code: def testCriticLoss():
      """Test Sac Critic/QF loss."""
      # pylint: disable=no-member
      policy = DummyActorPolicy()
      spec = MagicMock
      spec.max_episode_length = 100
      sac = SAC(
          env_spec=spec,
          policy=policy,
          qf1=DummyCriticNet(),
          qf2=DummyCriticNet(),
          sampler=None,
          replay_buffer=None,
          gradient_steps_per_itr=1,
          discount=0.9,
          buffer_batch_size=2,
          target_entropy=3.0,
          optimizer=MagicMock,
      )
  
      observations = torch.FloatTensor([[1, 2], [3, 4]])
      actions = torch.FloatTensor([[5], [6]])
      rewards = torch.FloatTensor([10, 20])
      terminals = torch.Tensor([[0.0], [0.0]])
      next_observations = torch.FloatTensor([[5, 6], [7, 8]])
      samples_data = {
          "observation": observations,
          "action": actions,
          "reward": rewards,
          "terminal": terminals,
          "next_observation": next_observations,
      }
      td_targets = [7.3, 19.1]
      pred_td_targets = [7.0, 10.0]
  
      # Expected critic loss has factor of 2, for the two TD3 critics.
      expected_loss = 2 * F.mse_loss(
          torch.Tensor(td_targets), torch.Tensor(pred_td_targets)
      )
      loss = sac._critic_objective(samples_data)
      assert np.all(np.isclose(np.sum(loss), expected_loss))
  
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 1 for calling a global function with example usage: testCriticLoss() without return types