code: def make_agent(self, env, gpu):
      obs_size = env.observation_space.low.size
      action_size = env.action_space.low.size
      hidden_size = 20
  
      def squashed_diagonal_gaussian_head(x):
          assert x.shape[-1] == action_size * 2
          mean, log_scale = torch.split(
              x, int(list(x.size())[-1] / 2), dim=1
          )
          log_scale = torch.clamp(log_scale, -20.0, 2.0)
          var = torch.exp(log_scale * 2)
          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)],
          )
  
      policy = nn.Sequential(
          nn.Linear(obs_size, hidden_size),
          nn.ReLU(),
          nn.Linear(
              hidden_size,
              action_size * 2,
          ),
          nn.Tanh(),
          Lambda(squashed_diagonal_gaussian_head),
      )
      policy[2].weight.detach().mul_(1e-1)
      policy_optimizer = torch.optim.Adam(policy.parameters())
  
      def make_q_func_with_optimizer():
          q_func = nn.Sequential(
              pfrl.nn.ConcatObsAndAction(),
              nn.Linear(obs_size + action_size, hidden_size),
              nn.ReLU(),
              nn.Linear(hidden_size, 1),
          )
          q_func[3].weight.detach().mul_(1e-1)
          q_func_optimizer = torch.optim.Adam(
              q_func.parameters(), lr=1e-2
          )
          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()
  
      rbuf = pfrl.replay_buffers.ReplayBuffer(10**6)
  
      def burnin_action_func():
          return np.random.uniform(
              env.action_space.low, env.action_space.high
          ).astype(np.float32)
  
      agent = pfrl.agents.SoftActorCritic(
          policy=policy,
          q_func1=q_func1,
          q_func2=q_func2,
          policy_optimizer=policy_optimizer,
          q_func1_optimizer=q_func1_optimizer,
          q_func2_optimizer=q_func2_optimizer,
          replay_buffer=rbuf,
          gamma=0.5,
          minibatch_size=100,
          replay_start_size=100,
          burnin_action_func=burnin_action_func,
          entropy_target=-action_size,
          max_grad_norm=1.0,
      )
  
      return agent
  
imports: import os
  import tempfile
  import numpy
  import pytest
  import torch
  from torch import distributions, nn
  import pfrl
  from pfrl.envs.abc import ABC
  from pfrl.experiments import train_agent_batch_with_evaluation, train_agent_with_evaluation
  from pfrl.experiments.evaluator import batch_run_evaluation_episodes, run_evaluation_episodes
  from pfrl.nn.lmbda import Lambda
label_desc: Use image node_id 12 for calling the TestSoftActorCritic obj's underlying member method code with example usage: obj.make_agent(env, gpu) and returns: agent, distributions, q_func,  q_func_optimizer, np