code: def get_all_q_values_for_states_and_softmax_probabilities(
      self, states: StateType
  ):
      actions_q_values, softmax_probabilities = None, None
      if self.exploration_policy.requires_action_values():
          outputs = [
              self.networks["main"]
              .online_network.output_heads[0]
              .q_values,
              self.networks["main"]
              .online_network.output_heads[0]
              .softmax,
          ]
          actions_q_values, softmax_probabilities = self.get_prediction(
              states, outputs=outputs
          )
  
      return actions_q_values, softmax_probabilities
  
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 5 for calling the CategoricalDQNAgent 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