code: def optimizer(self):
      lr = tf.get_variable(
          "learning_rate", initializer=2e-4, trainable=False
      )
      return tf.train.AdamOptimizer(lr, epsilon=1e-5)
  
imports: import argparse
  import numpy
  import os
  import sys
  import cv2
  import tensorflow
  from tensorpack import *
  from tensorpack.dataflow import dataset
  from tensorpack.tfutils.sessinit import SmartInit
  from tensorpack.tfutils.summary import add_param_summary
  from tensorpack.tfutils.varreplace import remap_variables
  from tensorpack.utils.gpu import get_num_gpu
  from dorefa import get_dorefa, ternarize
  from imagenet_utils import ImageNetModel, eval_classification, fbresnet_augmentor, get_imagenet_dataflow
label_desc: Use image node_id 2 for calling the Model obj's underlying member method code with example usage: obj.optimizer() and returns: tf