code: def optimizer(self):
      lr = tf.get_variable(
          "learning_rate", initializer=2e-3, trainable=False
      )
      opt = tf.train.AdamOptimizer(lr)
      return optimizer.apply_grad_processors(opt, [GlobalNormClip(5)])
  
imports: import argparse
  import numpy
  import operator
  import os
  import sys
  from collections import Counter
  import tensorflow
  from tensorpack import *
  from tensorpack.tfutils import optimizer, summary
  from tensorpack.tfutils.gradproc import GlobalNormClip
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