code: def get_all_q_values_for_states(self, states: StateType):
      if self.exploration_policy.requires_action_values():
          quantile_values = self.get_prediction(states)
          actions_q_values = self.get_q_values(quantile_values)
      else:
          actions_q_values = None
      return actions_q_values
  
imports: from copy import copy
  from typing import Union
  import numpy
  from rl_coach.agents.dqn_agent import DQNAgentParameters, DQNNetworkParameters, DQNAlgorithmParameters
  from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
  from rl_coach.architectures.head_parameters import QuantileRegressionQHeadParameters
  from rl_coach.core_types import StateType
  from rl_coach.schedules import LinearSchedule
label_desc: Use image node_id 4 for calling the QuantileRegressionDQNAgent obj's underlying member method code with example usage: obj.get_all_q_values_for_states(states) and returns: actions_q_values