How to use the airtest.aircv.get_resolution function in airtest

def _resize_image(self, image, screen, resize_method):
        """模板匹配中，将输入的截图适配成 等待模板匹配的截图."""
        # 未记录录制分辨率，跳过
        if not self.resolution:
            return image
        screen_resolution = aircv.get_resolution(screen)
        # 如果分辨率一致，则不需要进行im_search的适配:
        if tuple(self.resolution) == tuple(screen_resolution) or resize_method is None:
            return image
        if isinstance(resize_method, types.MethodType):
            resize_method = resize_method.__func__
        # 分辨率不一致则进行适配，默认使用cocos_min_strategy:
        h, w = image.shape[:2]
        w_re, h_re = resize_method(w, h, self.resolution, screen_resolution)
        # 确保w_re和h_re > 0, 至少有1个像素:
        w_re, h_re = max(1, w_re), max(1, h_re)
        # 调试代码: 输出调试信息.
        G.LOGGING.debug("resize: (%s, %s)->(%s, %s), resolution: %s=>%s" % (
                        w, h, w_re, h_re, self.resolution, screen_resolution))
        # 进行图片缩放:
        image = cv2.resize(image, (w_re, h_re))
        return image

threshold: default is None
        interval: sleep interval before next attempt to find the image template
        intervalfunc: function that is executed after unsuccessful attempt to find the image template

    Raises:
        TargetNotFoundError: when image template is not found in screenshot

    Returns:
        TargetNotFoundError if image template not found, otherwise returns the position where the image template has
        been found in screenshot

    """
    start_time = time.time()
    while True:
        screen = driver.screenshot()
        query.resolution = get_resolution(screen)
        if screen is None:
            print("Screen is None, may be locked")
        else:
            if threshold:
                query.threshold = threshold
            match_pos = query.match_in(screen)
            if match_pos:
                try_log_screen(screen)
                return match_pos

        if intervalfunc is not None:
            intervalfunc()

        # 超时则raise，未超时则进行下次循环:
        if (time.time() - start_time) > timeout:
            try_log_screen(screen)

args.append({
                "key": key,
                "value": value,
            })
        for k, arg in enumerate(args):
            value = arg["value"]
            if isinstance(value, dict) and value.get("__class__") == "Template":
                if self.export_dir:  # all relative path
                    image_path = value['filename']
                    if not os.path.isfile(os.path.join(self.script_root, image_path)):
                        shutil.copy(value['_filepath'], self.script_root)  # copy image used by using statement
                else:
                    image_path = os.path.abspath(value['_filepath'] or value['filename'])
                arg["image"] = image_path
                crop_img = imread(value['_filepath'] or value['filename'])
                arg["resolution"] = get_resolution(crop_img)
        return code

def _get_left_up_offset(self):
        window_pos = self.get_window_position()
        window_size = self.get_window_size()
        mouse = Controller()
        screen = self.screenshot()
        screen_size = get_resolution(screen)
        offset = window_size["width"] - screen_size[0], window_size["height"] - screen_size[1]
        pos = (int(offset[0] / 2 + window_pos['x']), int(offset[1] + window_pos['y'] - offset[0] / 2))
        return pos

def _find_keypoint_result_in_predict_area(self, func, image, screen):
        if not self.record_pos:
            return None
        # calc predict area in screen
        image_wh, screen_resolution = aircv.get_resolution(image), aircv.get_resolution(screen)
        xmin, ymin, xmax, ymax = Predictor.get_predict_area(self.record_pos, image_wh, self.resolution, screen_resolution)
        # crop predict image from screen
        predict_area = aircv.crop_image(screen, (xmin, ymin, xmax, ymax))
        if not predict_area.any():
            return None
        # keypoint matching in predicted area:
        ret_in_area = func(image, predict_area, threshold=self.threshold, rgb=self.rgb)
        # calc cv ret if found
        if not ret_in_area:
            return None
        ret = deepcopy(ret_in_area)
        if "rectangle" in ret:
            for idx, item in enumerate(ret["rectangle"]):
                ret["rectangle"][idx] = (item[0] + xmin, item[1] + ymin)
        ret["result"] = (ret_in_area["result"][0] + xmin, ret_in_area["result"][1] + ymin)
        return ret