code: def _do_test_fault_fatal(self, config, fail_eval=False):
      # Test raises real error when out of workers.
      config.num_rollout_workers = 2
      config.env = "fault_env"
      # Make both worker idx=1 and 2 fail.
      config.env_config = {"bad_indices": [1, 2]}
      if fail_eval:
          config.evaluation_num_workers = 2
          config.evaluation_interval = 1
          config.evaluation_config = {
              # Make eval worker (index 1) fail.
              "env_config": {
                  "bad_indices": [1],
                  "evaluation": True,
              },
          }
  
      for _ in framework_iterator(config, frameworks=("torch", "tf")):
          a = config.build()
          self.assertRaises(Exception, lambda: a.train())
          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 4 for calling the TestWorkerFailures obj's underlying member method code with example usage: obj._do_test_fault_fatal(config, fail_eval) without return types