human
Here's a good function header given the following:
I wrote the below code

    def write_ranking(filename, header, names, scores):
        "Helper    method    which    saves    submissions'    scores    and    names."
        order = np.argsort(scores)[::(-1)]
        with open(filename, 'w') as f:
            writer = csv.writer(f)
            writer.writerow(header)
            for idx in order:
                writer.writerow([names[idx], scores[idx]])
    def write_score_matrix(filename, scores, row_names, column_names):
        'Helper    method    which    saves    score    matrix.'
        result = np.pad(scores, ((1, 0), (1, 0)), 'constant').astype(np.object)
        result[(0, 0)] = ''
        result[1:, 0] = row_names
        result[0, 1:] = column_names
        np.savetxt(filename, result, fmt='%s', delimiter=',')
    attack_names = list(attacks_output.attack_names)
    attack_names_idx = {name: index for (index, name) in enumerate(attack_names)}
    targeted_attack_names = list(attacks_output.targeted_attack_names)
    targeted_attack_names_idx = {name: index for (index, name) in enumerate(targeted_attack_names)}
    defense_names = list(defenses_output.keys())
    defense_names_idx = {name: index for (index, name) in enumerate(defense_names)}
    accuracy_on_attacks = np.zeros((len(attack_names), len(defense_names)), dtype=np.int32)
    accuracy_on_targeted_attacks = np.zeros((len(targeted_attack_names), len(defense_names)), dtype=np.int32)
    hit_target_class = np.zeros((len(targeted_attack_names), len(defense_names)), dtype=np.int32)
    for (defense_name, defense_result) in defenses_output.items():
        for (image_filename, predicted_label) in defense_result.items():
            (attack_name, is_targeted, image_id) = attacks_output.image_by_base_filename(image_filename)
            true_label = dataset_meta.get_true_label(image_id)
            defense_idx = defense_names_idx[defense_name]
            if is_targeted:
                target_class = dataset_meta.get_target_class(image_id)
                if (true_label == predicted_label):
                   ...

What's a good function header?
assistant
#Here's a good function header.

def compute_and_save_scores_and_ranking(attacks_output, defenses_output, dataset_meta, output_dir, save_all_classification=False):