code: def optimizer(self):
      lr = tf.get_variable(
          "learning_rate", initializer=5e-4, trainable=False
      )
      opt = tf.train.AdamOptimizer(lr, epsilon=1e-3)
      return optimizer.apply_grad_processors(
          opt,
          [
              gradproc.ScaleGradient(("STN.*", 0.1)),
              gradproc.SummaryGradient(),
          ],
      )
  
file_path: tensorpack/examples/SpatialTransformer/mnist-addition.py
imports: import argparse
  import numpy
  import os
  import cv2
  from tensorpack import tfv1
  from tensorpack import *
  from tensorpack.dataflow import dataset
  from tensorpack.tfutils import gradproc, optimizer, summary
label_desc: Use image node_id 3 for calling the Model obj's underlying member method code with example usage: obj.optimizer() and returns: optimizer