class_docstr: Write me
      
class_docstr_tok: ["Write","me"]
code: def compile(self, optimizer, metrics=[]):
      metrics += [mean_q]
  
      if type(optimizer) in (list, tuple):
          if len(optimizer) != 2:
              raise ValueError(
                  "More than two optimizers provided. Please only provide a maximum of two optimizers, the first one for the actor and the second one for the critic."
              )
          actor_optimizer, critic_optimizer = optimizer
      else:
          actor_optimizer = optimizer
          critic_optimizer = clone_optimizer(optimizer)
      if type(actor_optimizer) is str:
          actor_optimizer = optimizers.get(actor_optimizer)
      if type(critic_optimizer) is str:
          critic_optimizer = optimizers.get(critic_optimizer)
      assert actor_optimizer != critic_optimizer
  
      if (
          len(metrics) == 2
          and hasattr(metrics[0], "__len__")
          and hasattr(metrics[1], "__len__")
      ):
          actor_metrics, critic_metrics = metrics
      else:
          actor_metrics = critic_metrics = metrics
  
      def clipped_error(y_true, y_pred):
          return K.mean(
              huber_loss(y_true, y_pred, self.delta_clip), axis=-1
          )
  
      # Compile target networks. We only use them in feed-forward mode, hence we can pass any
      # optimizer and loss since we never use it anyway.
      self.target_actor = clone_model(
          self.actor, self.custom_model_objects
      )
      self.target_actor.compile(optimizer="sgd", loss="mse")
      self.target_critic = clone_model(
          self.critic, self.custom_model_objects
      )
      self.target_critic.compile(optimizer="sgd", loss="mse")
  
      # We also compile the actor. We never optimize the actor using Keras but instead compute
      # the policy gradient ourselves. However, we need the actor in feed-forward mode, hence
      # we also compile it with any optimzer and
      self.actor.compile(optimizer="sgd", loss="mse")
  
      # Compile the critic.
      if self.target_model_update < 1.0:
          # We use the `AdditionalUpdatesOptimizer` to efficiently soft-update the target model.
          critic_updates = get_soft_target_model_updates(
              self.target_critic, self.critic, self.target_model_update
          )
          critic_optimizer = AdditionalUpdatesOptimizer(
              critic_optimizer, critic_updates
          )
      self.critic.compile(
          optimizer=critic_optimizer,
          loss=clipped_error,
          metrics=critic_metrics,
      )
  
      # Combine actor and critic so that we can get the policy gradient.
      # Assuming critic's state inputs are the same as actor's.
      combined_inputs = []
      state_inputs = []
      for i in self.critic.input:
          if i == self.critic_action_input:
              combined_inputs.append([])
          else:
              combined_inputs.append(i)
              state_inputs.append(i)
      combined_inputs[self.critic_action_input_idx] = self.actor(
          state_inputs
      )
  
      combined_output = self.critic(combined_inputs)
  
      updates = actor_optimizer.get_updates(
          params=self.actor.trainable_weights,
          loss=-K.mean(combined_output),
      )
      if self.target_model_update < 1.0:
          # Include soft target model updates.
          updates += get_soft_target_model_updates(
              self.target_actor, self.actor, self.target_model_update
          )
      updates += (
          self.actor.updates
      )  # include other updates of the actor, e.g. for BN
  
      # Finally, combine it all into a callable function.
      if K.backend() == "tensorflow":
          self.actor_train_fn = K.function(
              state_inputs + [K.learning_phase()],
              [self.actor(state_inputs)],
              updates=updates,
          )
      else:
          if self.uses_learning_phase:
              state_inputs += [K.learning_phase()]
          self.actor_train_fn = K.function(
              state_inputs, [self.actor(state_inputs)], updates=updates
          )
      self.actor_optimizer = actor_optimizer
  
      self.compiled = True
  
imports: from __future__ import division
  from collections import deque
  import os
  import warnings
  import numpy
  import keras.backend
  import keras.optimizers
  from rl.core import Agent
  from rl.random import OrnsteinUhlenbeckProcess
  from rl.util import *
label_desc: Use image node_id 3 for calling the DDPGAgent obj's underlying member method code with example usage: obj.compile(optimizer, metrics) and returns: K