class_docstr: Sample or Padding frame skeleton feature.
  Args:
      window_size (int): Temporal size of skeleton feature.
      random_pad (bool): Whether do random padding when frame length < window size. Default: False.
class_docstr_tok: ["Sample","or","Padding","frame","skeleton","feature",".","Args",":","window_size","(","int",")",":","Temporal","size","of","skeleton","feature",".","random_pad","(","bool",")",":","Whether","do","random","padding","when","frame","length","<","window","size",".","Default",":","False","."]
code: def __init__(self, window_size=100, random_pad=False):
      self.window_size = window_size
      self.random_pad = random_pad
  
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 1 to create a new AutoPadding object from inherited base classes: object with example: obj = AutoPadding(window_size, random_pad)