PySpark: Can't pass more than 256 arguments to a UDF

This code:

https://github.com/apache/spark/blob/712874fa0937f0784f47740b127c3bab20da8569/python/pyspark/worker.py#L367-L379

Creates Python lambdas that call UDF functions passing arguments singly, rather than using varargs.  For example: `lambda a: f(a[0], a[1], ...)`.

This fails when there are more than 256 arguments.

mlflow, when generating model predictions, uses an argument for each feature column.  I have a model with > 500 features.

I was able to easily hack around this by changing the generated lambdas to use varargs, as in `lambda a: f(*a)`. 

IDK why these lambdas were created the way they were.  Using varargs is much simpler and works fine in my testing.