def METHOD_NAME(image, result, color_map, save_dir=None, weight=0.6):
    """
    Convert predict result to color image, and save added image.
    Args:
        image (str): The path of origin image.
        result (np.ndarray): The predict result of image.
        color_map (list): The color used to save the prediction results.
        save_dir (str): The directory for saving visual image. Default: None.
        weight (float): The image weight of visual image, and the result weight is (1 - weight). Default: 0.6
    Returns:
        vis_result (np.ndarray): If `save_dir` is None, return the visualized result.
    """
    color_map = [color_map[i:i + 3] for i in range(0, len(color_map), 3)]
    color_map = np.array(color_map).astype("uint8")
    # Use OpenCV LUT for color mapping
    c1 = cv2.LUT(result, color_map[:, 0])
    c2 = cv2.LUT(result, color_map[:, 1])
    c3 = cv2.LUT(result, color_map[:, 2])
    pseudo_img = np.dstack((c1, c2, c3))
    im = cv2.imread(image)
    vis_result = cv2.addWeighted(im, weight, pseudo_img, 1 - weight, 0)
    if save_dir is not None:
        if not os.path.exists(save_dir):
            os.makedirs(save_dir)
        image_name = os.path.split(image)[-1]
        out_path = os.path.join(save_dir, image_name)
        cv2.imwrite(out_path, vis_result)
    else:
        return vis_result