code: def imgs_transform(
      imgs,
      mode,
      seg_num,
      seglen,
      short_size,
      target_size,
      img_mean,
      img_std,
      name="",
  ):
      imgs = group_scale(imgs, short_size)
  
      np_imgs = np.array(
          [np.array(img).astype("float32") for img in imgs]
      )  # dhwc
      np_imgs = group_center_crop(np_imgs, target_size)
      np_imgs = np_imgs.transpose(0, 3, 1, 2) / 255  # dchw
      np_imgs -= img_mean
      np_imgs /= img_std
  
      return np_imgs
  
file_path: PaddleHub/modules/video/classification/videotag_tsn_lstm/resource/reader/kinetics_reader.py
imports: import sys
  import random
  import functools
  import logging
  import paddle
  import cv2
  import numpy
  from PIL import Image
  from .reader_utils import DataReader
label_desc: Use image node_id 1 for calling a global function with example usage: imgs_transform(imgs, mode, seg_num, seglen, short_size, target_size, img_mean, img_std, name) and returns: np_imgs