code: def main(
      env=None,
      seed=24,
      n=psutil.cpu_count(logical=False),
      buffer_size=None,
      n_steps=None,
      max_episode_length=None,
  ):
      """Wrapper to setup the logging directory.
  
      Args:
          env (str): Name of the atari environment, can either be the prefix
              or the full name. For example, this can either be 'Pong' or
              'PongNoFrameskip-v4'. If the former is used, the env used will be
              `env` + 'NoFrameskip-v4'.
          seed (int): Seed to use for the RNG.
          n (int): Number of workers to use. Defaults to the number of CPU cores
              available.
          buffer_size (int): size of the replay buffer in transitions. If None,
              defaults to hyperparams['buffer_size']. This is used by the
              integration tests.
          n_steps (float): Total number of environment steps to run for, not
              not including evaluation. If this is not None, n_epochs will
              be recalculated based on this value.
          max_episode_length (int): Max length of an episode. If None, defaults
              to the timelimit specific to the environment. Used by integration
              tests.
      """
      if "-v" not in env:
          env += "NoFrameskip-v4"
      logdir = "data/local/experiment/" + env
  
      if n_steps is not None:
          hyperparams["n_epochs"] = math.ceil(
              int(n_steps)
              / (
                  hyperparams["steps_per_epoch"]
                  * hyperparams["sampler_batch_size"]
              )
          )
      if buffer_size is not None:
          hyperparams["buffer_size"] = buffer_size
  
      dqn_atari(
          dict(log_dir=logdir),
          env=env,
          seed=seed,
          n_workers=n,
          max_episode_length=max_episode_length,
          **hyperparams,
      )
  
imports: import math
  import click
  import gym
  import numpy
  import psutil
  import torch
  from garage import wrap_experiment
  from garage.envs import GymEnv
  from garage.envs.wrappers.clip_reward import ClipReward
  from garage.envs.wrappers.episodic_life import EpisodicLife
  from garage.envs.wrappers.fire_reset import FireReset
  from garage.envs.wrappers.grayscale import Grayscale
  from garage.envs.wrappers.max_and_skip import MaxAndSkip
  from garage.envs.wrappers.noop import Noop
  from garage.envs.wrappers.resize import Resize
  from garage.envs.wrappers.stack_frames import StackFrames
  from garage.experiment.deterministic import set_seed
  from garage.np.exploration_policies import EpsilonGreedyPolicy
  from garage.replay_buffer import PathBuffer
  from garage.sampler import FragmentWorker, LocalSampler
  from garage.torch import set_gpu_mode
  from garage.torch.algos import DQN
  from garage.torch.policies import DiscreteQFArgmaxPolicy
  from garage.torch.q_functions import DiscreteCNNQFunction
  from garage.trainer import Trainer
label_desc: Use image node_id 1 for calling a global function with example usage: main(env, seed, n, buffer_size, n_steps, max_episode_length) without return types