code: def group_center_crop(np_imgs, target_size):
      d, h, w, c = np_imgs.shape
      th, tw = target_size, target_size
      assert (w >= target_size) and (
          h >= target_size
      ), "image width({}) and height({}) should be larger than crop size".format(
          w, h, target_size
      )
  
      h_off = int(round((h - th) / 2.0))
      w_off = int(round((w - tw) / 2.0))
  
      img_crop = np_imgs[
          :, h_off : h_off + target_size, w_off : w_off + target_size, :
      ]
      return img_crop
  
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 2 for calling a global function with example usage: group_center_crop(np_imgs, target_size) and returns: img_crop