code: def __init__(
      self,
      render_factor: int = 32,
      output_path: int = "output",
      load_checkpoint: str = None,
  ):
      super(DeOldifyPredictor, self).__init__()
      self.model = build_model()
      self.render_factor = render_factor
      self.output = os.path.join(output_path, "DeOldify")
      if not os.path.exists(self.output):
          os.makedirs(self.output)
      if load_checkpoint is not None:
          state_dict = paddle.load(load_checkpoint)
          self.model.load_dict(state_dict)
          print("load custom checkpoint success")
  
      else:
          checkpoint = os.path.join(
              self.directory, "DeOldify_stable.pdparams"
          )
          if not os.path.exists(checkpoint):
              os.system(
                  "wget https://paddlegan.bj.bcebos.com/applications/DeOldify_stable.pdparams -O "
                  + checkpoint
              )
          state_dict = paddle.load(checkpoint)
          self.model.load_dict(state_dict)
          print("load pretrained checkpoint success")
  
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 1 to create a new DeOldifyPredictor object from inherited base classes: nn with example: obj = DeOldifyPredictor(render_factor, output_path, load_checkpoint)