code: def setup():
      set_seed(24)
      n_epochs = 11
      steps_per_epoch = 10
      sampler_batch_size = 512
      num_timesteps = 100 * steps_per_epoch * sampler_batch_size
      env = GymEnv("CartPole-v0")
  
      replay_buffer = PathBuffer(capacity_in_transitions=int(1e6))
  
      qf = DiscreteMLPQFunction(env_spec=env.spec, hidden_sizes=(8, 5))
  
      policy = DiscreteQFArgmaxPolicy(env_spec=env.spec, qf=qf)
      exploration_policy = EpsilonGreedyPolicy(
          env_spec=env.spec,
          policy=policy,
          total_timesteps=num_timesteps,
          max_epsilon=1.0,
          min_epsilon=0.01,
          decay_ratio=0.4,
      )
      sampler = LocalSampler(
          agents=exploration_policy,
          envs=env,
          max_episode_length=env.spec.max_episode_length,
          worker_class=FragmentWorker,
      )
      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=5e-5,
          double_q=False,
          discount=0.9,
          min_buffer_size=int(1e4),
          n_train_steps=500,
          target_update_freq=30,
          buffer_batch_size=64,
      )
  
      return algo, env, replay_buffer, n_epochs, sampler_batch_size
  
imports: import copy
  import tempfile
  from unittest.mock import MagicMock
  import pytest
  import torch
  from torch.nn import functional
  from garage.envs import GymEnv
  from garage.experiment import SnapshotConfig
  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 np_to_torch
  from garage.torch.algos import DQN
  from garage.torch.policies import DiscreteQFArgmaxPolicy
  from garage.torch.q_functions import DiscreteMLPQFunction
  from garage.trainer import Trainer
  from tests.fixtures import snapshot_config
label_desc: Use image node_id 1 for calling a global function with example usage: setup() and returns: algo,  env,  replay_buffer,  n_epochs,  sampler_batch_size