How to use the verticapy.learn.preprocessing.Normalizer function in verticapy

("test_relation", test_relation, [str], False)])
	if not(cursor):
		cursor = read_auto_connect().cursor()
	else:
		check_cursor(cursor)
	try:
		cursor.execute("SELECT GET_MODEL_ATTRIBUTE (USING PARAMETERS model_name = '" + name + "', attr_name = 'call_string')")
		info = cursor.fetchone()[0].replace('\n', ' ')
	except:
		try:
			cursor.execute("SELECT GET_MODEL_SUMMARY (USING PARAMETERS model_name = '" + name + "')")
			info = cursor.fetchone()[0].replace('\n', ' ')
			info = "kmeans(" + info.split("kmeans(")[1]
		except:
			from verticapy.learn.preprocessing import Normalizer
			model = Normalizer(name, cursor)
			model.param = to_tablesample(query = "SELECT GET_MODEL_ATTRIBUTE(USING PARAMETERS model_name = '{}', attr_name = 'details')".format(name.replace("'", "''")), cursor = cursor)
			model.param.table_info = False
			model.X = ['"' + item + '"' for item in model.param.values["column_name"]]
			if ("avg" in model.param.values):
				model.method = "zscore" 
			elif ("max" in model.param.values):
				model.method = "minmax" 
			else:
				model.method = "robust_zscore"
			return model
	try:
		info = info.split("SELECT ")[1].split("(")
	except:
		info = info.split("(")
	model_type = info[0].lower()
	info = info[1].split(")")[0].replace(" ", '').split("USINGPARAMETERS")