code: def __init__(
      self,
      agent_parameters: AgentParameters,
      spaces: SpacesDefinition,
      network_name: str,
      head_idx: int = 0,
      loss_weight: float = 1.0,
      is_local: bool = True,
      activation_function: str = "tanh",
      batchnorm: bool = True,
      dense_layer=Dense,
      is_training=False,
  ):
      super().__init__(
          agent_parameters,
          spaces,
          network_name,
          head_idx,
          loss_weight,
          is_local,
          activation_function,
          dense_layer=dense_layer,
          is_training=is_training,
      )
      self.name = "wolpertinger_actor_head"
      self.return_type = Embedding
      self.action_embedding_width = (
          agent_parameters.algorithm.action_embedding_width
      )
      self.batchnorm = batchnorm
      self.output_scale = (
          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 None
      )
  
file_path: coach/rl_coach/architectures/tensorflow_components/heads/wolpertinger_actor_head.py
imports: import tensorflow
  from rl_coach.architectures.tensorflow_components.layers import batchnorm_activation_dropout, Dense
  from rl_coach.architectures.tensorflow_components.heads.head import Head
  from rl_coach.base_parameters import AgentParameters
  from rl_coach.core_types import Embedding
  from rl_coach.spaces import SpacesDefinition, BoxActionSpace
label_desc: Use image node_id 1 to create a new WolpertingerActorHead object from inherited base classes: Head with example: obj = WolpertingerActorHead(agent_parameters, spaces, network_name, head_idx, loss_weight, is_local, activation_function, batchnorm, dense_layer, is_training)