code: def main():
      import logging
  
      parser = argparse.ArgumentParser()
      parser.add_argument("processes", type=int)
      parser.add_argument("--env", type=str, default="CartPole-v0")
      parser.add_argument(
          "--arch",
          type=str,
          default="FFSoftmax",
          choices=("FFSoftmax", "FFMellowmax", "LSTMGaussian"),
      )
      parser.add_argument(
          "--seed", type=int, default=0, help="Random seed [0, 2 ** 32)"
      )
      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("--eval-interval", type=int, default=10**5)
      parser.add_argument("--eval-n-runs", type=int, default=10)
      parser.add_argument(
          "--reward-scale-factor", type=float, default=1e-2
      )
      parser.add_argument("--rmsprop-epsilon", type=float, default=1e-1)
      parser.add_argument(
          "--render", action="store_true", default=False
      )
      parser.add_argument("--lr", type=float, default=7e-4)
      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(
          "--logger-level", type=int, default=logging.DEBUG
      )
      parser.add_argument("--monitor", action="store_true")
      args = parser.parse_args()
  
      logging.basicConfig(level=args.logger_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.processes) + args.seed * args.processes
      )
      assert process_seeds.max() < 2**32
  
      args.outdir = experiments.prepare_output_dir(args, args.outdir)
  
      def make_env(process_idx, test):
          env = gym.make(args.env)
          # Use different random seeds for train and test envs
          process_seed = int(process_seeds[process_idx])
          env_seed = (
              2**32 - 1 - process_seed if test else process_seed
          )
          env.seed(env_seed)
          # Cast observations to float32 because our model uses float32
          env = chainerrl.wrappers.CastObservationToFloat32(env)
          if args.monitor and process_idx == 0:
              env = chainerrl.wrappers.Monitor(env, args.outdir)
          if not test:
              # Scale rewards (and thus returns) to a reasonable range so that
              # training is easier
              env = chainerrl.wrappers.ScaleReward(
                  env, args.reward_scale_factor
              )
          if args.render and process_idx == 0 and not test:
              env = chainerrl.wrappers.Render(env)
          return env
  
      sample_env = gym.make(args.env)
      timestep_limit = sample_env.spec.max_episode_steps
      obs_space = sample_env.observation_space
      action_space = sample_env.action_space
  
      # Switch policy types accordingly to action space types
      if args.arch == "LSTMGaussian":
          model = A3CLSTMGaussian(
              obs_space.low.size, action_space.low.size
          )
      elif args.arch == "FFSoftmax":
          model = A3CFFSoftmax(obs_space.low.size, action_space.n)
      elif args.arch == "FFMellowmax":
          model = A3CFFMellowmax(obs_space.low.size, action_space.n)
  
      opt = rmsprop_async.RMSpropAsync(
          lr=args.lr, eps=args.rmsprop_epsilon, 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))
  
      agent = a3c.A3C(
          model, opt, t_max=args.t_max, gamma=0.99, beta=args.beta
      )
      if args.load:
          agent.load(args.load)
  
      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,
              max_episode_len=timestep_limit,
          )
          print(
              "n_runs: {} mean: {} median: {} stdev {}".format(
                  args.eval_n_runs,
                  eval_stats["mean"],
                  eval_stats["median"],
                  eval_stats["stdev"],
              )
          )
      else:
          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,
              max_episode_len=timestep_limit,
          )
  
imports: import argparse
  import os
  import chainer
  from chainer import functions
  from chainer import links
  import gym
  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 policies
  from chainerrl.recurrent import RecurrentChainMixin
  from chainerrl import v_function
label_desc: Use image node_id 1 for calling a global function with example usage: main() and returns: env