code: def learn_from_batch(self, batch):
      #########################################
      # need to update the following networks:
      # 1. actor (policy)
      # 2. state value (v)
      # 3. critic (q1 and q2)
      # 4. target network - probably already handled by V
  
      #########################################
      # define the networks to be used
  
      # State Value Network
      value_network = self.networks["v"]
      value_network_keys = self.ap.network_wrappers[
          "v"
      ].input_embedders_parameters.keys()
  
      # Critic Network
      q_network = self.networks["q"].online_network
      q_head = q_network.output_heads[0]
      q_network_keys = self.ap.network_wrappers[
          "q"
      ].input_embedders_parameters.keys()
  
      # Actor (policy) Network
      policy_network = self.networks["policy"].online_network
      policy_network_keys = self.ap.network_wrappers[
          "policy"
      ].input_embedders_parameters.keys()
  
      ##########################################
      # 1. updating the actor - according to (13) in the paper
      policy_inputs = copy.copy(batch.states(policy_network_keys))
      policy_results = policy_network.predict(policy_inputs)
  
      (
          policy_mu,
          policy_std,
          sampled_raw_actions,
          sampled_actions,
          sampled_actions_logprob,
          sampled_actions_logprob_mean,
      ) = policy_results
  
      self.policy_means.add_sample(policy_mu)
      self.policy_logsig.add_sample(policy_std)
      self.policy_logprob_sampled.add_sample(
          sampled_actions_logprob_mean
      )
  
      # get the state-action values for the replayed states and their corresponding actions from the policy
      q_inputs = copy.copy(batch.states(q_network_keys))
      q_inputs["output_0_0"] = sampled_actions
      log_target = q_network.predict(q_inputs)[0].squeeze()
  
      # log internal q values
      q1_vals, q2_vals = q_network.predict(
          q_inputs, outputs=[q_head.q1_output, q_head.q2_output]
      )
      self.q1_values.add_sample(q1_vals)
      self.q2_values.add_sample(q2_vals)
  
      # calculate the gradients according to (13)
      # get the gradients of log_prob w.r.t the weights (parameters) - indicated as phi in the paper
      initial_feed_dict = {
          policy_network.gradients_weights_ph[5]: np.array(1.0)
      }
      dlogp_dphi = policy_network.predict(
          policy_inputs,
          outputs=policy_network.weighted_gradients[5],
          initial_feed_dict=initial_feed_dict,
      )
  
      # calculate dq_da
      dq_da = q_network.predict(
          q_inputs,
          outputs=q_network.gradients_wrt_inputs[1]["output_0_0"],
      )
  
      # calculate da_dphi
      initial_feed_dict = {
          policy_network.gradients_weights_ph[3]: dq_da
      }
      dq_dphi = policy_network.predict(
          policy_inputs,
          outputs=policy_network.weighted_gradients[3],
          initial_feed_dict=initial_feed_dict,
      )
  
      # now given dlogp_dphi, dq_dphi we need to calculate the policy gradients according to (13)
      policy_grads = [
          dlogp_dphi[l] - dq_dphi[l] for l in range(len(dlogp_dphi))
      ]
  
      # apply the gradients to policy networks
      policy_network.apply_gradients(policy_grads)
      grads_sumabs = np.sum(
          [
              np.sum(np.abs(policy_grads[l]))
              for l in range(len(policy_grads))
          ]
      )
      self.policy_grads.add_sample(grads_sumabs)
  
      ##########################################
      # 2. updating the state value online network weights
      # done by calculating the targets for the v head according to (5) in the paper
      # value_targets = log_targets-sampled_actions_logprob
      value_inputs = copy.copy(batch.states(value_network_keys))
      value_targets = log_target - sampled_actions_logprob
  
      self.v_onl_ys.add_sample(value_targets)
  
      # call value_network apply gradients with this target
      value_loss = value_network.online_network.train_on_batch(
          value_inputs, value_targets[:, None]
      )[0]
  
      ##########################################
      # 3. updating the critic (q networks)
      # updating q networks according to (7) in the paper
  
      # define the input to the q network: state has been already updated previously, but now we need
      # the actions from the batch (and not those sampled by the policy)
      q_inputs["output_0_0"] = batch.actions(
          len(batch.actions().shape) == 1
      )
  
      # define the targets : scale_reward * reward + (1-terminal)*discount*v_target_next_state
      # define v_target_next_state
      value_inputs = copy.copy(batch.next_states(value_network_keys))
      v_target_next_state = value_network.target_network.predict(
          value_inputs
      )
      self.v_tgt_ns.add_sample(v_target_next_state)
      # Note: reward is assumed to be rescaled by RewardRescaleFilter in the preset parameters
      TD_targets = (
          batch.rewards(expand_dims=True)
          + (1.0 - batch.game_overs(expand_dims=True))
          * self.ap.algorithm.discount
          * v_target_next_state
      )
  
      # call critic network update
      result = q_network.train_on_batch(
          q_inputs,
          TD_targets,
          additional_fetches=[q_head.q1_loss, q_head.q2_loss],
      )
      total_loss, losses, unclipped_grads = result[:3]
      q1_loss, q2_loss = result[3]
      self.TD_err1.add_sample(q1_loss)
      self.TD_err2.add_sample(q2_loss)
  
      ##########################################
      # 4. updating the value target network
      # I just need to set the parameter rate_for_copying_weights_to_target in the agent parameters to be 1-tau
      # where tau is the hyper parameter as defined in sac original implementation
  
      return total_loss, losses, unclipped_grads
  
imports: from typing import Union
  import copy
  import numpy
  from collections import OrderedDict
  from rl_coach.agents.agent import Agent
  from rl_coach.agents.policy_optimization_agent import PolicyOptimizationAgent
  from rl_coach.architectures.head_parameters import SACQHeadParameters, SACPolicyHeadParameters, VHeadParameters
  from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
  from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, AgentParameters, EmbedderScheme, MiddlewareScheme
  from rl_coach.core_types import ActionInfo, EnvironmentSteps, RunPhase
  from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
  from rl_coach.memories.non_episodic.experience_replay import ExperienceReplayParameters
  from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
  from rl_coach.spaces import BoxActionSpace
label_desc: Use image node_id 3 for calling the SoftActorCriticAgent obj's underlying member method code with example usage: obj.learn_from_batch(batch) and returns: total_loss,  losses,  unclipped_grads