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
      threshold (float): The threshold of score for visualization
      window_size(int): Temporal size of skeleton feature.
      random_pad (bool): Whether do random padding when frame length < window_size.
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code: def predict_skeleton(
      self, skeleton_list, run_benchmark=False, repeats=1
  ):
      results = []
      for i, skeleton in enumerate(skeleton_list):
          if run_benchmark:
              # preprocess
              inputs = self.preprocess(skeleton)  # warmup
              self.det_times.preprocess_time_s.start()
              inputs = self.preprocess(skeleton)
              self.det_times.preprocess_time_s.end()
  
              # model prediction
              result = self.predict(repeats=repeats)  # warmup
              self.det_times.inference_time_s.start()
              result = self.predict(repeats=repeats)
              self.det_times.inference_time_s.end(repeats=repeats)
  
              # postprocess
              result_warmup = self.postprocess(inputs, result)  # warmup
              self.det_times.postprocess_time_s.start()
              result = self.postprocess(inputs, result)
              self.det_times.postprocess_time_s.end()
              self.det_times.img_num += len(skeleton)
  
              cm, gm, gu = get_current_memory_mb()
              self.cpu_mem += cm
              self.gpu_mem += gm
              self.gpu_util += gu
          else:
              # preprocess
              self.det_times.preprocess_time_s.start()
              inputs = self.preprocess(skeleton)
              self.det_times.preprocess_time_s.end()
  
              # model prediction
              self.det_times.inference_time_s.start()
              result = self.predict()
              self.det_times.inference_time_s.end()
  
              # postprocess
              self.det_times.postprocess_time_s.start()
              result = self.postprocess(inputs, result)
              self.det_times.postprocess_time_s.end()
              self.det_times.img_num += len(skeleton)
  
          results.append(result)
      return results
  
imports: import os
  import yaml
  import glob
  import cv2
  import numpy
  import math
  import paddle
  import sys
  from paddle.inference import Config, create_predictor
  from python.utils import argsparser, Timer, get_current_memory_mb
  from python.benchmark_utils import PaddleInferBenchmark
  from python.infer import Detector, print_arguments
  from attr_infer import AttrDetector
label_desc: Use image node_id 4 for calling the SkeletonActionRecognizer obj's underlying member method code with example usage: obj.predict_skeleton(skeleton_list, run_benchmark, repeats) and returns: results