Scenario: Pym Tech, a U.S.-based technology manufacturer led by Hank Pym, is headquartered on Treasure Island in San Francisco, California. With business booming and expansion plans underway, the company is facing a number of IT challenges. To address these, Hank has contracted you to provide technical guidance to his IT team. The current focus is on using Azure Machine Learning to run an experiment that trains a classification model. Hank has asked the team to use HyperDrive to tune hyperparameters with the goal of optimizing the AUC (Area Under the Curve) metric. A developer configures a HyperDriveConfig object as follows: python hyperdrive = HyperDriveConfig ( estimator = pym_estimator , hyperparameter_sampling = hanks_params , policy = policy , primary_metric_name = 'AUC' , primary_metric_goal = PrimaryMetricGoal . MAXIMIZE , max_total_runs = 6 , max_concurrent_runs = 4 ) The experiment will run a script that trains a random forest model and evaluates it using validation data. The true labels for the validation data are stored in a variable called y_test , and the predicted probabilities are stored in a variable called y_predicted . To enable HyperDrive to track the AUC metric, the developer adds the following code to the training script: python from sklearn . metrics import roc_auc_score import logging # Code to train model omitted auc = roc_auc_score ( y_test , y_predicted ) logging . info ( "AUC: " + str ( auc )) Requirement: Ensure the script logs the AUC metric in a way that allows HyperDrive to capture and optimize it. Question: Does the developer’s code fulfill the requirement for logging the AUC metric to support HyperDrive optimization?