code: def norm(self, img, render_factor=32, render_base=16):
      target_size = render_factor * render_base
      img = img.resize(
          (target_size, target_size), resample=Image.BILINEAR
      )
  
      img = np.array(img).transpose([2, 0, 1]).astype("float32") / 255.0
  
      img_mean = np.array([0.485, 0.456, 0.406]).reshape((3, 1, 1))
      img_std = np.array([0.229, 0.224, 0.225]).reshape((3, 1, 1))
  
      img -= img_mean
      img /= img_std
      return img.astype("float32")
  
imports: import glob
  import os
  import cv2
  import numpy
  import paddle
  import paddle.nn
  from PIL import Image
  from tqdm import tqdm
  from .None import utils
  from .base_module import build_model
  from paddlehub.module.module import moduleinfo
  from paddlehub.module.module import serving
label_desc: Use image node_id 2 for calling the DeOldifyPredictor obj's underlying member method code with example usage: obj.norm(img, render_factor, render_base) and returns: img