custom pipeline in pyspark - pyspark

Question

I would like to create a custom pipeline in pyspark to label encoder. It works well except I get an error when save the pipeline object:
from pyspark.sql import functions as f
from pyspark.sql.types import StringType
from pyspark.ml import Transformer
class CatTransform(Transformer):
"""
A custom Transformer which map all categorical features to numeric features
"""
def __init__(self, cat_feats: Iterable[str]):
super(Transformer, self).__init__()
self.cat_feats = cat_feats
self.dict_ = {}
def _transform(self, df: DataFrame) -> DataFrame:
for feat in self.cat_feats:
if not self.dict_.get(feat):
this_dict = df.groupby(feat).count().toPandas().set_index(feat).to_dict()["count"]
self.dict_[feat] = this_dict
this_dict = self.dict_[feat]
this_udf = f.udf(lambda x: this_dict[x] if x in this_dict.keys() else -1, IntegerType())
df = df.withColumn(feat, this_udf(feat))
return df
Then I got error:
ValueError: ('Pipeline write will fail on this pipeline because stage %s of type %s is not MLWritable', 'CatTransform_d08e899887b0', <class 'CatTransform'>)
Someone can help me, please!