code: def main():
      parser = argparse.ArgumentParser()
      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")
      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("--steps", type=int, default=8 * 10**7)
      parser.add_argument("--update-steps", type=int, default=5)
      parser.add_argument("--lr", type=float, default=7e-4)
      parser.add_argument(
          "--gamma", type=float, default=0.99, help="discount factor"
      )
      parser.add_argument("--rmsprop-epsilon", type=float, default=1e-5)
      parser.add_argument(
          "--use-gae",
          action="store_true",
          default=False,
          help="use generalized advantage estimation",
      )
      parser.add_argument(
          "--tau", type=float, default=0.95, help="gae parameter"
      )
      parser.add_argument(
          "--alpha",
          type=float,
          default=0.99,
          help="RMSprop optimizer alpha",
      )
      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("--demo", action="store_true", default=False)
      parser.add_argument("--load", type=str, default="")
      parser.add_argument(
          "--max-grad-norm",
          type=float,
          default=40,
          help="value loss coefficient",
      )
      parser.add_argument(
          "--gpu",
          "-g",
          type=int,
          default=-1,
          help="GPU ID (negative value indicates CPU)",
      )
      parser.add_argument("--num-envs", type=int, default=1)
      parser.add_argument(
          "--logging-level",
          type=int,
          default=20,
          help="Logging level. 10:DEBUG, 20:INFO etc.",
      )
      parser.add_argument(
          "--monitor",
          action="store_true",
          default=False,
          help="Monitor env. Videos and additional information"
          " are saved as output files.",
      )
      parser.add_argument(
          "--render",
          action="store_true",
          default=False,
          help="Render env states in a GUI window.",
      )
      parser.set_defaults(use_lstm=False)
      args = parser.parse_args()
  
      logging.basicConfig(level=args.logging_level)
  
      # Set a random seed used in ChainerRL.
      # If you use more than one processes, the results will be no longer
      # 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.num_envs) + args.seed * args.num_envs
      )
      assert process_seeds.max() < 2**31
  
      args.outdir = experiments.prepare_output_dir(args, args.outdir)
      print("Output files are saved in {}".format(args.outdir))
  
      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
  
      def make_batch_env(test):
          return chainerrl.envs.MultiprocessVectorEnv(
              [
                  functools.partial(make_env, idx, test)
                  for idx, env in enumerate(range(args.num_envs))
              ]
          )
  
      sample_env = make_env(0, test=False)
  
      n_actions = sample_env.action_space.n
  
      model = A2CFF(n_actions)
      optimizer = rmsprop_async.RMSpropAsync(
          lr=args.lr, eps=args.rmsprop_epsilon, alpha=args.alpha
      )
      optimizer.setup(model)
      optimizer.add_hook(
          chainer.optimizer.GradientClipping(args.max_grad_norm)
      )
      if args.weight_decay > 0:
          optimizer.add_hook(NonbiasWeightDecay(args.weight_decay))
      agent = a2c.A2C(
          model,
          optimizer,
          gamma=args.gamma,
          gpu=args.gpu,
          num_processes=args.num_envs,
          update_steps=args.update_steps,
          phi=phi,
          use_gae=args.use_gae,
          tau=args.tau,
      )
  
      if args.load:
          agent.load(args.load)
  
      if args.demo:
          eval_stats = experiments.eval_performance(
              env=make_batch_env(test=True),
              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:
          experiments.train_agent_batch_with_evaluation(
              agent=agent,
              env=make_batch_env(test=False),
              eval_env=make_batch_env(test=True),
              steps=args.steps,
              eval_n_steps=None,
              eval_n_episodes=args.eval_n_runs,
              eval_interval=args.eval_interval,
              outdir=args.outdir,
              save_best_so_far_agent=False,
              log_interval=1000,
          )
  
imports: import argparse
  import functools
  import logging
  import chainer
  import numpy
  import chainerrl
  from chainerrl.agents import a2c
  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 import v_function
  from chainerrl.wrappers import atari_wrappers
label_desc: Use image node_id 2 for calling a global function with example usage: main() and returns: env, chainerrl