code: def main():
      parser = argparse.ArgumentParser()
      parser.add_argument("processes", type=int)
      parser.add_argument(
          "--env", type=str, default="BreakoutNoFrameskip-v4"
      )
      parser.add_argument(
          "--seed", type=int, default=0, help="Random seed [0, 2 ** 31)"
      )
      parser.add_argument(
          "--outdir",
          type=str,
          default="results",
          help="Directory path to save output files."
          " If it does not exist, it will be created.",
      )
      parser.add_argument("--t-max", type=int, default=5)
      parser.add_argument("--beta", type=float, default=1e-2)
      parser.add_argument("--profile", action="store_true")
      parser.add_argument("--steps", type=int, default=8 * 10**7)
      parser.add_argument(
          "--max-frames",
          type=int,
          default=30 * 60 * 60,  # 30 minutes with 60 fps
          help="Maximum number of frames for each episode.",
      )
      parser.add_argument("--lr", type=float, default=7e-4)
      parser.add_argument("--eval-interval", type=int, default=10**6)
      parser.add_argument("--eval-n-runs", type=int, default=10)
      parser.add_argument("--weight-decay", type=float, default=0.0)
      parser.add_argument("--use-lstm", action="store_true")
      parser.add_argument("--demo", action="store_true", default=False)
      parser.add_argument("--load", type=str, default="")
      parser.add_argument(
          "--logging-level",
          type=int,
          default=20,
          help="Logging level. 10:DEBUG, 20:INFO etc.",
      )
      parser.add_argument(
          "--render",
          action="store_true",
          default=False,
          help="Render env states in a GUI window.",
      )
      parser.add_argument(
          "--monitor",
          action="store_true",
          default=False,
          help="Monitor env. Videos and additional information"
          " are saved as output files.",
      )
      parser.set_defaults(use_lstm=False)
      args = parser.parse_args()
  
      import logging
  
      logging.basicConfig(level=args.logging_level)
  
      # Set a random seed used in ChainerRL.
      # If you use more than one process (i.e. processes > 1),
      # the results will be no longer be deterministic
      # even with the same random seed.
      misc.set_random_seed(args.seed)
  
      # Set different random seeds for different subprocesses.
      # If seed=0 and processes=4, subprocess seeds are [0, 1, 2, 3].
      # If seed=1 and processes=4, subprocess seeds are [4, 5, 6, 7].
      process_seeds = (
          np.arange(args.processes) + args.seed * args.processes
      )
      assert process_seeds.max() < 2**31
  
      args.outdir = experiments.prepare_output_dir(args, args.outdir)
      print("Output files are saved in {}".format(args.outdir))
  
      n_actions = gym.make(args.env).action_space.n
  
      if args.use_lstm:
          model = A3CLSTM(n_actions)
      else:
          model = A3CFF(n_actions)
  
      # Draw the computational graph and save it in the output directory.
      fake_obs = chainer.Variable(
          np.zeros((4, 84, 84), dtype=np.float32)[None],
          name="observation",
      )
      with chainerrl.recurrent.state_reset(model):
          # The state of the model is reset again after drawing the graph
          chainerrl.misc.draw_computational_graph(
              [model(fake_obs)], os.path.join(args.outdir, "model")
          )
  
      opt = rmsprop_async.RMSpropAsync(lr=7e-4, eps=1e-1, alpha=0.99)
      opt.setup(model)
      opt.add_hook(chainer.optimizer.GradientClipping(40))
      if args.weight_decay > 0:
          opt.add_hook(NonbiasWeightDecay(args.weight_decay))
  
      def phi(x):
          # Feature extractor
          return np.asarray(x, dtype=np.float32) / 255
  
      agent = a3c.A3C(
          model,
          opt,
          t_max=args.t_max,
          gamma=0.99,
          beta=args.beta,
          phi=phi,
      )
  
      if args.load:
          agent.load(args.load)
  
      def make_env(process_idx, test):
          # Use different random seeds for train and test envs
          process_seed = process_seeds[process_idx]
          env_seed = (
              2**31 - 1 - process_seed if test else process_seed
          )
          env = atari_wrappers.wrap_deepmind(
              atari_wrappers.make_atari(
                  args.env, max_frames=args.max_frames
              ),
              episode_life=not test,
              clip_rewards=not test,
          )
          env.seed(int(env_seed))
          if args.monitor:
              env = chainerrl.wrappers.Monitor(
                  env,
                  args.outdir,
                  mode="evaluation" if test else "training",
              )
          if args.render:
              env = chainerrl.wrappers.Render(env)
          return env
  
      if args.demo:
          env = make_env(0, True)
          eval_stats = experiments.eval_performance(
              env=env,
              agent=agent,
              n_steps=None,
              n_episodes=args.eval_n_runs,
          )
          print(
              "n_runs: {} mean: {} median: {} stdev: {}".format(
                  args.eval_n_runs,
                  eval_stats["mean"],
                  eval_stats["median"],
                  eval_stats["stdev"],
              )
          )
      else:
          # Linearly decay the learning rate to zero
          def lr_setter(env, agent, value):
              agent.optimizer.lr = value
  
          lr_decay_hook = experiments.LinearInterpolationHook(
              args.steps, args.lr, 0, lr_setter
          )
  
          experiments.train_agent_async(
              agent=agent,
              outdir=args.outdir,
              processes=args.processes,
              make_env=make_env,
              profile=args.profile,
              steps=args.steps,
              eval_n_steps=None,
              eval_n_episodes=args.eval_n_runs,
              eval_interval=args.eval_interval,
              global_step_hooks=[lr_decay_hook],
              save_best_so_far_agent=False,
          )
  
imports: import argparse
  import os
  import chainer
  from chainer import links
  import gym
  import gym.wrappers
  import numpy
  import chainerrl
  from chainerrl.agents import a3c
  from chainerrl import experiments
  from chainerrl import links
  from chainerrl import misc
  from chainerrl.optimizers.nonbias_weight_decay import NonbiasWeightDecay
  from chainerrl.optimizers import rmsprop_async
  from chainerrl import policy
  from chainerrl.recurrent import RecurrentChainMixin
  from chainerrl import v_function
  from chainerrl.wrappers import atari_wrappers
label_desc: Use image node_id 1 for calling a global function with example usage: main() and returns: unknown, env