class_docstr: Large-scale implementation of Evolution Strategies in Ray.
class_docstr_tok: ["Large-scale","implementation","of","Evolution","Strategies","in","Ray","."]
code: def set_weights(self, weights: Dict[PolicyID, dict]):
      self.policy.set_flat_weights(weights)
  
imports: import logging
  import random
  import time
  from collections import namedtuple
  from typing import Dict, List, Optional
  import numpy
  from rllib_es.es import optimizers, utils
  from rllib_es.es.es_tf_policy import ESTFPolicy, rollout
  import ray
  from ray.rllib.algorithms import Algorithm
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
  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.deprecation import Deprecated
  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
  from ray.rllib.utils.typing import PolicyID
label_desc: Use image node_id 12 for calling the ES obj's underlying member method code with example usage: obj.set_weights(weights) without return types