Fix loss_dict problem

    _loss_step = 0 #internal

    # size = len(ds.indexes)

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

    loss_logging = []

    # losses = torch.zeros((size,))

    # previous_mean_losses = [0]

    # previous_mean_loss = 0

                    else:

                        c = stack_conds(batch.cond).to(devices.device, non_blocking=pin_memory)

                    loss = shared.sd_model(x, c)[0] / gradient_step

                    loss_logging.append(loss.item())

                    del x

                    del c

                if shared.opts.training_enable_tensorboard:

                    epoch_num = hypernetwork.step // len(ds)

                    epoch_step = hypernetwork.step - (epoch_num * len(ds)) + 1

                    mean_loss = sum(sum(x) for x in loss_dict.values()) / sum(len(x) for x in loss_dict.values())

                    mean_loss = sum(loss_logging) / len(loss_logging)

                    textual_inversion.tensorboard_add(tensorboard_writer, loss=mean_loss, global_step=hypernetwork.step, step=epoch_step, learn_rate=scheduler.learn_rate, epoch_num=epoch_num)

                textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, steps_per_epoch, {