code: def learn_from_batch(self, batch):
      # replay buffer holds the actions in the discrete manner, as the agent is expected to act with discrete actions
      # with the BoxDiscretization output filter. But DDPG needs to work on continuous actions, thus converting to
      # continuous actions. This is actually a duplicate since this filtering is also done before applying actions on
      # the environment. So might want to somehow reuse that conversion. Maybe can hold this information in the info
      # dictionary of the transition.
  
      output_action_filter = list(
          self.output_filter.action_filters.values()
      )[0]
      continuous_actions = []
      for action in batch.actions():
          continuous_actions.append(output_action_filter.filter(action))
      batch._actions = np.array(continuous_actions).squeeze()
  
      return super().learn_from_batch(batch)
  
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 2 for calling the WolpertingerAgent obj's underlying member method code with example usage: obj.learn_from_batch(batch) and returns: super