docstr: Given a batch of transitions, calculates their target values and updates the network.
  
  :param batch: A list of transitions
  :return: The total loss of the training, the loss per head and the unclipped gradients
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code: def learn_from_batch(self, batch) -> Tuple[float, List, List]:
      """
      Given a batch of transitions, calculates their target values and updates the network.
  
      :param batch: A list of transitions
      :return: The total loss of the training, the loss per head and the unclipped gradients
      """
      return 0, [], []
  
imports: import copy
  import random
  from collections import OrderedDict
  from typing import Dict, List, Union, Tuple
  import numpy
  from six.moves import range
  from rl_coach.agents.agent_interface import AgentInterface
  from rl_coach.architectures.network_wrapper import NetworkWrapper
  from rl_coach.base_parameters import AgentParameters, Device, DeviceType, DistributedTaskParameters, Frameworks
  from rl_coach.core_types import RunPhase, PredictionType, EnvironmentEpisodes, ActionType, Batch, Episode, StateType
  from rl_coach.core_types import Transition, ActionInfo, TrainingSteps, EnvironmentSteps, EnvResponse
  from rl_coach.logger import screen, Logger, EpisodeLogger
  from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplay
  from rl_coach.saver import SaverCollection
  from rl_coach.spaces import SpacesDefinition, VectorObservationSpace, GoalsSpace, AttentionActionSpace
  from rl_coach.utils import Signal, force_list
  from rl_coach.utils import dynamic_import_and_instantiate_module_from_params
  from rl_coach.memories.backend.memory_impl import get_memory_backend
  from rl_coach.core_types import TimeTypes
  from rl_coach.off_policy_evaluators.ope_manager import OpeManager
  from rl_coach.core_types import PickledReplayBuffer, CsvDataset
label_desc: Use image node_id 21 for calling the Agent obj's underlying member method code with example usage: obj.learn_from_batch(batch) and returns: int,  list,  list