code: def main():
      import logging
  
      parser = argparse.ArgumentParser()
      parser.add_argument("--gpu", type=int, default=0)
      parser.add_argument("--env", type=str, default="Hopper-v2")
      parser.add_argument(
          "--arch",
          type=str,
          default="FFGaussian",
          choices=("FFSoftmax", "FFMellowmax", "FFGaussian"),
      )
      parser.add_argument("--bound-mean", action="store_true")
      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("--steps", type=int, default=10**6)
      parser.add_argument("--eval-interval", type=int, default=10000)
      parser.add_argument("--eval-n-runs", type=int, default=10)
      parser.add_argument(
          "--reward-scale-factor", type=float, default=1e-2
      )
      parser.add_argument(
          "--standardize-advantages", action="store_true"
      )
      parser.add_argument(
          "--render", action="store_true", default=False
      )
      parser.add_argument("--lr", type=float, default=3e-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")
  
      parser.add_argument("--update-interval", type=int, default=2048)
      parser.add_argument("--batchsize", type=int, default=64)
      parser.add_argument("--epochs", type=int, default=10)
      parser.add_argument("--entropy-coef", type=float, default=0.0)
      args = parser.parse_args()
  
      logging.basicConfig(level=args.logger_level)
  
      # Set a random seed used in ChainerRL
      misc.set_random_seed(args.seed, gpus=(args.gpu,))
  
      args.outdir = experiments.prepare_output_dir(args, args.outdir)
  
      def make_env(test):
          env = gym.make(args.env)
          # Use different random seeds for train and test envs
          env_seed = 2**32 - 1 - args.seed if test else args.seed
          env.seed(env_seed)
          # Cast observations to float32 because our model uses float32
          env = chainerrl.wrappers.CastObservationToFloat32(env)
          if args.monitor:
              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:
              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
  
      # Normalize observations based on their empirical mean and variance
      obs_normalizer = chainerrl.links.EmpiricalNormalization(
          obs_space.low.size, clip_threshold=5
      )
  
      # Switch policy types accordingly to action space types
      if 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)
      elif args.arch == "FFGaussian":
          model = A3CFFGaussian(
              obs_space.low.size,
              action_space,
              bound_mean=args.bound_mean,
          )
  
      opt = chainer.optimizers.Adam(alpha=args.lr, eps=1e-5)
      opt.setup(model)
      if args.weight_decay > 0:
          opt.add_hook(NonbiasWeightDecay(args.weight_decay))
      agent = PPO(
          model,
          opt,
          obs_normalizer=obs_normalizer,
          gpu=args.gpu,
          update_interval=args.update_interval,
          minibatch_size=args.batchsize,
          epochs=args.epochs,
          clip_eps_vf=None,
          entropy_coef=args.entropy_coef,
          standardize_advantages=args.standardize_advantages,
      )
  
      if args.load:
          agent.load(args.load)
  
      if args.demo:
          env = make_env(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:
          # Linearly decay the learning rate to zero
          def lr_setter(env, agent, value):
              agent.optimizer.alpha = value
  
          lr_decay_hook = experiments.LinearInterpolationHook(
              args.steps, args.lr, 0, lr_setter
          )
  
          # Linearly decay the clipping parameter to zero
          def clip_eps_setter(env, agent, value):
              agent.clip_eps = max(value, 1e-8)
  
          clip_eps_decay_hook = experiments.LinearInterpolationHook(
              args.steps, 0.2, 0, clip_eps_setter
          )
  
          experiments.train_agent_with_evaluation(
              agent=agent,
              env=make_env(False),
              eval_env=make_env(True),
              outdir=args.outdir,
              steps=args.steps,
              eval_n_steps=None,
              eval_n_episodes=args.eval_n_runs,
              eval_interval=args.eval_interval,
              train_max_episode_len=timestep_limit,
              save_best_so_far_agent=False,
              step_hooks=[
                  lr_decay_hook,
                  clip_eps_decay_hook,
              ],
          )
  
imports: import argparse
  import chainer
  from chainer import functions
  import gym
  import chainerrl
  from chainerrl.agents import a3c
  from chainerrl.agents import PPO
  from chainerrl import experiments
  from chainerrl import links
  from chainerrl import misc
  from chainerrl.optimizers.nonbias_weight_decay import NonbiasWeightDecay
  from chainerrl import policies
label_desc: Use image node_id 1 for calling a global function with example usage: main() and returns: env