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
      pfrl.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:
          assert n_succeeded > 0.8 * n_test_runs
  
      # Save
      agent.save(self.agent_dirname)
  
imports: import os
  import tempfile
  import numpy
  import pytest
  import torch
  from torch import nn
  import pfrl
  from pfrl.agents.a2c import A2C
  from pfrl.envs.abc import ABC
  from pfrl.experiments.evaluator import batch_run_evaluation_episodes
  from pfrl.nn import Branched
  from pfrl.policies import GaussianHeadWithStateIndependentCovariance, SoftmaxCategoricalHead
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