code: def optimize(self, vars_to_optimize, iterations=200):
      self.sess.run(self._reset_global_step)
      self.sess.run(self.init_id_vars)
      fetch_ops = [
          self.min_op,
          self.loss,
          self.id_loss_comp,
          self.learning_rate,
      ]
      for _ in range(iterations):
          if self.args.optimizer == "lbfgs":
              self.optimizer.minimize(
                  self.sess, fetches=[vars_to_optimize, self.loss]
              )
              yield {"loss": self.loss.eval()}
          else:
              _, loss, id_loss, lr = self.sess.run(fetch_ops)
              yield {"loss": loss, "id_loss": id_loss, "lr": lr}
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import tensorflow
  import bz2
  import PIL.Image
  from PIL import ImageFilter
  import numpy
  from keras.models import Model
  from keras.applications.vgg16 import VGG16, preprocess_input
  import keras.backend
  import traceback
  import external.stylegan2.dnnlib.tflib
  from core.landmark_handler import Landmark_Handler
  from core import arcface_handler
label_desc: Use image node_id 6 for calling the PerceptualModel obj's underlying member method code with example usage: obj.optimize(vars_to_optimize, iterations) without return types