code: def test_eval_workers_fault_but_recover(self):
      # Counter that will survive restarts.
      COUNTER_NAME = "test_eval_workers_fault_but_recover"
      counter = Counter.options(name=COUNTER_NAME).remote()
  
      config = (
          PGConfig()
          .rollouts(
              env_runner_cls=ForwardHealthCheckToEnvWorker,
              num_rollout_workers=2,
              rollout_fragment_length=16,
          )
          .training(
              train_batch_size=32,
              model={"fcnet_hiddens": [4]},
          )
          .environment(env="fault_env")
          .evaluation(
              evaluation_num_workers=2,
              evaluation_interval=1,
              evaluation_config=PGConfig.overrides(
                  env_config={
                      "evaluation": True,
                      "p_terminated": 0.0,
                      "max_episode_len": 20,
                      # Make both eval workers fail.
                      "bad_indices": [1, 2],
                      # Env throws error between steps 10 and 12.
                      "failure_start_count": 3,
                      "failure_stop_count": 4,
                      "counter": COUNTER_NAME,
                  },
              ),
          )
          .fault_tolerance(
              recreate_failed_workers=True,  # And recover
              # 0 delay for testing purposes.
              delay_between_worker_restarts_s=0,
          )
      )
  
      for _ in framework_iterator(config, frameworks=("tf2", "torch")):
          # Reset interaciton counter.
          ray.wait([counter.reset.remote()])
  
          a = config.build()
  
          # Before train loop, workers are fresh and not recreated.
          self.assertEqual(
              a.evaluation_workers.num_healthy_remote_workers(), 2
          )
          self.assertEqual(
              a.evaluation_workers.num_remote_worker_restarts(), 0
          )
  
          a.train()
          wait_for_restore()
          a.train()
  
          # Everything still healthy. And all workers are restarted.
          self.assertEqual(
              a.evaluation_workers.num_healthy_remote_workers(), 2
          )
          self.assertEqual(
              a.evaluation_workers.num_remote_worker_restarts(), 2
          )
  
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 17 for calling the TestWorkerFailures obj's underlying member method code with example usage: obj.test_eval_workers_fault_but_recover() without return types