Major Description
Capable of registering vectorized UDFs and then use it in SQL statement.
For example,
>>> import random
>>> from pyspark.sql.types import IntegerType
>>> from pyspark.sql.functions import pandas_udf
>>> random_pandas_udf = pandas_udf(
... lambda x: random.randint(0, 100) + x, IntegerType())
... .asNondeterministic() # doctest: +SKIP
>>> _ = spark.catalog.registerFunction(
... "random_pandas_udf", random_pandas_udf, IntegerType()) # doctest: +SKIP
>>> spark.sql("SELECT random_pandas_udf(2)").collect() # doctest: +SKIP
[Row(random_pandas_udf(2)=84)]
