code: def handle_episode_ended(self):
      super().handle_episode_ended()
  
      # get the last full episode that we have collected
      episode = self.call_memory("get_last_complete_episode")
      if episode is not None and self.phase != RunPhase.TEST:
          assert (
              len(self.current_episode_state_embeddings)
              == episode.length()
          )
          discounted_rewards = episode.get_transitions_attribute(
              "n_step_discounted_rewards"
          )
          actions = episode.get_transitions_attribute("action")
          self.networks["main"].online_network.output_heads[0].DND.add(
              self.current_episode_state_embeddings,
              actions,
              discounted_rewards,
          )
  
imports: import os
  import pickle
  from typing import Union, List
  import numpy
  from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
  from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
  from rl_coach.architectures.head_parameters import DNDQHeadParameters
  from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
  from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, AgentParameters
  from rl_coach.core_types import RunPhase, EnvironmentSteps, Episode, StateType
  from rl_coach.exploration_policies.e_greedy import EGreedyParameters
  from rl_coach.logger import screen
  from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters, MemoryGranularity
  from rl_coach.schedules import ConstantSchedule
label_desc: Use image node_id 9 for calling the NECAgent obj's underlying member method code with example usage: obj.handle_episode_ended() without return types