code: def _random_noop(self):
      # simulate a random initial environment state by stepping for a random number of times between 0 and 30
      step_count = 0
      random_initialization_steps = random.randint(
          0, self.random_initialization_steps
      )
      while self.action_space is not None and (
          self.state is None or step_count < random_initialization_steps
      ):
          step_count += 1
          self.step(self.action_space.default_action)
  
imports: import gym
  import numpy
  from enum import IntEnum
  import scipy.ndimage
  from rl_coach.graph_managers.graph_manager import ScheduleParameters
  from rl_coach.utils import lower_under_to_upper, short_dynamic_import
  from typing import Dict, Any, Union
  from rl_coach.core_types import RunPhase, EnvironmentSteps
  from rl_coach.environments.environment import Environment, EnvironmentParameters, LevelSelection
  from rl_coach.spaces import DiscreteActionSpace, BoxActionSpace, ImageObservationSpace, VectorObservationSpace, PlanarMapsObservationSpace, TensorObservationSpace, StateSpace, RewardSpace
  from rl_coach.filters.filter import NoInputFilter, NoOutputFilter
  from rl_coach.filters.reward.reward_clipping_filter import RewardClippingFilter
  from rl_coach.filters.observation.observation_rescale_to_size_filter import ObservationRescaleToSizeFilter
  from rl_coach.filters.observation.observation_stacking_filter import ObservationStackingFilter
  from rl_coach.filters.observation.observation_rgb_to_y_filter import ObservationRGBToYFilter
  from rl_coach.filters.observation.observation_to_uint8_filter import ObservationToUInt8Filter
  from rl_coach.filters.filter import InputFilter
  import random
  from rl_coach.base_parameters import VisualizationParameters
  from rl_coach.logger import screen
label_desc: Use image node_id 5 for calling the GymEnvironment obj's underlying member method code with example usage: obj._random_noop() without return types