code: def _test_load_ppo(self, gpu):
      obs_size = 11
      action_size = 3
      from pfrl.policies import (
          GaussianHeadWithStateIndependentCovariance,
      )
  
      policy = torch.nn.Sequential(
          nn.Linear(obs_size, 64),
          nn.Tanh(),
          nn.Linear(64, 64),
          nn.Tanh(),
          nn.Linear(64, action_size),
          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),
      )
  
      model = pnn.Branched(policy, vf)
      opt = torch.optim.Adam(model.parameters(), lr=3e-4, eps=1e-5)
  
      agent = agents.PPO(
          model,
          opt,
          obs_normalizer=None,
          gpu=gpu,
          update_interval=2048,
          minibatch_size=64,
          epochs=10,
          clip_eps_vf=None,
          entropy_coef=0,
          standardize_advantages=True,
          gamma=0.995,
          lambd=0.97,
      )
  
      downloaded_model, exists = download_model(
          "PPO", "Hopper-v2", model_type="final"
      )
      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 1 for calling the TestLoadPPO obj's underlying member method code with example usage: obj._test_load_ppo(gpu) without return types