code: def apply(model_path, lowres_path="", output_path="."):
      assert os.path.isfile(lowres_path)
      assert os.path.isdir(output_path)
      lr = cv2.imread(lowres_path).astype(np.float32)
      baseline = cv2.resize(
          lr, (0, 0), fx=4, fy=4, interpolation=cv2.INTER_CUBIC
      )
      LR_SIZE_H, LR_SIZE_W = lr.shape[:2]
  
      predict_func = OfflinePredictor(
          PredictConfig(
              model=Model(LR_SIZE_H, LR_SIZE_W),
              session_init=SmartInit(model_path),
              input_names=["Ilr"],
              output_names=["prediction"],
          )
      )
  
      pred = predict_func(lr[None, ...])
      p = np.clip(pred[0][0, ...], 0, 255)
  
      cv2.imwrite(os.path.join(output_path, "predition.png"), p)
      cv2.imwrite(os.path.join(output_path, "baseline.png"), baseline)
  
imports: import argparse
  import numpy
  import os
  import cv2
  import six
  import tensorflow
  from tensorpack import *
  from tensorpack.tfutils.scope_utils import auto_reuse_variable_scope
  from tensorpack.tfutils.summary import add_moving_summary
  from tensorpack.utils import logger
  from tensorpack.utils.gpu import get_num_gpu
  from data_sampler import CenterSquareResize, ImageDataFromZIPFile, ImageDecode, RejectTooSmallImages
  from GAN import GANModelDesc, SeparateGANTrainer
label_desc: Use image node_id 3 for calling a global function with example usage: apply(model_path, lowres_path, output_path) without return types