code: def optimizer(self):
      lr = tf.get_variable(
          "learning_rate", initializer=3e-5, trainable=False
      )
      opt = tf.train.AdamOptimizer(lr, epsilon=1e-3)
      return optimizer.apply_grad_processors(
          opt,
          [
              gradproc.ScaleGradient(
                  [("convfcweight.*", 0.1), ("conv5_.*", 5)]
              )
          ],
      )
  
imports: import argparse
  import numpy
  import os
  import cv2
  import tensorflow
  from tensorpack import *
  from tensorpack.dataflow import dataset
  from tensorpack.tfutils import gradproc, optimizer
  from tensorpack.tfutils.summary import add_moving_summary, add_param_summary
  from tensorpack.utils.gpu import get_num_gpu
  from tensorpack.utils import logger
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