code: def optimizer(self):
      lr = tf.get_variable(
          "learning_rate", initializer=0.001, trainable=False
      )
      opt = tf.train.AdamOptimizer(lr, epsilon=1e-3)
  
      gradprocs = [
          MapGradient(
              lambda grad: tf.clip_by_norm(
                  grad, 0.1 * tf.cast(tf.size(grad), tf.float32)
              )
          ),
          SummaryGradient(),
      ]
      opt = optimizer.apply_grad_processors(opt, gradprocs)
      return opt
  
imports: import argparse
  import cv2
  import gym
  import multiprocessing
  import numpy
  import os
  import sys
  import uuid
  import tensorflow
  from six.moves import queue
  from concurrent import futures
  from tensorpack import *
  from tensorpack.tfutils.gradproc import MapGradient, SummaryGradient
  from tensorpack.utils.concurrency import ensure_proc_terminate, start_proc_mask_signal
  from tensorpack.utils.gpu import get_num_gpu
  from tensorpack.utils.serialize import dumps
  from atari_wrapper import FireResetEnv, FrameStack, LimitLength, MapState
  from common import Evaluator, eval_model_multithread, play_n_episodes
  from simulator import SimulatorMaster, SimulatorProcess, TransitionExperience
label_desc: Use image node_id 4 for calling the Model obj's underlying member method code with example usage: obj.optimizer() and returns: opt