def METHOD_NAME(self, X):
    """
    Perform regression on samples in X.
    For an one-class model, +1 (inlier) or -1 (outlier) is returned.
    Parameters
    ----------
    X : {array-like, sparse matrix} of shape (n_samples, n_features)
        For kernel="precomputed", the expected shape of X is
        (n_samples_test, n_samples_train).
    Returns
    -------
    y_pred : ndarray of shape (n_samples,)
        The predicted values.
    """
    if sklearn_check_version("1.0"):
        self._check_feature_names(X, reset=False)
    return dispatch(
        self,
        "predict",
        {
            "onedal": self.__class__._onedal_predict,
            "sklearn": sklearn_NuSVR.METHOD_NAME,
        },
        X,
    )