code: def apply_compact(graph_path):
      
      with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
          # Note, we just load the graph and do *not* need to initialize anything.
          with tf.gfile.GFile(graph_path, "rb") as f:
              graph_def = tf.GraphDef()
              graph_def.ParseFromString(f.read())
              tf.import_graph_def(graph_def)
  
          input_img = sess.graph.get_tensor_by_name('import/input_img:0')
          prediction_img = sess.graph.get_tensor_by_name('import/prediction_img:0')
  
          prediction = sess.run(prediction_img, {input_img: cv2.imread('lena.png')[None, ...]})
          cv2.imwrite('applied_compact.png', prediction[0])
docs: Run the pruned and frozen inference graph.