class_docstr: Large-scale implementation of Augmented Random Search in Ray.
class_docstr_tok: ["Large-scale","implementation","of","Augmented","Random","Search","in","Ray","."]
code: def restore_workers(self, workers: WorkerSet):
      restored = self.workers.probe_unhealthy_workers()
      if restored:
          self._sync_weights_to_workers(
              worker_set=self.workers, worker_ids=restored
          )
  
imports: import logging
  import random
  import time
  from collections import namedtuple
  from typing import Optional
  import numpy
  from rllib_ars.ars.ars_tf_policy import ARSTFPolicy
  import ray
  from ray.rllib.algorithms import Algorithm
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
  from ray.rllib.algorithms.es import optimizers, utils
  from ray.rllib.algorithms.es.es_tf_policy import rollout
  from ray.rllib.env.env_context import EnvContext
  from ray.rllib.evaluation.worker_set import WorkerSet
  from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID
  from ray.rllib.utils import FilterManager
  from ray.rllib.utils.actor_manager import FaultAwareApply
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.metrics import NUM_AGENT_STEPS_SAMPLED, NUM_AGENT_STEPS_TRAINED, NUM_ENV_STEPS_SAMPLED, NUM_ENV_STEPS_TRAINED
  from ray.rllib.utils.torch_utils import set_torch_seed
label_desc: Use image node_id 6 for calling the ARS obj's underlying member method code with example usage: obj.restore_workers(workers) without return types