def METHOD_NAME():
    args = argsparser()
    config_parser = ConfigParser(args)
    args = config_parser.parser()
    random.seed(args.seed)
    np.random.seed(args.seed)
    paddle.seed(args.seed)
    paddle.device.set_device(args.device)
    class_name = args.category
    assert class_name in mvtec.CLASS_NAMES
    print("Testing model for {}".format(class_name))
    # build model
    model = get_model(args.method)(arch=args.backbone,
                                   pretrained=False,
                                   k=args.k,
                                   method=args.method)
    model.eval()
    state = paddle.load(args.model_path)
    model.model.set_dict(state["params"])
    model.load(state["stats"])
    model.eval()
    # build data
    MVTecDataset = mvtec.MVTecDataset(is_predict=True)
    transform_x = MVTecDataset.get_transform_x()
    x = Image.open(args.img_path).convert('RGB')
    x = transform_x(x).unsqueeze(0)
    predict(args, model, x)