code: def run_pre_network_filter_for_inference(
      self, state: StateType, update_internal_state: bool = False
  ):
      dummy_env_response = EnvResponse(
          next_state=state, reward=0, game_over=False
      )
      update_internal_state = (
          self.ap.algorithm.update_pre_network_filters_state_on_inference
      )
      return self.pre_network_filter.filter(
          dummy_env_response,
          update_internal_state=update_internal_state,
      )[0].next_state
  
imports: import copy
  import math
  from collections import OrderedDict
  from random import shuffle
  from typing import Union
  import numpy
  from rl_coach.agents.actor_critic_agent import ActorCriticAgent
  from rl_coach.agents.policy_optimization_agent import PolicyGradientRescaler
  from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
  from rl_coach.architectures.head_parameters import PPOHeadParameters, VHeadParameters
  from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
  from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, AgentParameters
  from rl_coach.core_types import EnvironmentSteps, Batch, EnvResponse, StateType
  from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
  from rl_coach.exploration_policies.categorical import CategoricalParameters
  from rl_coach.logger import screen
  from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
  from rl_coach.schedules import ConstantSchedule
  from rl_coach.spaces import DiscreteActionSpace, BoxActionSpace
label_desc: Use image node_id 8 for calling the ClippedPPOAgent obj's underlying member method code with example usage: obj.run_pre_network_filter_for_inference(state, update_internal_state) and returns: self