code: def main():
      parser = argparse.ArgumentParser()
      parser.add_argument(
          "--env",
          type=str,
          default="BreakoutNoFrameskip-v4",
          help="OpenAI Atari domain to perform algorithm on.",
      )
      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(
          "--seed", type=int, default=0, help="Random seed [0, 2 ** 31)"
      )
      parser.add_argument(
          "--gpu",
          type=int,
          default=0,
          help="GPU to use, set to -1 if no GPU.",
      )
      parser.add_argument(
          "--load", type=str, default=None, required=True
      )
      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.add_argument(
          "--steps",
          type=int,
          default=5 * 10**7,
          help="Total number of demo timesteps to collect",
      )
      args = parser.parse_args()
  
      import logging
  
      logging.basicConfig(level=args.logging_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)
      print("Output files are saved in {}".format(args.outdir))
  
      def make_env():
          env = atari_wrappers.wrap_deepmind(
              atari_wrappers.make_atari(args.env, max_frames=None),
              episode_life=False,
              clip_rewards=False,
          )
          env.seed(int(args.seed))
          # Randomize actions like epsilon-greedy
          env = chainerrl.wrappers.RandomizeAction(env, 0.01)
          if args.monitor:
              env = chainerrl.wrappers.Monitor(
                  env, args.outdir, mode="evaluation"
              )
          if args.render:
              env = chainerrl.wrappers.Render(env)
          return env
  
      env = make_env()
  
      n_actions = env.action_space.n
      q_func = links.Sequence(
          links.NatureDQNHead(),
          L.Linear(512, n_actions),
          DiscreteActionValue,
      )
  
      # Draw the computational graph and save it in the output directory.
      chainerrl.misc.draw_computational_graph(
          [q_func(np.zeros((4, 84, 84), dtype=np.float32)[None])],
          os.path.join(args.outdir, "model"),
      )
  
      # The optimizer and replay buffer are dummy variables required by agent
      opt = optimizers.RMSpropGraves()
      opt.setup(q_func)
      rbuf = replay_buffer.ReplayBuffer(1)
  
      def phi(x):
          # Feature extractor
          return np.asarray(x, dtype=np.float32) / 255
  
      Agent = agents.DQN
      agent = Agent(
          q_func,
          opt,
          rbuf,
          gpu=args.gpu,
          gamma=0.99,
          explorer=None,
          replay_start_size=1,
          minibatch_size=1,
          target_update_interval=None,
          clip_delta=True,
          update_interval=4,
          phi=phi,
      )
  
      agent.load(args.load)
  
      # saves demos to outdir/demos.pickle
      experiments.collect_demonstrations(
          agent=agent,
          env=env,
          steps=args.steps,
          episodes=None,
          outdir=args.outdir,
          max_episode_len=None,
      )
  
file_path: chainerrl/examples/atari/collect_demos_ale.py
imports: import argparse
  import os
  from chainer import links
  from chainer import optimizers
  import numpy
  import chainerrl
  from chainerrl.action_value import DiscreteActionValue
  from chainerrl import agents
  from chainerrl import experiments
  from chainerrl import links
  from chainerrl import misc
  from chainerrl import replay_buffer
  from chainerrl.wrappers import atari_wrappers
label_desc: Use image node_id 1 for calling a global function with example usage: main() and returns: env, unknown