How to use the verticapy.learn.naive_bayes.MultinomialNB function in verticapy

parameters_dict[elem] = parameters_dict[elem].replace("'", "")
	if (model_type == "rf_regressor"):
		from verticapy.learn.ensemble import RandomForestRegressor
		model = RandomForestRegressor(name, cursor, int(parameters_dict['ntree']), int(parameters_dict['mtry']), int(parameters_dict['max_breadth']), float(parameters_dict['sampling_size']), int(parameters_dict['max_depth']), int(parameters_dict['min_leaf_size']), float(parameters_dict['min_info_gain']), int(parameters_dict['nbins']))
	elif (model_type == "rf_classifier"):
		from verticapy.learn.ensemble import RandomForestClassifier
		model = RandomForestClassifier(name, cursor, int(parameters_dict['ntree']), int(parameters_dict['mtry']), int(parameters_dict['max_breadth']), float(parameters_dict['sampling_size']), int(parameters_dict['max_depth']), int(parameters_dict['min_leaf_size']), float(parameters_dict['min_info_gain']), int(parameters_dict['nbins']))
	elif (model_type == "logistic_reg"):
		from verticapy.learn.linear_model import LogisticRegression
		model = LogisticRegression(name, cursor, parameters_dict['regularization'], float(parameters_dict['epsilon']), float(parameters_dict['lambda']), int(parameters_dict['max_iterations']), parameters_dict['optimizer'], float(parameters_dict['alpha']))
	elif (model_type == "linear_reg"):
		from verticapy.learn.linear_model import ElasticNet
		model = ElasticNet(name, cursor, parameters_dict['regularization'], float(parameters_dict['epsilon']), float(parameters_dict['lambda']), int(parameters_dict['max_iterations']), parameters_dict['optimizer'], float(parameters_dict['alpha']))
	elif (model_type == "naive_bayes"):
		from verticapy.learn.naive_bayes import MultinomialNB
		model = MultinomialNB(name, cursor, float(parameters_dict['alpha']))
	elif (model_type == "svm_regressor"):
		from verticapy.learn.svm import LinearSVR
		model = LinearSVR(name, cursor, float(parameters_dict['epsilon']), float(parameters_dict['C']), True, float(parameters_dict['intercept_scaling']), parameters_dict['intercept_mode'], float(parameters_dict['error_tolerance']), int(parameters_dict['max_iterations']))
	elif (model_type == "svm_classifier"):
		from verticapy.learn.svm import LinearSVC
		model = LinearSVC(name, cursor, float(parameters_dict['epsilon']), float(parameters_dict['C']), True, float(parameters_dict['intercept_scaling']), parameters_dict['intercept_mode'], [float(item) for item in parameters_dict['class_weights'].split(",")], int(parameters_dict['max_iterations']))
	elif (model_type == "kmeans"):
		from verticapy.learn.cluster import KMeans
		model = KMeans(name, cursor, -1, parameters_dict['init_method'], int(parameters_dict['max_iterations']), float(parameters_dict['epsilon']))
	elif (model_type == "pca"):
		from verticapy.learn.decomposition import PCA
		model = PCA(name, cursor, 0, bool(parameters_dict['scale']))
	elif (model_type == "svd"):
		from verticapy.learn.decomposition import SVD
		model = SVD(name, cursor)
	elif (model_type == "one_hot_encoder_fit"):