code: def _test_load_sac(self, gpu):
      obs_size = 11
      action_size = 3
  
      def squashed_diagonal_gaussian_head(x):
          assert x.shape[-1] == action_size * 2
          mean, log_scale = torch.chunk(x, 2, dim=1)
          log_scale = torch.clamp(log_scale, -20.0, 2.0)
          var = torch.exp(log_scale * 2)
          from torch import distributions
  
          base_distribution = distributions.Independent(
              distributions.Normal(loc=mean, scale=torch.sqrt(var)), 1
          )
          # cache_size=1 is required for numerical stability
          return distributions.transformed_distribution.TransformedDistribution(
              base_distribution,
              [distributions.transforms.TanhTransform(cache_size=1)],
          )
  
      from pfrl.nn.lmbda import Lambda
  
      policy = nn.Sequential(
          nn.Linear(obs_size, 256),
          nn.ReLU(),
          nn.Linear(256, 256),
          nn.ReLU(),
          nn.Linear(256, action_size * 2),
          Lambda(squashed_diagonal_gaussian_head),
      )
      policy_optimizer = torch.optim.Adam(policy.parameters(), lr=3e-4)
  
      def make_q_func_with_optimizer():
          q_func = nn.Sequential(
              pfrl.nn.ConcatObsAndAction(),
              nn.Linear(obs_size + action_size, 256),
              nn.ReLU(),
              nn.Linear(256, 256),
              nn.ReLU(),
              nn.Linear(256, 1),
          )
          torch.nn.init.xavier_uniform_(q_func[1].weight)
          torch.nn.init.xavier_uniform_(q_func[3].weight)
          torch.nn.init.xavier_uniform_(q_func[5].weight)
          q_func_optimizer = torch.optim.Adam(
              q_func.parameters(), lr=3e-4
          )
          return q_func, q_func_optimizer
  
      q_func1, q_func1_optimizer = make_q_func_with_optimizer()
      q_func2, q_func2_optimizer = make_q_func_with_optimizer()
  
      agent = agents.SoftActorCritic(
          policy,
          q_func1,
          q_func2,
          policy_optimizer,
          q_func1_optimizer,
          q_func2_optimizer,
          replay_buffers.ReplayBuffer(100),
          gamma=0.99,
          replay_start_size=1000,
          gpu=gpu,
          minibatch_size=256,
          burnin_action_func=None,
          entropy_target=-3,
          temperature_optimizer_lr=3e-4,
      )
  
      downloaded_model, exists = download_model(
          "SAC", "Hopper-v2", model_type=self.pretrained_type
      )
      agent.load(downloaded_model)
      if os.environ.get("PFRL_ASSERT_DOWNLOADED_MODEL_IS_CACHED"):
          assert exists
  
imports: import os
  import numpy
  import pytest
  import torch
  from torch import nn
  import pfrl
  import pfrl.nn
  from pfrl import agents, explorers, replay_buffers
  from pfrl.initializers import init_chainer_default
  from pfrl.utils import download_model
label_desc: Use image node_id 2 for calling the TestLoadSAC obj's underlying member method code with example usage: obj._test_load_sac(gpu) and returns: distributions, q_func,  q_func_optimizer