result after training a ttc model on my data (synthetic data), even after setting every possible seed:
```{python}
torch.manual_seed(42)
random.seed(42)
np.random.seed(42)

torch.use_deterministic_algorithms(True)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(42)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False

pytorch_lightning.seed_everything(42)
```

For full reproducibility:

```{bash}
git clone -b main https://github.com/mateomorin/ttc-training.git
# uncomment last test of test_reproducibility
# potentialy change your data path if you do not have access to bucket projet-ape
uv run pytest # should have 2 passed, 1 failed and potential warnings
```

Clone the main branch of mateomorin/ttc-training, uncomment the last test in test_reproducibility, and run uv run pytest with the provided seeding setup. Compare the reproducibility test results and warnings; done should mean the test no longer demonstrates differing results after training on the synthetic data.

Written by the indexing model from the issue text.