code: def learn_from_batch(self, batch):
      network_keys = self.ap.network_wrappers[
          "main"
      ].input_embedders_parameters.keys()
  
      network_inputs = batch.states(network_keys)
      network_inputs["goal"] = np.repeat(
          np.expand_dims(self.current_goal, 0), batch.size, axis=0
      )
  
      # get the current outputs of the network
      targets = self.networks["main"].online_network.predict(
          network_inputs
      )
  
      # change the targets for the taken actions
      for i in range(batch.size):
          targets[i, batch.actions()[i]] = (
              batch[i].info["future_measurements"].flatten()
          )
  
      result = self.networks["main"].train_and_sync_networks(
          network_inputs, targets
      )
      total_loss, losses, unclipped_grads = result[:3]
  
      return total_loss, losses, unclipped_grads
  
imports: import copy
  from enum import Enum
  from typing import Union
  import numpy
  from rl_coach.agents.agent import Agent
  from rl_coach.architectures.head_parameters import MeasurementsPredictionHeadParameters
  from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
  from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
  from rl_coach.architectures.tensorflow_components.layers import Conv2d, Dense
  from rl_coach.base_parameters import AlgorithmParameters, AgentParameters, NetworkParameters, MiddlewareScheme
  from rl_coach.core_types import ActionInfo, EnvironmentSteps, RunPhase
  from rl_coach.exploration_policies.e_greedy import EGreedyParameters
  from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
  from rl_coach.memories.memory import MemoryGranularity
  from rl_coach.spaces import SpacesDefinition, VectorObservationSpace
label_desc: Use image node_id 3 for calling the DFPAgent obj's underlying member method code with example usage: obj.learn_from_batch(batch) and returns: total_loss,  losses,  unclipped_grads