statistics for pbar

    size = len(ds.indexes)

    loss_dict = defaultdict(lambda : deque(maxlen = 1024))

    losses = torch.zeros((size,))

    previous_mean_losses = [0]

    previous_mean_loss = 0

    print("Mean loss of {} elements".format(size))

    for i, entries in pbar:

        hypernetwork.step = i + ititial_step

        if len(loss_dict) > 0:

            previous_mean_loss = sum(i[-1] for i in loss_dict.values()) / len(loss_dict)

            previous_mean_losses = [i[-1] for i in loss_dict.values()]

            previous_mean_loss = mean(previous_mean_losses)

        scheduler.apply(optimizer, hypernetwork.step)

        if scheduler.finished:

        if torch.isnan(losses[hypernetwork.step % losses.shape[0]]):

            raise RuntimeError("Loss diverged.")

        pbar.set_description(f"dataset loss: {previous_mean_loss:.7f}")

        if len(previous_mean_losses) > 1:

            std = stdev(previous_mean_losses)

        else:

            std = 0

        dataset_loss_info = f"dataset loss:{mean(previous_mean_losses):.3f}" + u"\u00B1" + f"({std / (len(previous_mean_losses) ** 0.5):.3f})"

        pbar.set_description(dataset_loss_info)

        if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0:

            # Before saving, change name to match current checkpoint.