code: def _test_load_trpo(self, gpu):
      obs_size = 11
      action_size = 3
  
      policy = torch.nn.Sequential(
          nn.Linear(obs_size, 64),
          nn.Tanh(),
          nn.Linear(64, 64),
          nn.Tanh(),
          nn.Linear(64, action_size),
          pfrl.policies.GaussianHeadWithStateIndependentCovariance(
              action_size=action_size,
              var_type="diagonal",
              var_func=lambda x: torch.exp(
                  2 * x
              ),  # Parameterize log std
              var_param_init=0,  # log std = 0 => std = 1
          ),
      )
  
      vf = torch.nn.Sequential(
          nn.Linear(obs_size, 64),
          nn.Tanh(),
          nn.Linear(64, 64),
          nn.Tanh(),
          nn.Linear(64, 1),
      )
      vf_opt = torch.optim.Adam(vf.parameters())
  
      agent = agents.TRPO(
          policy=policy,
          vf=vf,
          vf_optimizer=vf_opt,
          gpu=gpu,
          update_interval=5000,
          max_kl=0.01,
          conjugate_gradient_max_iter=20,
          conjugate_gradient_damping=1e-1,
          gamma=0.995,
          lambd=0.97,
          vf_epochs=5,
          entropy_coef=0,
      )
  
      downloaded_model, exists = download_model(
          "TRPO", "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 TestLoadTRPO obj's underlying member method code with example usage: obj._test_load_trpo(gpu) without return types