code: def _test_abc(
      self,
      steps=1000000,
      require_success=True,
      gpu=-1,
      load_model=False,
  ):
      env, _ = self.make_env_and_successful_return(
          test=False, n=self.num_processes
      )
      test_env, successful_return = self.make_env_and_successful_return(
          test=True, n=1
      )
      agent = self.make_agent(env, gpu)
  
      if load_model:
          print("Load agent from", self.agent_dirname)
          agent.load(self.agent_dirname)
  
      # Train
      chainerrl.experiments.train_agent_batch_with_evaluation(
          agent=agent,
          env=env,
          steps=steps,
          outdir=self.tmpdir,
          log_interval=10,
          eval_interval=200,
          eval_n_steps=None,
          eval_n_episodes=50,
          successful_score=1,
          eval_env=test_env,
      )
      env.close()
  
      # Test
      n_test_runs = 100
      eval_returns = batch_run_evaluation_episodes(
          test_env,
          agent,
          n_steps=None,
          n_episodes=n_test_runs,
      )
      test_env.close()
      n_succeeded = np.sum(
          np.asarray(eval_returns) >= successful_return
      )
      if require_success:
          self.assertGreater(n_succeeded, 0.8 * n_test_runs)
  
      # Save
      agent.save(self.agent_dirname)
  
imports: import os
  import tempfile
  import unittest
  from chainer import optimizers
  from chainer import testing
  import numpy
  import chainerrl
  from chainerrl.agents.a2c import A2C
  from chainerrl.agents.a2c import A2CSeparateModel
  from chainerrl.envs.abc import ABC
  from chainerrl.experiments.evaluator import batch_run_evaluation_episodes
  from chainerrl import policies
  from chainerrl import v_functions
label_desc: Use image node_id 6 for calling the TestA2C obj's underlying member method code with example usage: obj._test_abc(steps, require_success, gpu, load_model) without return types