class_docstr: Args:
      model_dir (str): root path of model.pdiparams, model.pdmodel and infer_cfg.yml
      device (str): Choose the device you want to run, it can be: CPU/GPU/XPU/NPU, default is CPU
      run_mode (str): mode of running(paddle/trt_fp32/trt_fp16)
      batch_size (int): size of pre batch in inference
      trt_min_shape (int): min shape for dynamic shape in trt
      trt_max_shape (int): max shape for dynamic shape in trt
      trt_opt_shape (int): opt shape for dynamic shape in trt
      trt_calib_mode (bool): If the model is produced by TRT offline quantitative
          calibration, trt_calib_mode need to set True
      cpu_threads (int): cpu threads
      enable_mkldnn (bool): whether to open MKLDNN
      output_dir (string): The path of output, default as 'output'
      threshold (float): Score threshold of the detected bbox, default as 0.5
      save_images (bool): Whether to save visualization image results, default as False
      save_mot_txts (bool): Whether to save tracking results (txt), default as False
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code: def tracking(self, inputs, det_results):
      result = self.centertrack_post_process(
          det_results, inputs, self.tracker.out_thresh
      )
      online_targets = self.tracker.update(result)
  
      online_tlwhs, online_scores, online_ids = [], [], []
      for t in online_targets:
          bbox = t["bbox"]
          tlwh = [
              bbox[0],
              bbox[1],
              bbox[2] - bbox[0],
              bbox[3] - bbox[1],
          ]
          tscore = float(t["score"])
          tid = int(t["tracking_id"])
          if tlwh[2] * tlwh[3] > 0:
              online_tlwhs.append(tlwh)
              online_ids.append(tid)
              online_scores.append(tscore)
      return online_tlwhs, online_scores, online_ids
  
imports: import os
  import copy
  import math
  import time
  import yaml
  import cv2
  import numpy
  from collections import defaultdict
  import paddle
  from benchmark_utils import PaddleInferBenchmark
  from utils import gaussian_radius, gaussian2D, draw_umich_gaussian
  from preprocess import preprocess, decode_image, WarpAffine, NormalizeImage, Permute
  from utils import argsparser, Timer, get_current_memory_mb
  from infer import Detector, get_test_images, print_arguments, bench_log, PredictConfig
  from keypoint_preprocess import get_affine_transform
  import sys
  from pptracking.python.mot import CenterTracker
  from pptracking.python.mot.utils import MOTTimer, write_mot_results
  from pptracking.python.mot.visualize import plot_tracking
label_desc: Use image node_id 6 for calling the CenterTrack obj's underlying member method code with example usage: obj.tracking(inputs, det_results) and returns: online_tlwhs,  online_scores,  online_ids