code: def evaluation(session, graph_ops, saver):
      """
      Evaluate a model.
      """
      saver.restore(session, test_model_path)
      print("Restored model weights from ", test_model_path)
      monitor_env = gym.make(game)
      monitor_env.monitor.start("qlearning/eval")
  
      # Unpack graph ops
      s = graph_ops["s"]
      q_values = graph_ops["q_values"]
  
      # Wrap env with AtariEnvironment helper class
      env = AtariEnvironment(
          gym_env=monitor_env, action_repeat=action_repeat
      )
  
      for i_episode in xrange(num_eval_episodes):
          s_t = env.get_initial_state()
          ep_reward = 0
          terminal = False
          while not terminal:
              monitor_env.render()
              readout_t = q_values.eval(
                  session=session, feed_dict={s: [s_t]}
              )
              action_index = np.argmax(readout_t)
              s_t1, r_t, terminal, info = env.step(action_index)
              s_t = s_t1
              ep_reward += r_t
          print(ep_reward)
      monitor_env.monitor.close()
  
imports: from __future__ import division, print_function, absolute_import
  import threading
  import random
  import numpy
  import time
  from skimage.transform import resize
  from skimage.color import rgb2gray
  from collections import deque
  import gym
  import tensorflow.compat.v1
  import tflearn
label_desc: Use image node_id 8 for calling a global function with example usage: evaluation(session, graph_ops, saver) without return types