code: def dqn_atari(
      ctxt=None,
      env=None,
      seed=24,
      n_workers=psutil.cpu_count(logical=False),
      max_episode_length=None,
      **kwargs,
  ):
      """Train DQN with PongNoFrameskip-v4 environment.
  
      Args:
          ctxt (garage.experiment.ExperimentContext): The experiment
              configuration used by Trainer to create the snapshotter.
          env (str): Name of the atari environment, eg. 'PongNoFrameskip-v4'.
          seed (int): Used to seed the random number generator to produce
              determinism.
          n_workers (int): Number of workers to use. Defaults to the number of
              CPU cores available.
          max_episode_length (int): Max length of an episode. If None, defaults
              to the timelimit specific to the environment. Used by integration
              tests.
          kwargs (dict): hyperparameters to be saved to variant.json.
  
      """
      assert n_workers > 0
      assert env is not None
      env = gym.make(env)
      env = Noop(env, noop_max=30)
      env = MaxAndSkip(env, skip=4)
      env = EpisodicLife(env)
      if "FIRE" in env.unwrapped.get_action_meanings():
          env = FireReset(env)
      env = Grayscale(env)
      env = Resize(env, 84, 84)
      env = ClipReward(env)
      env = StackFrames(env, 4, axis=0)
      env = GymEnv(
          env, max_episode_length=max_episode_length, is_image=True
      )
      set_seed(seed)
      trainer = Trainer(ctxt)
  
      n_epochs = hyperparams["n_epochs"]
      steps_per_epoch = hyperparams["steps_per_epoch"]
      sampler_batch_size = hyperparams["sampler_batch_size"]
      num_timesteps = n_epochs * steps_per_epoch * sampler_batch_size
      replay_buffer = PathBuffer(
          capacity_in_transitions=hyperparams["buffer_size"]
      )
  
      qf = DiscreteCNNQFunction(
          env_spec=env.spec,
          image_format="NCHW",
          hidden_channels=hyperparams["hidden_channels"],
          kernel_sizes=hyperparams["kernel_sizes"],
          strides=hyperparams["strides"],
          hidden_w_init=(
              lambda x: torch.nn.init.orthogonal_(x, gain=np.sqrt(2))
          ),
          hidden_sizes=hyperparams["hidden_sizes"],
      )
  
      policy = DiscreteQFArgmaxPolicy(env_spec=env.spec, qf=qf)
      exploration_policy = EpsilonGreedyPolicy(
          env_spec=env.spec,
          policy=policy,
          total_timesteps=num_timesteps,
          max_epsilon=hyperparams["max_epsilon"],
          min_epsilon=hyperparams["min_epsilon"],
          decay_ratio=hyperparams["decay_ratio"],
      )
  
      sampler = LocalSampler(
          agents=exploration_policy,
          envs=env,
          max_episode_length=env.spec.max_episode_length,
          worker_class=FragmentWorker,
          n_workers=n_workers,
      )
  
      algo = DQN(
          env_spec=env.spec,
          policy=policy,
          qf=qf,
          exploration_policy=exploration_policy,
          replay_buffer=replay_buffer,
          sampler=sampler,
          steps_per_epoch=steps_per_epoch,
          qf_lr=hyperparams["lr"],
          clip_gradient=hyperparams["clip_gradient"],
          discount=hyperparams["discount"],
          min_buffer_size=hyperparams["min_buffer_size"],
          n_train_steps=hyperparams["n_train_steps"],
          target_update_freq=hyperparams["target_update_freq"],
          buffer_batch_size=hyperparams["buffer_batch_size"],
      )
  
      set_gpu_mode(False)
      torch.set_num_threads(1)
      if torch.cuda.is_available():
          set_gpu_mode(True)
          algo.to()
  
      trainer.setup(algo, env)
  
      trainer.train(n_epochs=n_epochs, batch_size=sampler_batch_size)
      env.close()
  
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 2 for calling a global function with example usage: dqn_atari(ctxt, env, seed, n_workers, max_episode_length) without return types