code: def init_environment_dependent_modules(self):
      super().init_environment_dependent_modules()
      if isinstance(self.spaces.action, DiscreteActionSpace):
          for i in range(len(self.spaces.action.actions)):
              self.q_value_for_action[i] = self.register_signal(
                  "Q for action {}".format(i),
                  dump_one_value_per_episode=False,
                  dump_one_value_per_step=True,
              )
  
imports: from collections import OrderedDict
  from typing import Union, List
  import numpy
  from rl_coach.agents.agent import Agent
  from rl_coach.core_types import ActionInfo, StateType, Batch
  from rl_coach.filters.filter import NoInputFilter
  from rl_coach.logger import screen
  from rl_coach.memories.non_episodic.prioritized_experience_replay import PrioritizedExperienceReplay
  from rl_coach.spaces import DiscreteActionSpace
  from copy import deepcopy, copy
label_desc: Use image node_id 2 for calling the ValueOptimizationAgent obj's underlying member method code with example usage: obj.init_environment_dependent_modules() without return types