code: def run(model_path, image_path, output):
      pred_config = PredictConfig(
          model=Model(),
          session_init=SmartInit(model_path),
          input_names=["image"],
          output_names=["output" + str(k) for k in range(1, 7)],
      )
      predictor = OfflinePredictor(pred_config)
      im = cv2.imread(image_path)
      assert im is not None
      im = cv2.resize(
          im, (im.shape[1] // 16 * 16, im.shape[0] // 16 * 16)
      )[None, :, :, :].astype("float32")
      outputs = predictor(im)
      if output is None:
          for k in range(6):
              pred = outputs[k][0]
              cv2.imwrite(
                  "out{}.png".format(
                      "-fused" if k == 5 else str(k + 1)
                  ),
                  pred * 255,
              )
          logger.info("Results saved to out*.png")
      else:
          pred = outputs[5][0]
          cv2.imwrite(output, pred * 255)
  
imports: import argparse
  import numpy
  import os
  import cv2
  import tensorflow
  from tensorpack import *
  from tensorpack.dataflow import dataset
  from tensorpack.tfutils import gradproc, optimizer
  from tensorpack.tfutils.summary import add_moving_summary, add_param_summary
  from tensorpack.utils.gpu import get_num_gpu
  from tensorpack.utils import logger
label_desc: Use image node_id 6 for calling a global function with example usage: run(model_path, image_path, output) without return types