class_docstr: Write me
      
code: def compile(self, optimizer, metrics=[]):
      metrics += [mean_q]  # register default metrics
  
      # Create target V model. We don't need targets for mu or L.
      self.target_V_model = clone_model(
          self.V_model, self.custom_model_objects
      )
      self.target_V_model.compile(optimizer="sgd", loss="mse")
  
      # Build combined model.
      a_in = Input(shape=(self.nb_actions,), name="action_input")
      if type(self.V_model.input) is list:
          observation_shapes = [
              i._keras_shape[1:] for i in self.V_model.input
          ]
      else:
          observation_shapes = [self.V_model.input._keras_shape[1:]]
      os_in = [
          Input(shape=shape, name="observation_input_{}".format(idx))
          for idx, shape in enumerate(observation_shapes)
      ]
      L_out = self.L_model([a_in] + os_in)
      V_out = self.V_model(os_in)
  
      mu_out = self.mu_model(os_in)
      A_out = NAFLayer(self.nb_actions, mode=self.covariance_mode)(
          [L_out, mu_out, a_in]
      )
      combined_out = Lambda(
          lambda x: x[0] + x[1], output_shape=lambda x: x[0]
      )([A_out, V_out])
      combined = Model(inputs=[a_in] + os_in, outputs=[combined_out])
      # Compile combined model.
      if self.target_model_update < 1.0:
          # We use the `AdditionalUpdatesOptimizer` to efficiently soft-update the target model.
          updates = get_soft_target_model_updates(
              self.target_V_model,
              self.V_model,
              self.target_model_update,
          )
          optimizer = AdditionalUpdatesOptimizer(optimizer, updates)
  
      def clipped_error(y_true, y_pred):
          return K.mean(
              huber_loss(y_true, y_pred, self.delta_clip), axis=-1
          )
  
      combined.compile(
          loss=clipped_error, optimizer=optimizer, metrics=metrics
      )
      self.combined_model = combined
  
      self.compiled = True
  
imports: from __future__ import division
  import warnings
  import keras.backend
  from keras.models import Model
  from keras.layers import Lambda, Input, Layer, Dense
  from rl.core import Agent
  from rl.policy import EpsGreedyQPolicy, GreedyQPolicy
  from rl.util import *
label_desc: Use image node_id 6 for calling the NAFAgent obj's underlying member method code with example usage: obj.compile(optimizer, metrics) and returns: K