code: def observe_transition(self, transition):
      # sum up the total shaped reward
      self.total_shaped_reward_in_current_episode += transition.reward
      self.shaped_reward.add_sample(transition.reward)
  
      # create and store the transition
      if self.phase in [RunPhase.TRAIN, RunPhase.HEATUP]:
          # for episodic memories we keep the transitions in a local buffer until the episode is ended.
          # for regular memories we insert the transitions directly to the memory
          self.current_episode_buffer.insert(transition)
          if (
              not isinstance(self.memory, EpisodicExperienceReplay)
              and not self.ap.algorithm.store_transitions_only_when_episodes_are_terminated
          ):
              self.call_memory("store", transition)
  
      if self.ap.visualization.dump_in_episode_signals:
          self.update_step_in_episode_log()
  
      return transition.game_over
  
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 33 for calling the Agent obj's underlying member method code with example usage: obj.observe_transition(transition) and returns: transition