code: def _test_abc(self, steps=100000, require_success=True):
      nproc = 8
  
      def make_env(process_idx, test):
          return ABC(
              episodic=self.episodic or test,
              partially_observable=self.use_lstm,
              deterministic=test,
          )
  
      sample_env = make_env(0, False)
      action_space = sample_env.action_space
      obs_space = sample_env.observation_space
      ndim_obs = obs_space.low.size
      n_actions = action_space.n
  
      def random_action_func():
          return np.random.randint(n_actions)
  
      def make_agent(process_idx):
          n_hidden_channels = 50
          if self.use_lstm:
              q_func = FCLSTMStateQFunction(
                  ndim_obs,
                  n_actions,
                  n_hidden_channels=n_hidden_channels,
                  n_hidden_layers=2,
              )
          else:
              q_func = FCStateQFunctionWithDiscreteAction(
                  ndim_obs,
                  n_actions,
                  n_hidden_channels=n_hidden_channels,
                  n_hidden_layers=2,
              )
          opt = rmsprop_async.RMSpropAsync(
              lr=1e-3, eps=1e-2, alpha=0.99
          )
          opt.setup(q_func)
          if self.explorer == "epsilon_greedy":
              explorer = chainerrl.explorers.ConstantEpsilonGreedy(
                  process_idx / 10, random_action_func
              )
          else:
              explorer = chainerrl.explorers.Boltzmann()
  
          return nsq.NSQ(
              q_func,
              opt,
              t_max=self.t_max,
              gamma=0.9,
              i_target=100,
              explorer=explorer,
          )
  
      with warnings.catch_warnings(record=True) as warns:
          agent = train_agent_async(
              outdir=self.outdir,
              processes=nproc,
              make_env=make_env,
              make_agent=make_agent,
              steps=steps,
              max_episode_len=5,
              eval_interval=500,
              eval_n_steps=None,
              eval_n_episodes=5,
              successful_score=1,
          )
          # There should be no AbnormalExitWarning
          self.assertEqual(
              sum(
                  1
                  if issubclass(w.category, async_.AbnormalExitWarning)
                  else 0
                  for w in warns
              ),
              0,
          )
  
      # The agent returned by train_agent_async is not guaranteed to be
      # successful because parameters could be modified by other processes
      # after success. Thus here the successful model is loaded explicitly.
      if require_success:
          agent.load(os.path.join(self.outdir, "successful"))
      agent.stop_episode()
  
      # Test
      n_test_runs = 5
      env = make_env(0, True)
      for _ in range(n_test_runs):
          total_r = 0
          obs = env.reset()
          print("test run offset:", env._offset)
          done = False
          r = 0.0
  
          while not done:
              action = agent.act(obs)
              print(("state:", obs, "action:", action))
              obs, r, done, _ = env.step(action)
              total_r += r
          if require_success:
              self.assertAlmostEqual(total_r, 1)
          agent.stop_episode()
  
imports: import logging
  import os
  import tempfile
  import unittest
  import warnings
  from chainer import testing
  import numpy
  import chainerrl
  from chainerrl.agents import nsq
  from chainerrl.envs.abc import ABC
  from chainerrl.experiments.train_agent_async import train_agent_async
  from chainerrl.misc import async_
  from chainerrl.optimizers import rmsprop_async
  from chainerrl.q_functions import FCLSTMStateQFunction
  from chainerrl.q_functions import FCStateQFunctionWithDiscreteAction
label_desc: Use image node_id 4 for calling the TestNSQ obj's underlying member method code with example usage: obj._test_abc(steps, require_success) and returns: ABC, np, nsq