code: def _test_abc(
      self,
      t_max,
      use_lstm,
      discrete=True,
      episodic=True,
      steps=100000,
      require_success=True,
  ):
      nproc = 8
  
      def make_env(process_idx, test):
          size = 2
          return ABC(
              size=size,
              discrete=discrete,
              episodic=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
  
      replay_buffer = EpisodicReplayBuffer(10**4)
      obs_size = obs_space.low.size
      hidden_size = 20
      if discrete:
          n_actions = action_space.n
          head = acer.ACERDiscreteActionHead(
              pi=nn.Sequential(
                  nn.Linear(hidden_size, n_actions),
                  SoftmaxCategoricalHead(),
              ),
              q=nn.Sequential(
                  nn.Linear(hidden_size, n_actions),
                  DiscreteActionValueHead(),
              ),
          )
      else:
          action_size = action_space.low.size
          head = acer.ACERContinuousActionHead(
              pi=nn.Sequential(
                  nn.Linear(hidden_size, action_size * 2),
                  GaussianHeadWithDiagonalCovariance(),
              ),
              v=nn.Sequential(
                  nn.Linear(hidden_size, 1),
              ),
              adv=nn.Sequential(
                  ConcatObsAndAction(),
                  nn.Linear(hidden_size + action_size, 1),
              ),
          )
      if use_lstm:
          model = pfrl.nn.RecurrentSequential(
              nn.Linear(obs_size, hidden_size),
              nn.LeakyReLU(),
              nn.LSTM(
                  num_layers=1,
                  input_size=hidden_size,
                  hidden_size=hidden_size,
              ),
              head,
          )
      else:
          model = nn.Sequential(
              nn.Linear(obs_size, hidden_size),
              nn.LeakyReLU(),
              head,
          )
      eps = 1e-8
      opt = pfrl.optimizers.SharedRMSpropEpsInsideSqrt(
          model.parameters(), lr=1e-3, eps=eps, alpha=0.99
      )
      gamma = 0.5
      beta = 1e-5
      if self.n_times_replay == 0 and self.disable_online_update:
          # At least one of them must be enabled
          pytest.skip()
      agent = acer.ACER(
          model,
          opt,
          replay_buffer=replay_buffer,
          t_max=t_max,
          gamma=gamma,
          beta=beta,
          n_times_replay=self.n_times_replay,
          act_deterministically=True,
          disable_online_update=self.disable_online_update,
          replay_start_size=100,
          use_trust_region=self.use_trust_region,
          recurrent=use_lstm,
      )
  
      max_episode_len = None if episodic else 2
  
      with warnings.catch_warnings(record=True) as warns:
          train_agent_async(
              outdir=self.outdir,
              processes=nproc,
              make_env=make_env,
              agent=agent,
              steps=steps,
              max_episode_len=max_episode_len,
              eval_interval=500,
              eval_n_steps=None,
              eval_n_episodes=5,
              successful_score=1,
          )
          assert len(warns) == 0, 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"))
  
      # Test
      env = make_env(0, True)
      n_test_runs = 5
      eval_returns, _ = run_evaluation_episodes(
          env,
          agent,
          n_steps=None,
          n_episodes=n_test_runs,
          max_episode_len=max_episode_len,
      )
      successful_return = 1
      if require_success:
          n_succeeded = np.sum(
              np.asarray(eval_returns) >= successful_return
          )
          assert n_succeeded == n_test_runs
  
imports: import copy
  import logging
  import os
  import tempfile
  import warnings
  import numpy
  import pytest
  import torch
  from torch import nn
  import pfrl
  from pfrl.agents import acer
  from pfrl.envs.abc import ABC
  from pfrl.experiments.evaluator import run_evaluation_episodes
  from pfrl.experiments.train_agent_async import train_agent_async
  from pfrl.nn import ConcatObsAndAction
  from pfrl.policies import GaussianHeadWithDiagonalCovariance, SoftmaxCategoricalHead
  from pfrl.q_functions import DiscreteActionValueHead
  from pfrl.replay_buffers import EpisodicReplayBuffer
label_desc: Use image node_id 4 for calling the _TestACER obj's underlying member method code with example usage: obj._test_abc(t_max, use_lstm, discrete, episodic, steps, require_success) and returns: ABC