code: def make_agent(self, env, gpu):
      obs_size = env.observation_space.low.size
      action_size = env.action_space.low.size
      hidden_size = 20
      policy = chainer.Sequential(
          L.Linear(obs_size, hidden_size),
          F.relu,
          L.Linear(
              hidden_size,
              action_size,
              initialW=chainer.initializers.LeCunNormal(1e-1),
          ),
          F.tanh,
          chainerrl.distribution.ContinuousDeterministicDistribution,
      )
      policy_optimizer = optimizers.Adam().setup(policy)
      policy_optimizer.add_hook(
          chainer.optimizer_hooks.GradientClipping(1)
      )
  
      def make_q_func_with_optimizer():
          q_func = chainer.Sequential(
              concat_obs_and_action,
              L.Linear(obs_size + action_size, hidden_size),
              F.relu,
              L.Linear(
                  hidden_size,
                  1,
                  initialW=chainer.initializers.LeCunNormal(1e-1),
              ),
          )
          q_func_optimizer = optimizers.Adam(1e-2).setup(q_func)
          q_func_optimizer.add_hook(
              chainer.optimizer_hooks.GradientClipping(1)
          )
          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 = chainerrl.replay_buffer.ReplayBuffer(10**6)
  
      explorer = chainerrl.explorers.AdditiveGaussian(
          scale=0.3,
          low=env.action_space.low,
          high=env.action_space.high,
      )
  
      def burnin_action_func():
          return np.random.uniform(
              env.action_space.low, env.action_space.high
          ).astype(np.float32)
  
      agent = chainerrl.agents.TD3(
          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,
          explorer=explorer,
          gamma=0.5,
          minibatch_size=100,
          replay_start_size=100,
          burnin_action_func=burnin_action_func,
      )
  
      return agent
  
imports: import os
  import tempfile
  import unittest
  import chainer
  import chainer.functions
  from chainer import links
  from chainer import optimizers
  from chainer import testing
  import numpy
  import chainerrl
  from chainerrl.envs.abc import ABC
  from chainerrl.experiments.evaluator import batch_run_evaluation_episodes
  from chainerrl.experiments.evaluator import run_evaluation_episodes
  from chainerrl.experiments import train_agent_batch_with_evaluation
  from chainerrl.experiments import train_agent_with_evaluation
label_desc: Use image node_id 12 for calling the TestTD3 obj's underlying member method code with example usage: obj.make_agent(env, gpu) and returns: agent, q_func,  q_func_optimizer, np