code: def test_sac_to():
      """Test moving Sac between CPU and GPU."""
      env = normalize(
          GymEnv("InvertedDoublePendulum-v2", max_episode_length=100)
      )
      deterministic.set_seed(0)
      policy = TanhGaussianMLPPolicy(
          env_spec=env.spec,
          hidden_sizes=[32, 32],
          hidden_nonlinearity=torch.nn.ReLU,
          output_nonlinearity=None,
          min_std=np.exp(-20.0),
          max_std=np.exp(2.0),
      )
  
      qf1 = ContinuousMLPQFunction(
          env_spec=env.spec,
          hidden_sizes=[32, 32],
          hidden_nonlinearity=F.relu,
      )
  
      qf2 = ContinuousMLPQFunction(
          env_spec=env.spec,
          hidden_sizes=[32, 32],
          hidden_nonlinearity=F.relu,
      )
      replay_buffer = PathBuffer(
          capacity_in_transitions=int(1e6),
      )
      trainer = Trainer(snapshot_config=snapshot_config)
      sampler = LocalSampler(
          agents=policy,
          envs=env,
          max_episode_length=env.spec.max_episode_length,
          worker_class=FragmentWorker,
      )
      sac = SAC(
          env_spec=env.spec,
          policy=policy,
          qf1=qf1,
          qf2=qf2,
          sampler=sampler,
          gradient_steps_per_itr=100,
          replay_buffer=replay_buffer,
          min_buffer_size=1e3,
          target_update_tau=5e-3,
          discount=0.99,
          buffer_batch_size=64,
          reward_scale=1.0,
          steps_per_epoch=2,
      )
      trainer.setup(sac, env)
      if torch.cuda.is_available():
          set_gpu_mode(True)
      else:
          set_gpu_mode(False)
      sac.to()
      trainer.setup(algo=sac, env=env)
      trainer.train(n_epochs=1, batch_size=100)
      log_alpha = torch.clone(sac._log_alpha).cpu()
      set_gpu_mode(False)
      sac.to()
      assert torch.allclose(log_alpha, sac._log_alpha)
  
imports: from unittest.mock import MagicMock
  import numpy
  import pytest
  import torch
  from torch.nn import functional
  from garage.envs import GymEnv, normalize
  from garage.experiment import deterministic
  from garage.replay_buffer import PathBuffer
  from garage.sampler import FragmentWorker, LocalSampler
  from garage.torch import set_gpu_mode
  from garage.torch.algos import SAC
  from garage.torch.policies import TanhGaussianMLPPolicy
  from garage.torch.q_functions import ContinuousMLPQFunction
  from garage.trainer import Trainer
  from tests.fixtures import snapshot_config
label_desc: Use image node_id 6 for calling a global function with example usage: test_sac_to() without return types