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