code: def ema_filter(self, x_cur, x_pre, index):
      x_cur_hat = self.exponential_smoothing(x_cur, x_pre)
      self.x_prev_hat[index] = x_cur_hat
      return x_cur_hat
  
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 5 for calling the KeypointSmoothing obj's underlying member method code with example usage: obj.ema_filter(x_cur, x_pre, index) and returns: x_cur_hat