code: def get_all_q_values_for_states(self, states: StateType):
      q_values = None
      if self.exploration_policy.requires_action_values():
          q_values = self.get_prediction(
              states,
              outputs=[
                  self.networks["main"]
                  .online_network.output_heads[0]
                  .q_values
              ],
          )
  
      return q_values
  
imports: from typing import Union
  import numpy
  from rl_coach.agents.dqn_agent import DQNNetworkParameters, DQNAlgorithmParameters, DQNAgentParameters
  from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
  from rl_coach.architectures.head_parameters import CategoricalQHeadParameters
  from rl_coach.core_types import StateType
  from rl_coach.exploration_policies.e_greedy import EGreedyParameters
  from rl_coach.memories.non_episodic.prioritized_experience_replay import PrioritizedExperienceReplay
  from rl_coach.schedules import LinearSchedule
label_desc: Use image node_id 4 for calling the CategoricalDQNAgent obj's underlying member method code with example usage: obj.get_all_q_values_for_states(states) and returns: q_values