code: def _restart_environment_episode(self, force_environment_reset=False):
      # select start and end positions
      if self.experiment_suite:
          # if an expeirent suite is available, follow its given poses
          if self.current_pose >= len(self.current_experiment.poses):
              # load a new experiment
              self.current_experiment_idx = (
                  self.current_experiment_idx + 1
              ) % len(self.experiment_suite.get_experiments())
              self._load_experiment(self.current_experiment_idx)
  
          self.current_start_position_idx = (
              self.current_experiment.poses[self.current_pose][0]
          )
          self.current_goal = self.positions[
              self.current_experiment.poses[self.current_pose][1]
          ]
          self.current_pose += 1
      else:
          # go over all the possible positions in a cyclic manner
          self.current_start_position_idx = (
              self.current_start_position_idx + 1
          ) % self.num_positions
  
          # choose a random goal destination
          self.current_goal = random.choice(self.positions)
  
      try:
          self.game.start_episode(self.current_start_position_idx)
      except:
          self.game.connect()
          self.game.start_episode(self.current_start_position_idx)
  
      # start the game with some initial speed
      for i in range(self.num_speedup_steps):
          self.control = VehicleControl(
              throttle=1.0,
              brake=0,
              steer=0,
              hand_brake=False,
              reverse=False,
          )
          self.game.send_control(VehicleControl())
  
imports: import random
  import sys
  from os import path, environ
  from rl_coach.logger import screen
  from rl_coach.filters.action.partial_discrete_action_space_map import PartialDiscreteActionSpaceMap
  from rl_coach.filters.observation.observation_rgb_to_y_filter import ObservationRGBToYFilter
  from rl_coach.filters.observation.observation_to_uint8_filter import ObservationToUInt8Filter
  import os
  import signal
  import logging
  import subprocess
  import numpy
  from rl_coach.environments.environment import Environment, EnvironmentParameters, LevelSelection
  from rl_coach.spaces import BoxActionSpace, ImageObservationSpace, StateSpace, VectorObservationSpace
  from rl_coach.utils import get_open_port, force_list
  from enum import Enum
  from typing import List, Union
  from rl_coach.base_parameters import VisualizationParameters
  from rl_coach.filters.filter import InputFilter, NoOutputFilter
  from rl_coach.filters.observation.observation_rescale_to_size_filter import ObservationRescaleToSizeFilter
  from rl_coach.filters.observation.observation_stacking_filter import ObservationStackingFilter
label_desc: Use image node_id 9 for calling the CarlaEnvironment obj's underlying member method code with example usage: obj._restart_environment_episode(force_environment_reset) without return types