code: def _test_load_dqn(self, gpu):
      from pfrl.q_functions import DiscreteActionValueHead
  
      n_actions = 4
      q_func = nn.Sequential(
          pnn.LargeAtariCNN(),
          init_chainer_default(nn.Linear(512, n_actions)),
          DiscreteActionValueHead(),
      )
  
      # Use the same hyperparameters as the Nature paper
  
      opt = pfrl.optimizers.RMSpropEpsInsideSqrt(
          q_func.parameters(),
          lr=2.5e-4,
          alpha=0.95,
          momentum=0.0,
          eps=1e-2,
          centered=True,
      )
  
      rbuf = replay_buffers.ReplayBuffer(100)
  
      explorer = explorers.LinearDecayEpsilonGreedy(
          start_epsilon=1.0,
          end_epsilon=0.1,
          decay_steps=10**6,
          random_action_func=lambda: np.random.randint(4),
      )
  
      agent = agents.DQN(
          q_func,
          opt,
          rbuf,
          gpu=gpu,
          gamma=0.99,
          explorer=explorer,
          replay_start_size=50,
          target_update_interval=10**4,
          clip_delta=True,
          update_interval=4,
          batch_accumulator="sum",
          phi=lambda x: x,
      )
  
      downloaded_model, exists = download_model(
          "DQN",
          "BreakoutNoFrameskip-v4",
          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 TestLoadDQN obj's underlying member method code with example usage: obj._test_load_dqn(gpu) without return types