code: def __init__(
      self,
      agent_parameters,
      parent: Union["LevelManager", "CompositeAgent"] = None,
  ):
      super().__init__(agent_parameters, parent)
      self.current_episode_state_embeddings = []
      self.training_started = False
      self.current_episode_buffer = Episode(
          discount=self.ap.algorithm.discount,
          n_step=self.ap.algorithm.n_step,
          bootstrap_total_return_from_old_policy=self.ap.algorithm.bootstrap_total_return_from_old_policy,
      )
  
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 1 to create a new NECAgent object from inherited base classes: ValueOptimizationAgent with example: obj = NECAgent(agent_parameters, parent)