How to use the lightwood.config.config 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},
    'mixer':{'class': lightwood.BUILTIN_MIXERS.NnMixer, 'selfaware': SELFAWARE}}

import lightwood
import random
import pandas as pd
import numpy as np

lightwood.config.config.CONFIG.HELPER_MIXERS = False
random.seed(66)

n = 500
m = 800
train = True

data_train = {}
data_test = {}

for data, nr_ele in [(data_train,n), (data_test,m)]:
    for i in range(1,5):
        data[f'x_{i}'] = [random.random()*50 + 25  for _ in range(nr_ele)]

    data['y'] = [data['x_1'][i] * 0.9 + data['x_2'][i] * 0.09 + data['x_3'][i] * 0.009 + data['x_4'][i] * 0.0009 for i in range(nr_ele)]

data_train = pd.DataFrame(data_train)

import pandas
import random
import lightwood
from lightwood import Predictor
import os
from sklearn.metrics import r2_score
import numpy as np


lightwood.config.config.CONFIG.HELPER_MIXERS = False
random.seed(66)

### Generate a dataset
n = 100
m = n * 100
op = '*'

# generate random numbers between -10 and 10
data_train = {'x': [random.randint(-15, 5) for i in range(n)],
        'y': [random.randint(-15, 5) for i in range(n)]}

data_test = {'x': [random.randint(-15, 5) for i in range(m)],
        'y': [random.randint(-15, 5) for i in range(m)]}

if op == '/':
    for i in range(n):

def predict(self, mode='predict', ignore_columns=[]):
        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 mode == 'predict':
            # Doing it here since currently data cleanup is included in this, in the future separate data cleanup
            lightwood_config = self._create_lightwood_config()
            df = self.transaction.input_data.data_frame
        if mode == 'validate':
            df = self.transaction.input_data.validation_df
        elif mode == 'test':
            df = self.transaction.input_data.test_df

        if self.transaction.lmd['model_order_by'] is not None and len(self.transaction.lmd['model_order_by']) > 0:
            df = self._create_timeseries_df(df)

        if self.predictor is None:

def predict(self, mode='predict', ignore_columns=[]):
        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 mode == 'predict':
            # Doing it here since currently data cleanup is included in this, in the future separate data cleanup
            lightwood_config = self._create_lightwood_config()
            df = self.transaction.input_data.data_frame
        if mode == 'validate':
            df = self.transaction.input_data.validation_df
        elif mode == 'test':
            df = self.transaction.input_data.test_df

        if self.transaction.lmd['model_order_by'] is not None and len(self.transaction.lmd['model_order_by']) > 0:
            df = self._create_timeseries_df(df)

        if self.predictor is None:
            self.predictor = lightwood.Predictor(load_from_path=self.transaction.lmd['lightwood_data']['save_path'])