code: def learn_from_batch(self, batch):
      actor = self.networks["actor"]
      critic = self.networks["critic"]
  
      actor_keys = self.ap.network_wrappers[
          "actor"
      ].input_embedders_parameters.keys()
      critic_keys = self.ap.network_wrappers[
          "critic"
      ].input_embedders_parameters.keys()
  
      # TD error = r + discount*max(q_st_plus_1) - q_st
      next_actions, actions_mean = actor.parallel_prediction(
          [
              (actor.target_network, batch.next_states(actor_keys)),
              (actor.online_network, batch.states(actor_keys)),
          ]
      )
  
      # add noise to the next_actions
      noise = np.random.normal(
          0, self.ap.algorithm.policy_noise, next_actions.shape
      ).clip(
          -self.ap.algorithm.noise_clipping,
          self.ap.algorithm.noise_clipping,
      )
      next_actions = self.spaces.action.clip_action_to_space(
          next_actions + noise
      )
  
      critic_inputs = copy.copy(batch.next_states(critic_keys))
      critic_inputs["action"] = next_actions
      q_st_plus_1 = critic.target_network.predict(critic_inputs)[
          2
      ]  # output #2 is the min (Q1, Q2)
  
      # calculate the bootstrapped TD targets while discounting terminal states according to
      # use_non_zero_discount_for_terminal_states
      if self.ap.algorithm.use_non_zero_discount_for_terminal_states:
          TD_targets = (
              batch.rewards(expand_dims=True)
              + self.ap.algorithm.discount * q_st_plus_1
          )
      else:
          TD_targets = (
              batch.rewards(expand_dims=True)
              + (1.0 - batch.game_overs(expand_dims=True))
              * self.ap.algorithm.discount
              * q_st_plus_1
          )
  
      # clip the TD targets to prevent overestimation errors
      if self.ap.algorithm.clip_critic_targets:
          TD_targets = np.clip(
              TD_targets, *self.ap.algorithm.clip_critic_targets
          )
  
      self.TD_targets_signal.add_sample(TD_targets)
  
      # train the critic
      critic_inputs = copy.copy(batch.states(critic_keys))
      critic_inputs["action"] = batch.actions(
          len(batch.actions().shape) == 1
      )
      result = critic.train_and_sync_networks(critic_inputs, TD_targets)
      total_loss, losses, unclipped_grads = result[:3]
  
      if (
          self.training_iteration
          % self.ap.algorithm.update_policy_every_x_episode_steps
          == 0
      ):
          # get the gradients of output #3 (=mean of Q1 network) w.r.t the action
          critic_inputs = copy.copy(batch.states(critic_keys))
          critic_inputs["action"] = actions_mean
          action_gradients = critic.online_network.predict(
              critic_inputs,
              outputs=critic.online_network.gradients_wrt_inputs[3][
                  "action"
              ],
          )
  
          # apply the gradients from the critic to the actor
          initial_feed_dict = {
              actor.online_network.gradients_weights_ph[
                  0
              ]: -action_gradients
          }
          gradients = actor.online_network.predict(
              batch.states(actor_keys),
              outputs=actor.online_network.weighted_gradients[0],
              initial_feed_dict=initial_feed_dict,
          )
  
          if actor.has_global:
              actor.apply_gradients_to_global_network(gradients)
              actor.update_online_network()
          else:
              actor.apply_gradients_to_online_network(gradients)
  
      return total_loss, losses, unclipped_grads
  
imports: import copy
  from typing import Union
  from collections import OrderedDict
  import numpy
  from rl_coach.agents.agent import Agent
  from rl_coach.agents.ddpg_agent import DDPGAgent
  from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
  from rl_coach.architectures.head_parameters import DDPGActorHeadParameters, TD3VHeadParameters
  from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
  from rl_coach.base_parameters import NetworkParameters, AlgorithmParameters, AgentParameters, EmbedderScheme
  from rl_coach.core_types import ActionInfo, TrainingSteps, Transition
  from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
  from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
  from rl_coach.spaces import BoxActionSpace, GoalsSpace
label_desc: Use image node_id 3 for calling the TD3Agent obj's underlying member method code with example usage: obj.learn_from_batch(batch) and returns: total_loss,  losses,  unclipped_grads