code: def handle_episode_ended(self):
      last_episode = self.current_episode_buffer
      if (
          self.phase in [RunPhase.TRAIN, RunPhase.HEATUP]
          and last_episode
      ):
          self._update_measurements_targets(
              last_episode, self.ap.algorithm.num_predicted_steps_ahead
          )
      super().handle_episode_ended()
  
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 6 for calling the DFPAgent obj's underlying member method code with example usage: obj.handle_episode_ended() without return types