code: def run(self, image):
      sem = Image.fromarray(image).convert("L")
      sem = sem.resize(
          (self.gen_cfg.crop_size, self.gen_cfg.crop_size),
          Image.NEAREST,
      )
      sem = np.array(sem).astype("float32")
      sem = paddle.to_tensor(sem)
      sem = sem.reshape(
          [1, 1, self.gen_cfg.crop_size, self.gen_cfg.crop_size]
      )
  
      one_hot = data_onehot_pro(sem, self.gen_cfg)
      predicted = self.gen(one_hot)
      pic = (
          predicted.numpy()[0]
          .reshape((3, 256, 256))
          .transpose((1, 2, 0))
      )
      pic = ((pic + 1.0) / 2.0 * 255).astype("uint8")
  
      return pic
  
file_path: PaddleHub/modules/image/Image_gan/gan/photopen/model.py
imports: import os
  import cv2
  import numpy
  import paddle
  from PIL import Image
  from PIL import ImageOps
  from ppgan.models.generators import SPADEGenerator
  from ppgan.utils.filesystem import load
  from ppgan.utils.photopen import data_onehot_pro
label_desc: Use image node_id 2 for calling the PhotoPenPredictor obj's underlying member method code with example usage: obj.run(image) and returns: pic