如何从csv中读取Spark SQL UserDefinedType

from pyspark.sql import SparkSession
from pyspark.sql.types import StructType, StructField, StringType, IntegerType
from pyspark.sql.functions import udf

spark = SparkSession.builder.appName("CSV to Spark SQL UserDefinedType").getOrCreate()

class MyUserDefinedType:
    def __init__(self, field1, field2):
        self.field1 = field1
        self.field2 = field2

    def __repr__(self):
        return f"MyUserDefinedType(field1={self.field1}, field2={self.field2})"

def parse_udt(value):
    field1, field2 = value.split(",")
    return MyUserDefinedType(field1, field2)

udf_parse_udt = udf(parse_udt, StringType())
spark.udf.register("parse_udt", udf_parse_udt)

schema = StructType([
    StructField("udt_column", StringType(), True)
])

df = spark.read.csv("path/to/csv/file.csv", schema=schema, header=True)

df = df.withColumn("udt_column", udf_parse_udt(df["udt_column"]))