Hi @soulitzer, first of all thanks for help me!
Mmmm, I think the correct form to replicate this is trying to train two different model versions. So I train a v1 of my model and deploy it. After some time I train another version with more data and deploy it again, but I would like that the feature vector would be the same for the exact same image. I do this example:
train_losses, train_metrics, val_losses, val_metrics = fit(3, model, opt, loss_fn, train_dl, valid_dl, metric)

train_losses_1, train_metrics_1, val_losses_1, val_metrics_1 = fit(5, model_1, opt_1, loss_fn_1, train_dl, valid_dl, metric_1)

And then when I do the check:
img = next(iter(test_dl))[0]
_, feature1 = model(img)
_, feature2 = model_1(img)
print(torch.allclose(feature1, feature2)) #<--- False