code: def act(self):
      if self.phase == RunPhase.HEATUP:
          # get embedding in heatup (otherwise we get it through get_prediction)
          embedding = self.networks["main"].online_network.predict(
              self.prepare_batch_for_inference(self.curr_state, "main"),
              outputs=self.networks[
                  "main"
              ].online_network.state_embedding,
          )
          self.current_episode_state_embeddings.append(
              embedding.squeeze()
          )
  
      return super().act()
  
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 4 for calling the NECAgent obj's underlying member method code with example usage: obj.act() and returns: super