How to use the rlbot.botmanager.bot_helper_process.BotHelperProcess function in rlbot

from rlbot.botmanager.bot_helper_process import BotHelperProcess
from rlbot.utils.logging_utils import get_logger
import sys
from time import sleep
import psutil
from tensorboardX import SummaryWriter

name = 'online'
load_model = False
load_exp = False


class TorchManager(BotHelperProcess):
    def __init__(self, agent_metadata_queue, quit_event):
        super().__init__(agent_metadata_queue, quit_event)

        from multiprocessing.managers import BaseManager
        from leviathan.torch_memory import RewardMemory
        BaseManager.register('Memory', RewardMemory)

        self.logger = get_logger('torch_mgr')
        self.metadata_list = [None, None]
        self.manager = BaseManager()
        self.manager.start()
        self.game_memory = self.manager.Memory()
        self.writer = SummaryWriter()
        self.n_iter = 0

        if load_exp:

import os
import sys
import psutil
from rlbot.botmanager.bot_helper_process import BotHelperProcess
from rlbot.utils.logging_utils import get_logger
import time

from framework.utils import get_repo_directory


class BaseHiveManager(BotHelperProcess):

    batch_size = 2000
    memory_size = 100000

    def __init__(self, agent_metadata_queue, quit_event):
        super().__init__(agent_metadata_queue, quit_event)
        sys.path.insert(0, get_repo_directory())  # this is for separate process imports
        self.logger = get_logger('base_hive_mgr')

        self.actor_model = self.get_model()
        self.shared_model_handle = self.get_shared_model_handle()
        self.manager = self.setup_manager()
        self.game_memory = self.manager.Memory(self.memory_size, self.actor_model.get_input_state_dimension(),
                                               self.actor_model.get_model_output_dimension())
        self.model_path = None
        self.load_model = None