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