code: def run_test(model_path, img_file):
      predict_func = OfflinePredictor(
          PredictConfig(
              input_signature=[
                  tf.TensorSpec(
                      (None, 368, 368, 3), tf.float32, "input"
                  )
              ],
              tower_func=CPM,
              session_init=SmartInit(model_path),
              input_names=["input"],
              output_names=["resized_map"],
          )
      )
  
      im = cv2.imread(img_file, cv2.IMREAD_COLOR).astype("float32")
      im = cv2.resize(im, (368, 368))
      out = predict_func(im[None, :, :, :])[0][0]
      hm = out[:, :, :14].sum(axis=2)
      viz = colorize(im, hm)
      cv2.imwrite("output.jpg", viz)
  
file_path: tensorpack/examples/CaffeModels/load-cpm.py
imports: import argparse
  import numpy
  import cv2
  import tensorflow
  from tensorpack import *
  from tensorpack.utils import viz
  from tensorpack.utils.argtools import memoized
label_desc: Use image node_id 4 for calling a global function with example usage: run_test(model_path, img_file) without return types