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
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docstr: frame_list: list, frame list
  return: list
docstr_tok: ["frame_list",":","list",",","frame","list","return",":","list"]
code: def preprocess_frames(self, frame_list):
      """
      frame_list: list, frame list
      return: list
      """
  
      results = {}
      results["frames_len"] = len(frame_list)
      results["imgs"] = frame_list
  
      img_mean = [0.485, 0.456, 0.406]
      img_std = [0.229, 0.224, 0.225]
      ops = [
          CenterCrop(self.target_size),
          Image2Array(),
          Normalization(img_mean, img_std),
      ]
      for op in ops:
          results = op(results)
  
      res = np.expand_dims(results["imgs"], axis=0).copy()
      return [res]
  
imports: import os
  import yaml
  import glob
  import cv2
  import numpy
  import math
  import paddle
  import sys
  import paddle.nn.functional
  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 video_action_preprocess import VideoDecoder, Sampler, Scale, CenterCrop, Normalization, Image2Array
label_desc: Use image node_id 6 for calling the VideoActionRecognizer obj's underlying member method code with example usage: obj.preprocess_frames(frame_list) and returns: list