code: def get_initialized_knn(self):
      num_actions = len(self.spaces.action.actions)
      action_max_abs_range = (
          self.spaces.action.filtered_action_space.max_abs_range
          if (
              hasattr(self.spaces.action, "filtered_action_space")
              and isinstance(
                  self.spaces.action.filtered_action_space,
                  BoxActionSpace,
              )
          )
          else 1.0
      )
      keys = (
          np.expand_dims(
              (np.arange(num_actions) / (num_actions - 1) - 0.5) * 2, 1
          )
          * action_max_abs_range
      )
      values = np.expand_dims(np.arange(num_actions), 1)
      knn_tree = AnnoyDictionary(
          dict_size=num_actions,
          key_width=self.ap.algorithm.action_embedding_width,
      )
      knn_tree.add(keys, values, force_rebuild_tree=True)
  
      return knn_tree
  
imports: import copy
  from typing import Union
  from collections import OrderedDict
  import numpy
  from rl_coach.agents.ddpg_agent import DDPGAlgorithmParameters, DDPGActorNetworkParameters, DDPGCriticNetworkParameters, DDPGAgent
  from rl_coach.base_parameters import AgentParameters
  from rl_coach.core_types import ActionInfo
  from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
  from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
  from rl_coach.memories.non_episodic.differentiable_neural_dictionary import AnnoyDictionary
  from rl_coach.spaces import DiscreteActionSpace, BoxActionSpace
  from rl_coach.architectures.head_parameters import WolpertingerActorHeadParameters
label_desc: Use image node_id 6 for calling the WolpertingerAgent obj's underlying member method code with example usage: obj.get_initialized_knn() and returns: knn_tree