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 postprocess(self, inputs, result):
      # postprocess output of predictor
      np_bboxes = result["bboxes"]
      if np_bboxes.shape[0] <= 0:
          print("[WARNNING] No object detected and tracked.")
          result = {
              "bboxes": np.zeros([0, 6]),
              "cts": None,
              "tracking": None,
          }
          return result
      result = {k: v for k, v in result.items() if v is not None}
      return result
  
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 4 for calling the CenterTrack obj's underlying member method code with example usage: obj.postprocess(inputs, result) and returns: result, result