code: def train(session, graph_ops, num_actions, saver):
      """
      Train a model.
      """
  
      # Set up game environments (one per thread)
      envs = [gym.make(game) for i in range(n_threads)]
  
      summary_ops = build_summaries()
      summary_op = summary_ops[-1]
  
      # Initialize variables
      session.run(tf.initialize_all_variables())
      writer = writer_summary(summary_dir + "/qlearning", session.graph)
  
      # Initialize target network weights
      session.run(graph_ops["reset_target_network_params"])
  
      # Start n_threads actor-learner training threads
      actor_learner_threads = [
          threading.Thread(
              target=actor_learner_thread,
              args=(
                  thread_id,
                  envs[thread_id],
                  session,
                  graph_ops,
                  num_actions,
                  summary_ops,
                  saver,
              ),
          )
          for thread_id in range(n_threads)
      ]
      for t in actor_learner_threads:
          t.start()
          time.sleep(0.01)
  
      # Show the agents training and write summary statistics
      last_summary_time = 0
      while True:
          if show_training:
              for env in envs:
                  env.render()
          now = time.time()
          if now - last_summary_time > summary_interval:
              summary_str = session.run(summary_op)
              writer.add_summary(summary_str, float(T))
              last_summary_time = now
      for t in actor_learner_threads:
          t.join()
  
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 7 for calling a global function with example usage: train(session, graph_ops, num_actions, saver) without return types