code: def smooth(self, current_keypoint, threshold, index):
      distance = np.sqrt(
          np.square(
              (current_keypoint[0] - self.x_prev_hat[index][0])
              / self.image_width
          )
          + np.square(
              (current_keypoint[1] - self.x_prev_hat[index][1])
              / self.image_height
          )
      )
      if distance < threshold:
          result = self.x_prev_hat[index]
      else:
          result = self.smooth_func(
              current_keypoint, self.x_prev_hat[index], index
          )
  
      return result
  
imports: import os
  import json
  import cv2
  import math
  import numpy
  import paddle
  import yaml
  from det_keypoint_unite_utils import argsparser
  from preprocess import decode_image
  from infer import Detector, DetectorPicoDet, PredictConfig, print_arguments, get_test_images, bench_log
  from keypoint_infer import KeyPointDetector, PredictConfig_KeyPoint
  from visualize import visualize_pose
  from benchmark_utils import PaddleInferBenchmark
  from utils import get_current_memory_mb
  from keypoint_postprocess import translate_to_ori_images
label_desc: Use image node_id 3 for calling the KeypointSmoothing obj's underlying member method code with example usage: obj.smooth(current_keypoint, threshold, index) and returns: result