code: def _build_module(self, input_layer):
      # mean
      pre_activation_policy_value = self.dense_layer(
          self.action_embedding_width
      )(input_layer, name="actor_action_embedding")
      self.proto_action = batchnorm_activation_dropout(
          input_layer=pre_activation_policy_value,
          batchnorm=self.batchnorm,
          activation_function=self.activation_function,
          dropout_rate=0,
          is_training=self.is_training,
          name="BatchnormActivationDropout_0",
      )[-1]
      if self.output_scale is not None:
          self.proto_action = tf.multiply(
              self.proto_action, self.output_scale, name="proto_action"
          )
  
      self.output = [self.proto_action]
  
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 2 for calling the WolpertingerActorHead obj's underlying member method code with example usage: obj._build_module(input_layer) without return types