How to use the lightwood.config.config.CONFIG.USE_CUDA function in lightwood

def run_full_test(USE_CUDA, CACHE_ENCODED_DATA, SELFAWARE, PLINEAR):
    '''
    Run full test example with home_rentals dataset
    '''
    lightwood.config.config.CONFIG.USE_CUDA = USE_CUDA
    lightwood.config.config.CONFIG.PLINEAR = PLINEAR

    config = {'input_features': [
                        {'name': 'number_of_bathrooms', 'type': 'numeric'}, {'name': 'sqft', 'type': 'numeric'},
                        {'name': 'days_on_market', 'type': 'numeric'},
                        {'name': 'neighborhood', 'type': 'categorical','dropout':0.4}],
     'output_features': [{'name': 'number_of_rooms', 'type': 'categorical',
                       'weights':{
                             '0': 0.8,
                             '1': 0.6,
                             '2': 0.5,
                             '3': 0.7,
                             '4': 1,
                       }
    },{'name': 'rental_price', 'type': 'numeric'},{'name': 'location', 'type': 'categorical'}],
    'data_source': {'cache_transformed_data':CACHE_ENCODED_DATA},

def train(self):
        lightwood.config.config.CONFIG.USE_CUDA = self.transaction.lmd['use_gpu']
        lightwood.config.config.CONFIG.CACHE_ENCODED_DATA = not self.transaction.lmd['force_disable_cache']
        lightwood.config.config.CONFIG.SELFAWARE = self.transaction.lmd['use_selfaware_model']

        if self.transaction.lmd['model_order_by'] is not None and len(self.transaction.lmd['model_order_by']) > 0:
            self.transaction.log.debug('Reshaping data into timeseries format, this may take a while !')
            train_df = self._create_timeseries_df(self.transaction.input_data.train_df)
            test_df = self._create_timeseries_df(self.transaction.input_data.test_df)
            self.transaction.log.debug('Done reshaping data into timeseries format !')
        else:
            train_df = self.transaction.input_data.train_df
            test_df = self.transaction.input_data.test_df

        lightwood_config = self._create_lightwood_config()

        if self.transaction.lmd['skip_model_training'] == True:
            self.predictor = lightwood.Predictor(load_from_path=os.path.join(CONFIG.MINDSDB_STORAGE_PATH, self.transaction.lmd['name'] + '_lightwood_data'))

def get_devices():
    device_str = "cuda" if CONFIG.USE_CUDA else "cpu"
    if CONFIG.USE_DEVICE is not None:
        device_str = CONFIG.USE_DEVICE
    device = torch.device(device_str)

    available_devices = 1
    if device_str == 'cuda':
        available_devices = torch.cuda.device_count()

    return device, available_devices

def __init__(self, ds, dynamic_parameters, shape=None, selfaware=False, size_parameters={}, pretrained_net=None):
        self.input_size = None
        self.output_size = None
        self.selfaware = selfaware
        # How many devices we can train this network on
        self.available_devices = 1
        self.max_variance = None
        if ds is not None:
            self.out_indexes = ds.out_indexes

        device_str = "cuda" if CONFIG.USE_CUDA else "cpu"
        if CONFIG.USE_DEVICE is not None:
            device_str = CONFIG.USE_DEVICE
        self.device = torch.device(device_str)

        if CONFIG.DETERMINISTIC:
            '''
                Seed that always has the same value on the same dataset plus setting the bellow CUDA options
                In order to make sure pytorch randomly generate number will be the same every time
                when training on the same dataset
            '''
            if ds is not None:
                torch.manual_seed(len(ds))
            else:
                torch.manual_seed(2)

            if device_str == 'cuda':