added a guard for hypernet training that will stop early if weights are getting no gradients

    scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)

    optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)

    steps_without_grad = 0

    pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)

    for i, entries in pbar:

        hypernetwork.step = i + ititial_step

            losses[hypernetwork.step % losses.shape[0]] = loss.item()

            optimizer.zero_grad()

            weights[0].grad = None

            loss.backward()

            if weights[0].grad is None:

                steps_without_grad += 1

            else:

                steps_without_grad = 0

            assert steps_without_grad < 10, 'no gradient found for the trained weight after backward() for 10 steps in a row; this is a bug; training cannot continue'

            optimizer.step()

        mean_loss = losses.mean()

        if torch.isnan(mean_loss):

            raise RuntimeError("Loss diverged.")