code: def __init__(
      self,
      config: AlgorithmConfig,
      env_creator,
      noise,
      worker_index,
      min_task_runtime=0.2,
  ):
      # Set Python random, numpy, env, and torch/tf seeds.
      seed = config.seed
      if seed is not None:
          # Python random module.
          random.seed(seed)
          # Numpy.
          np.random.seed(seed)
          # Torch.
          if config.framework_str == "torch":
              set_torch_seed(seed)
  
      self.min_task_runtime = min_task_runtime
      self.config = config
      self.noise = SharedNoiseTable(noise)
  
      env_context = EnvContext(self.config.env_config, worker_index)
      self.env = env_creator(env_context)
      # Seed the env, if gym.Env.
      if not hasattr(self.env, "seed"):
          logger.info(
              "Env doesn't support env.seed(): {}".format(self.env)
          )
      # Gym.env.
      else:
          self.env.seed(seed)
  
      from ray.rllib import models
  
      self.preprocessor = models.ModelCatalog.get_preprocessor(self.env)
  
      policy_cls = get_policy_class(self.config)
      self.policy = policy_cls(
          self.env.observation_space,
          self.env.action_space,
          config.to_dict(),
      )
  
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 1 to create a new Worker object from inherited base classes: FaultAwareApply with example: obj = Worker(config, env_creator, noise, worker_index, min_task_runtime)