code: def test_double_dqn_loss(setup):
      algo, env, buff, _, batch_size = setup
  
      algo._double_q = True
      trainer = Trainer(snapshot_config)
      trainer.setup(algo, env)
  
      paths = trainer.obtain_episodes(0, batch_size=batch_size)
      buff.add_episode_batch(paths)
      timesteps = buff.sample_timesteps(algo._buffer_batch_size)
      timesteps_copy = copy.deepcopy(timesteps)
  
      observations = np_to_torch(timesteps.observations)
      rewards = np_to_torch(timesteps.rewards).reshape(-1, 1)
      actions = np_to_torch(timesteps.actions)
      next_observations = np_to_torch(timesteps.next_observations)
      terminals = np_to_torch(timesteps.terminals).reshape(-1, 1)
  
      next_inputs = next_observations
      inputs = observations
      with torch.no_grad():
          # double Q loss
          selected_actions = torch.argmax(algo._qf(next_inputs), axis=1)
          # use target qf to get Q values for those actions
          selected_actions = selected_actions.long().unsqueeze(1)
          best_qvals = torch.gather(
              algo._target_qf(next_inputs),
              dim=1,
              index=selected_actions,
          )
  
      rewards_clipped = rewards
      y_target = (
          rewards_clipped
          + (1.0 - terminals) * algo._discount * best_qvals
      )
      y_target = y_target.squeeze(1)
  
      # optimize qf
      qvals = algo._qf(inputs)
      selected_qs = torch.sum(qvals * actions, axis=1)
      qval_loss = F.smooth_l1_loss(selected_qs, y_target)
  
      algo_loss, algo_targets, algo_selected_qs = algo._optimize_qf(
          timesteps_copy
      )
      env.close()
  
      assert (qval_loss.detach() == algo_loss).all()
      assert (y_target == algo_targets).all()
      assert (selected_qs == algo_selected_qs).all()
  
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 4 for calling a global function with example usage: test_double_dqn_loss(setup) without return types