def latent_code_visualize(self, iterations):
        def to_float(item):
            if isinstance(item, torch.Tensor):
                item = item.detach().cpu().numpy()
            if isinstance(item, np.ndarray):
                if len(item.reshape(-1)) == 1:
                    item = float(item)
            return item
        with torch.no_grad():

            """latent codes"""  # !!!!! TD: add a separate function, merge with plot_clusters ????t-????????(t-SNE)???

            vis_dicts = {}
            for phase, co_loader, writer in [
                ['train', self.data_loader, self.writer]]:

                vis_dict = None
                # ??key????list
                for t, tcl_data in enumerate(co_loader):
                    vis_codes = {}
                    x = tcl_data["x"].to(self.data_device)  # 100,63,70
                    cond = tcl_data["cond"].to(self.data_device)  # 100, 663, 70
                    vis_codes['style_code'] = self.graph.generate_z(x, cond)
                    vis_codes['meta'] = tcl_data['meta']
                    vis_codes['label'] = tcl_data['label']
                    if vis_dict is None:
                        vis_dict = {}
                        for key, value in vis_codes.items():
                            vis_dict[key] = [value]
                    else:
                        for key, value in vis_codes.items():
                            vis_dict[key].append(value)

                for key, value in vis_dict.items():
                    if key == "meta" or key == "label":
                        num = len(value)
                        vis_dict[key] = [to_float(item) for i in range(num) for item in value[i]]
                    else:
                        vis_dict[key] = torch.cat(vis_dict[key], 0)
                        vis_dict[key] = vis_dict[key].cpu().numpy()
                        vis_dict[key] = to_float(vis_dict[key].reshape(vis_dict[key].shape[0], -1))

                vis_dicts[phase] = vis_dict

            writers = {"train": self.writer}
            from visualization.latent_plot_utils import get_all_plots
            get_all_plots(vis_dicts, os.path.join(self.log_dir, '%08d' % (iterations + 1)), writers, iterations + 1)