code: def make_agent(self, env, gpu):
      policy, vf = self.make_model(env)
      vf_opt = torch.optim.Adam(vf.parameters(), lr=1e-2)
  
      if self.standardize_obs:
          obs_normalizer = pfrl.nn.EmpiricalNormalization(
              env.observation_space.low.size
          )
      else:
          obs_normalizer = None
  
      agent = pfrl.agents.TRPO(
          policy=policy,
          vf=vf,
          vf_optimizer=vf_opt,
          obs_normalizer=obs_normalizer,
          gpu=gpu,
          gamma=0.5,
          lambd=self.lambd,
          entropy_coef=self.entropy_coef,
          standardize_advantages=self.standardize_advantages,
          update_interval=64,
          vf_batch_size=32,
          act_deterministically=True,
          recurrent=self.recurrent,
          max_grad_norm=1.0,
      )
  
      return agent
  
imports: import os
  import tempfile
  import unittest
  import numpy
  import pytest
  import torch
  from torch import nn
  import pfrl
  from pfrl.agents import trpo
  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 import RecurrentSequential
  from pfrl.policies import GaussianHeadWithStateIndependentCovariance, SoftmaxCategoricalHead
  from pfrl.testing import torch_assert_allclose
label_desc: Use image node_id 12 for calling the _TestTRPO obj's underlying member method code with example usage: obj.make_agent(env, gpu) and returns: agent