code: def get_prediction_and_update_embeddings(self, states):
      # get the actions q values and the state embedding
      embedding, actions_q_values = self.networks[
          "main"
      ].online_network.predict(
          self.prepare_batch_for_inference(states, "main"),
          outputs=[
              self.networks["main"].online_network.state_embedding,
              self.networks["main"]
              .online_network.output_heads[0]
              .output,
          ],
      )
      if self.phase != RunPhase.TEST:
          # store the state embedding for inserting it to the DND later
          self.current_episode_state_embeddings.append(
              embedding[0].squeeze()
          )
      actions_q_values = actions_q_values[0][0]
      return actions_q_values
  
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 7 for calling the NECAgent obj's underlying member method code with example usage: obj.get_prediction_and_update_embeddings(states) and returns: actions_q_values