code: def _validate_action(self, policy, action):
      if np.array(action).shape != ():
          raise ValueError(
              (
                  "The exploration_policy {} returned a vector of actions "
                  "instead of a single action. ValueOptimizationAgents "
                  "require exploration policies which return a single action."
              ).format(policy.__class__.__name__)
          )
  
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 7 for calling the ValueOptimizationAgent obj's underlying member method code with example usage: obj._validate_action(policy, action) without return types