Multiple Aggregation Using 'countDistinct' and 'first' result in error

from pyspark.sql import SparkSession
import pyspark.sql.functions as sfn

sparkSession = SparkSession.builder.master('local').getOrCreate()

df = spark.createDataFrame([
        (1, 'a', 'z'),
        (1, 'b', 'x'),
        (1, 'a', 'y'),
        (1, 'a', 'x'),
        (2, 'b', 'z'),
        (2, 'b', 'z')
    ], ['id', 'var1', 'var2'])

## Using two 'first' and one 'countDistinct' aggregations works
df.groupby('id')    \
        .agg(sfn.first('var1'),  \
                sfn.first('var2'),  \
                sfn.countDistinct('var1')).show()
                         
## Using one 'max' with both 'countDistinct' works:
df.groupby('id')    \
         .agg(sfn.max('var2'),                \
                 sfn.countDistinct('var1'),   \
                 sfn.countDistinct('var2')).show()

## But using both 'countDistinct' with at least one 'first' crashes
df.groupby('id')    \
        .agg(sfn.first('var1'),   \
                sfn.first('var2'),   \
                sfn.countDistinct('var1'), \
                sfn.countDistinct('var2')) \
        .show()