code: def run_image(model, sess_init, inputs):
      pred_config = PredictConfig(
          model=model,
          session_init=sess_init,
          input_names=["input"],
          output_names=["output"],
      )
      predictor = OfflinePredictor(pred_config)
      meta = dataset.ILSVRCMeta()
      words = meta.get_synset_words_1000()
  
      transformers = imgaug.AugmentorList(
          fbresnet_augmentor(isTrain=False)
      )
      for f in inputs:
          assert os.path.isfile(f), f
          img = cv2.imread(f).astype("float32")
          assert img is not None
  
          img = transformers.augment(img)[np.newaxis, :, :, :]
          outputs = predictor(img)[0]
          prob = outputs[0]
          ret = prob.argsort()[-10:][::-1]
  
          names = [words[i] for i in ret]
          print(f + ":")
          print(list(zip(names, prob[ret])))
  
imports: import argparse
  import numpy
  import os
  import sys
  import cv2
  import tensorflow
  from tensorpack import *
  from tensorpack.dataflow import dataset
  from tensorpack.tfutils.sessinit import SmartInit
  from tensorpack.tfutils.summary import add_param_summary
  from tensorpack.tfutils.varreplace import remap_variables
  from tensorpack.utils.gpu import get_num_gpu
  from dorefa import get_dorefa, ternarize
  from imagenet_utils import ImageNetModel, eval_classification, fbresnet_augmentor, get_imagenet_dataflow
label_desc: Use image node_id 3 for calling a global function with example usage: run_image(model, sess_init, inputs) without return types