def PDTP(  # pylint: disable=invalid-name
    target_estimator: "CLASSIFIER_TYPE",
    extra_estimator: "CLASSIFIER_TYPE",
    x: np.ndarray,
    y: np.ndarray,
    indexes: np.ndarray | None = None,
    num_iter: int = 10,
    comparison_type: ComparisonType | None = ComparisonType.RATIO,
) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
    """
    Compute the pointwise differential training privacy metric for the given classifier and training set.

    | Paper link: https://arxiv.org/abs/1712.09136

    :param target_estimator: The classifier to be analyzed.
    :param extra_estimator: Another classifier of the same type as the target classifier, but not yet fit.
    :param x: The training data of the classifier.
    :param y: Target values (class labels) of `x`, one-hot-encoded of shape (nb_samples, nb_classes) or indices of
              shape (nb_samples,).
    :param indexes: the subset of indexes of `x` to compute the PDTP metric on. If not supplied, PDTP will be
                    computed for all samples in `x`.
    :param num_iter: the number of iterations of PDTP computation to run for each sample. If not supplied,
                     defaults to 10. The result is the average across iterations.
    :param comparison_type: the way in which to compare the model outputs between models trained with and without
                            a certain sample. Default is to compute the ratio.
    :return: A tuple of three arrays, containing the average (worse, standard deviation) PDTP value for each sample in
             the training set respectively. The higher the value, the higher the privacy leakage for that sample.
    """
    from art.estimators.classification.pytorch import PyTorchClassifier
    from art.estimators.classification.tensorflow import TensorFlowV2Classifier
    from art.estimators.classification.scikitlearn import ScikitlearnClassifier

    supported_classifiers = (PyTorchClassifier, TensorFlowV2Classifier, ScikitlearnClassifier)

    if not isinstance(target_estimator, supported_classifiers) or not isinstance(
        extra_estimator, supported_classifiers
    ):
        raise ValueError("PDTP metric only supports classifiers of type PyTorch, TensorFlowV2 and ScikitLearn.")
    if target_estimator.input_shape[0] != x.shape[1]:
        raise ValueError("Shape of x does not match input_shape of classifier")
    y = check_and_transform_label_format(y, nb_classes=target_estimator.nb_classes)
    if y.shape[0] != x.shape[0]:
        raise ValueError("Number of rows in x and y do not match")

    results = []

    for _ in range(num_iter):
        iter_results = []
        # get probabilities from original model
        pred = target_estimator.predict(x)
        if not is_probability_array(pred):
            try:
                pred = scipy.special.softmax(pred, axis=1)
            except Exception as exc:  # pragma: no cover
                raise ValueError("PDTP metric only supports classifiers that output logits or probabilities.") from exc
        # divide into 100 bins and return center of bin
        bins = np.array(np.arange(0.0, 1.01, 0.01).round(decimals=2))
        pred_bin_indexes = np.digitize(pred, bins)
        pred_bin_indexes[pred_bin_indexes == 101] = 100
        pred_bin = bins[pred_bin_indexes] - 0.005

        if indexes is None:
            indexes = np.array(range(x.shape[0]))
        if indexes is not None:
            for row in indexes:
                # create new model without sample in training data
                alt_x = np.delete(x, row, 0)
                alt_y = np.delete(y, row, 0)
                try:
                    extra_estimator.reset()
                except NotImplementedError as exc:  # pragma: no cover
                    raise ValueError(
                        "PDTP metric can only be applied to classifiers that implement the reset method."
                    ) from exc
                extra_estimator.fit(alt_x, alt_y)
                # get probabilities from new model
                alt_pred = extra_estimator.predict(x)
                if not is_probability_array(alt_pred):
                    alt_pred = scipy.special.softmax(alt_pred, axis=1)
                # divide into 100 bins and return center of bin
                alt_pred_bin_indexes = np.digitize(alt_pred, bins)
                alt_pred_bin_indexes[alt_pred_bin_indexes == 101] = 100
                alt_pred_bin = bins[alt_pred_bin_indexes] - 0.005
                if comparison_type == ComparisonType.RATIO:
                    ratio_1 = pred_bin / alt_pred_bin
                    ratio_2 = alt_pred_bin / pred_bin
                    # get max value
                    max_value: float = max(ratio_1.max(), ratio_2.max())
                elif comparison_type == ComparisonType.DIFFERENCE:
                    max_value = float(np.max(abs(pred_bin - alt_pred_bin)))
                else:
                    raise ValueError("Unsupported comparison type.")
                iter_results.append(max_value)
            results.append(iter_results)

    # get average of iterations for each sample
    # We now have a list of lists, internal lists represent an iteration. We need to transpose and get averages.
    per_sample: list[list[float]] = list(map(list, zip(*results)))
    avg_per_sample = np.array([sum(val) / len(val) for val in per_sample])
    worse_per_sample = np.max(per_sample, axis=1)
    std_dev_per_sample = np.std(per_sample, axis=1)

    # return avg+worse leakage + standard deviation per sample
    return avg_per_sample, worse_per_sample, std_dev_per_sample

    Compute the pointwise differential training privacy metric for the given classifier and training set.

    | Paper link: https://arxiv.org/abs/1712.09136

    :param target_estimator: The classifier to be analyzed.
    :param extra_estimator: Another classifier of the same type as the target classifier, but not yet fit.
    :param x: The training data of the classifier.
    :param y: Target values (class labels) of `x`, one-hot-encoded of shape (nb_samples, nb_classes) or indices of
              shape (nb_samples,).
    :param indexes: the subset of indexes of `x` to compute the PDTP metric on. If not supplied, PDTP will be
                    computed for all samples in `x`.
    :param num_iter: the number of iterations of PDTP computation to run for each sample. If not supplied,
                     defaults to 10. The result is the average across iterations.
    :param comparison_type: the way in which to compare the model outputs between models trained with and without
                            a certain sample. Default is to compute the ratio.
    :return: A tuple of three arrays, containing the average (worse, standard deviation) PDTP value for each sample in
             the training set respectively. The higher the value, the higher the privacy leakage for that sample.
    
art/metrics/privacy/membership_leakage.py