def METHOD_NAME(attack, dataset, attack_train_ratio):
    (x_train, y_train), _ = dataset
    attack_train_size = int(len(x_train) * attack_train_ratio)
    kwargs = {
        "norm": 2,
        "max_iter": 2,
        "max_eval": 4,
        "init_eval": 1,
        "init_size": 1,
        "verbose": False,
    }
    # infer attacked feature on remainder of data
    inferred_train_prob = attack.infer(
        x_train[attack_train_size:], y_train[attack_train_size:], probabilities=True, **kwargs
    )
    # check accuracy
    backend_check_probabilities(inferred_train_prob)