code: def run_test(params, input):
      pred_config = PredictConfig(
          model=Model(),
          session_init=SmartInit(params),
          input_names=["input"],
          output_names=["prob"],
      )
      predict_func = OfflinePredictor(pred_config)
  
      prepro = imgaug.AugmentorList(get_inference_augmentor())
      im = cv2.imread(input).astype("float32")
      im = prepro.augment(im)
      im = np.reshape(im, (1, 224, 224, 3))
      outputs = predict_func(im)
      prob = outputs[0]
  
      ret = prob[0].argsort()[-10:][::-1]
      print(ret)
      meta = ILSVRCMeta().get_synset_words_1000()
      print([meta[k] for k in ret])
  
file_path: tensorpack/examples/ResNet/load-resnet.py
imports: import argparse
  import functools
  import numpy
  import re
  import cv2
  import six
  import tensorflow
  from tensorpack import *
  from tensorpack.dataflow.dataset import ILSVRCMeta
  from tensorpack.utils import logger
  from imagenet_utils import ImageNetModel, eval_classification, get_imagenet_dataflow
  from resnet_model import resnet_bottleneck, resnet_group
label_desc: Use image node_id 2 for calling a global function with example usage: run_test(params, input) without return types