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 get_policy(self, policy=DEFAULT_POLICY_ID):
      if policy != DEFAULT_POLICY_ID:
          raise ValueError(
              "ARS has no policy '{}'! Use {} "
              "instead.".format(policy, DEFAULT_POLICY_ID)
          )
      return self.policy
  
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 3 for calling the ARS obj's underlying member method code with example usage: obj.get_policy(policy) and returns: self