The data science team has created and logged a production model using MLflow. The model accepts a list of column names and returns a new column of type DOUBLE. The following code correctly imports the production model, loads the customers table containing the customer_id key column into a DataFrame, and defines the feature columns needed for the model: model = mlflow . pyfunc . spark_udf ( spark , model_uri = "models:/churn/prod" ) df = spark . table ( "customers" ) columns = [ "account_age" , "time_since_last_seen" , "app_rating" ] Which code block will output a DataFrame with the schema "customer_id LONG, predictions DOUBLE"?