How to use the labelme.utils.lblsave function in labelme

for shape in sorted(data['shapes'], key=lambda x: x['label']):
        label_name = shape['label']
        if label_name in label_name_to_value:
            label_value = label_name_to_value[label_name]
        else:
            label_value = len(label_name_to_value)
            label_name_to_value[label_name] = label_value
    lbl = utils.shapes_to_label(img.shape, data['shapes'], label_name_to_value)

    label_names = [None] * (max(label_name_to_value.values()) + 1)
    for name, value in label_name_to_value.items():
        label_names[value] = name
    lbl_viz = utils.draw_label(lbl, img, label_names)

    PIL.Image.fromarray(img).save(osp.join(out_dir, 'img.png'))
    utils.lblsave(osp.join(out_dir, 'label.png'), lbl)
    PIL.Image.fromarray(lbl_viz).save(osp.join(out_dir, 'label_viz.png'))

    with open(osp.join(out_dir, 'label_names.txt'), 'w') as f:
        for lbl_name in label_names:
            f.write(lbl_name + '\n')

    print('Saved to: %s' % out_dir)

'SegmentationClassVisualization',
                    base + '.jpg',
                )

            data = json.load(f)

            img_file = osp.join(osp.dirname(label_file), data['imagePath'])
            img = np.asarray(PIL.Image.open(img_file))
            PIL.Image.fromarray(img).save(out_img_file)

            lbl = labelme.utils.shapes_to_label(
                img_shape=img.shape,
                shapes=data['shapes'],
                label_name_to_value=class_name_to_id,
            )
            labelme.utils.lblsave(out_png_file, lbl)

            np.save(out_lbl_file, lbl)

            if not args.noviz:
                viz = imgviz.label2rgb(
                    label=lbl,
                    img=imgviz.rgb2gray(img),
                    font_size=15,
                    label_names=class_names,
                    loc='rb',
                )
                imgviz.io.imsave(out_viz_file, viz)

# class label
            labelme.utils.lblsave(out_clsp_file, cls)
            np.save(out_cls_file, cls)
            if not args.noviz:
                clsv = imgviz.label2rgb(
                    label=cls,
                    img=imgviz.rgb2gray(img),
                    label_names=class_names,
                    font_size=15,
                    loc='rb',
                )
                imgviz.io.imsave(out_clsv_file, clsv)

            # instance label
            labelme.utils.lblsave(out_insp_file, ins)
            np.save(out_ins_file, ins)
            if not args.noviz:
                instance_ids = np.unique(ins)
                instance_names = [str(i) for i in range(max(instance_ids) + 1)]
                insv = imgviz.label2rgb(
                    label=ins,
                    img=imgviz.rgb2gray(img),
                    label_names=instance_names,
                    font_size=15,
                    loc='rb',
                )
                imgviz.io.imsave(out_insv_file, insv)

if label_name in label_name_to_value:
            label_value = label_name_to_value[label_name]
        else:
            label_value = len(label_name_to_value)
            label_name_to_value[label_name] = label_value
    lbl = utils.shapes_to_label(img.shape, data['shapes'], label_name_to_value)

    label_names = [None] * (max(label_name_to_value.values()) + 1)
    for name, value in label_name_to_value.items():
        label_names[value] = name
    lbl_viz = imgviz.label2rgb(
        label=lbl, img=imgviz.rgb2gray(img), label_names=label_names, loc='rb'
    )

    PIL.Image.fromarray(img).save(osp.join(out_dir, 'img.png'))
    utils.lblsave(osp.join(out_dir, 'label.png'), lbl)
    PIL.Image.fromarray(lbl_viz).save(osp.join(out_dir, 'label_viz.png'))

    with open(osp.join(out_dir, 'label_names.txt'), 'w') as f:
        for lbl_name in label_names:
            f.write(lbl_name + '\n')

    logger.warning('info.yaml is being replaced by label_names.txt')
    info = dict(label_names=label_names)
    with open(osp.join(out_dir, 'info.yaml'), 'w') as f:
        yaml.safe_dump(info, f, default_flow_style=False)

    logger.info('Saved to: {}'.format(out_dir))

data = json.load(f)

            img_file = osp.join(osp.dirname(label_file), data['imagePath'])
            img = np.asarray(PIL.Image.open(img_file))
            PIL.Image.fromarray(img).save(out_img_file)

            cls, ins = labelme.utils.shapes_to_label(
                img_shape=img.shape,
                shapes=data['shapes'],
                label_name_to_value=class_name_to_id,
                type='instance',
            )
            ins[cls == -1] = 0  # ignore it.

            # class label
            labelme.utils.lblsave(out_clsp_file, cls)
            np.save(out_cls_file, cls)
            if not args.noviz:
                clsv = imgviz.label2rgb(
                    label=cls,
                    img=imgviz.rgb2gray(img),
                    label_names=class_names,
                    font_size=15,
                    loc='rb',
                )
                imgviz.io.imsave(out_clsv_file, clsv)

            # instance label
            labelme.utils.lblsave(out_insp_file, ins)
            np.save(out_ins_file, ins)
            if not args.noviz:
                instance_ids = np.unique(ins)