code: def _do_test_fault_fatal_but_recreate(
      self, config, multi_agent=False
  ):
      # Counter that will survive restarts.
      COUNTER_NAME = f"_do_test_fault_fatal_but_recreate{'_ma' if multi_agent else ''}"
      counter = Counter.options(name=COUNTER_NAME).remote()
  
      # Test raises real error when out of workers.
      config.num_rollout_workers = 1
      config.evaluation_num_workers = 1
      config.evaluation_interval = 1
      config.env = (
          "fault_env" if not multi_agent else "multi_agent_fault_env"
      )
      config.evaluation_config = AlgorithmConfig.overrides(
          recreate_failed_workers=True,
          # 0 delay for testing purposes.
          delay_between_worker_restarts_s=0,
          # Make eval worker (index 1) fail.
          env_config={
              "bad_indices": [1],
              "failure_start_count": 3,
              "failure_stop_count": 4,
              "counter": COUNTER_NAME,
          },
          **(
              dict(
                  policy_mapping_fn=(
                      lambda aid, episode, worker, **kwargs: (
                          # Allows this test to query this
                          # different-from-training-workers policy mapping fn.
                          "This is the eval mapping fn"
                          if episode is None
                          else "main"
                          if episode.episode_id % 2 == aid
                          else "p{}".format(np.random.choice([0, 1]))
                      )
                  )
              )
              if multi_agent
              else {}
          ),
      )
  
      for _ in framework_iterator(config, frameworks=("tf2", "torch")):
          # Reset interaction counter.
          ray.wait([counter.reset.remote()])
  
          a = config.build()
  
          # This should also work several times.
          for _ in range(2):
              a.train()
              wait_for_restore()
              a.train()
  
              self.assertEqual(
                  a.workers.num_healthy_remote_workers(), 1
              )
              self.assertEqual(
                  a.evaluation_workers.num_healthy_remote_workers(), 1
              )
              if multi_agent:
                  # Make a dummy call to the eval worker's policy_mapping_fn and
                  # make sure the restored eval worker received the correct one from
                  # the eval config (not the main workers' one).
                  test = a.evaluation_workers.foreach_worker(
                      lambda w: w.policy_mapping_fn(0, None, None)
                  )
                  self.assertEqual(
                      test[0], "This is the eval mapping fn"
                  )
          a.stop()
  
imports: from collections import defaultdict
  import gymnasium
  import numpy
  import time
  import unittest
  import ray
  from ray.util.state import list_actors
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig
  from ray.rllib.algorithms.callbacks import DefaultCallbacks
  from ray.rllib.algorithms.dqn.dqn import DQNConfig
  from ray.rllib.algorithms.impala import ImpalaConfig
  from ray.rllib.algorithms.pg import PGConfig
  from ray.rllib.algorithms.pg.pg_tf_policy import PGTF2Policy
  from ray.rllib.algorithms.pg.pg_torch_policy import PGTorchPolicy
  from ray.rllib.algorithms.ppo import PPOConfig
  from ray.rllib.env.multi_agent_env import make_multi_agent
  from ray.rllib.evaluation.rollout_worker import RolloutWorker
  from ray.rllib.examples.env.random_env import RandomEnv
  from ray.rllib.utils.test_utils import framework_iterator
  from ray.tune.registry import register_env
label_desc: Use image node_id 5 for calling the TestWorkerFailures obj's underlying member method code with example usage: obj._do_test_fault_fatal_but_recreate(config, multi_agent) without return types