code: def test_worker_recover_with_hanging_workers(self):
      # Counter that will survive restarts.
      COUNTER_NAME = "test_eval_workers_fault_but_recover"
      counter = Counter.options(name=COUNTER_NAME).remote()
  
      config = (
          # Must use off-policy algorithm since we are gonna have hanging workers.
          ImpalaConfig()
          .resources(
              num_gpus=0,
          )
          .rollouts(
              env_runner_cls=ForwardHealthCheckToEnvWorker,
              num_rollout_workers=3,
              rollout_fragment_length=16,
          )
          .training(
              train_batch_size=32,
              model={"fcnet_hiddens": [4]},
          )
          .reporting(
              # Make sure each iteration doesn't take too long.
              min_time_s_per_iteration=0.5,
              # Make sure metrics reporting doesn't hang for too long
              # since we are gonna have a hanging worker.
              metrics_episode_collection_timeout_s=1,
          )
          .environment(
              env="fault_env",
              env_config={
                  "evaluation": True,
                  "p_terminated": 0.0,
                  "max_episode_len": 20,
                  # Worker 1 and 2 will fail in step().
                  "bad_indices": [1, 2],
                  # Env throws error between steps 3 and 4.
                  "failure_start_count": 3,
                  "failure_stop_count": 4,
                  "counter": COUNTER_NAME,
                  # Worker 2 will hang for long time during init after restart.
                  "init_delay": 3600,
                  "init_delay_indices": [2],
                  # Worker 3 will hang in env.step().
                  "step_delay": 3600,
                  "step_delay_indices": [3],
              },
          )
          .fault_tolerance(
              recreate_failed_workers=True,  # And recover
              worker_health_probe_timeout_s=0.01,
              worker_restore_timeout_s=5,
              delay_between_worker_restarts_s=0,  # For testing, no delay.
          )
      )
  
      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.workers.num_healthy_remote_workers(), 3)
          self.assertEqual(a.workers.num_remote_worker_restarts(), 0)
  
          a.train()
          wait_for_restore(num_restarting_allowed=1)
          # Most importantly, training progressed fine.
          a.train()
  
          # 2 healthy remote workers left, although worker 3 is stuck in rollout.
          self.assertEqual(a.workers.num_healthy_remote_workers(), 2)
          # Only 1 successful restore, since worker 2 is stuck in indefinite init
          # and can not be properly restored.
          self.assertEqual(a.workers.num_remote_worker_restarts(), 1)
  
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 18 for calling the TestWorkerFailures obj's underlying member method code with example usage: obj.test_worker_recover_with_hanging_workers() without return types