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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docstr: Args:
      repeats (int): repeat number for prediction
  Returns:
      results (dict): 
docstr_tok: ["Args",":","repeats","(","int",")",":","repeat","number","for","prediction","Returns",":","results","(","dict",")",":"]
code: def predict(self, repeats=1):
      """
      Args:
          repeats (int): repeat number for prediction
      Returns:
          results (dict):
      """
      # model prediction
      output_names = self.predictor.get_output_names()
      for i in range(repeats):
          self.predictor.run()
          output_tensor = self.predictor.get_output_handle(
              output_names[0]
          )
          np_output = output_tensor.copy_to_cpu()
      result = dict(output=np_output)
      return result
  
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 3 for calling the SkeletonActionRecognizer obj's underlying member method code with example usage: obj.predict(repeats) and returns: result