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

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"):
		from verticapy.learn.preprocessing import OneHotEncoder
		model = OneHotEncoder(name, cursor)
	model.input_relation = info.split(",")[1].replace("'", '').replace('\\', '')
	model.test_relation = test_relation if (test_relation) else model.input_relation
	if (model_type not in ("kmeans", "pca", "svd", "one_hot_encoder_fit")):
		model.X = info.split(",")[3:len(info.split(","))]
		model.X = [item.replace("'", '').replace('\\', '') for item in model.X]
		model.y = info.split(",")[2].replace("'", '').replace('\\', '')
	elif (model_type in ("pca")):
		model.X = info.split(",")[2:len(info.split(","))]
		model.X = [item.replace("'", '').replace('\\', '') for item in model.X]
		model.components = to_tablesample(query = "SELECT GET_MODEL_ATTRIBUTE(USING PARAMETERS model_name = '{}', attr_name = 'principal_components')".format(name.replace("'", "''")), cursor = cursor)
		model.components.table_info = False
		model.explained_variance = to_tablesample(query = "SELECT GET_MODEL_ATTRIBUTE(USING PARAMETERS model_name = '{}', attr_name = 'singular_values')".format(name.replace("'", "''")), cursor = cursor)
		model.explained_variance.table_info = False
		model.mean = to_tablesample(query = "SELECT GET_MODEL_ATTRIBUTE(USING PARAMETERS model_name = '{}', attr_name = 'columns')".format(name.replace("'", "''")), cursor = cursor)
		model.mean.table_info = False
	elif (model_type in ("svd")):