def METHOD_NAME(self, X, y, sample_weight=None):
    """
    Fit the SVM model according to the given training data.
    Parameters
    ----------
    X : {array-like, sparse matrix} of shape (n_samples, n_features) \
            or (n_samples, n_samples)
        Training vectors, where `n_samples` is the number of samples
        and `n_features` is the number of features.
        For kernel="precomputed", the expected shape of X is
        (n_samples, n_samples).
    y : array-like of shape (n_samples,)
        Target values (class labels in classification, real numbers in
        regression).
    sample_weight : array-like of shape (n_samples,), default=None
        Per-sample weights. Rescale C per sample. Higher weights
        force the classifier to put more emphasis on these points.
    Returns
    -------
    self : object
        Fitted estimator.
    Notes
    -----
    If X and y are not C-ordered and contiguous arrays of np.float64 and
    X is not a scipy.sparse.csr_matrix, X and/or y may be copied.
    If X is a dense array, then the other methods will not support sparse
    matrices as input.
    """
    if sklearn_check_version("1.2"):
        self._validate_params()
    if sklearn_check_version("1.0"):
        self._check_feature_names(X, reset=True)
    dispatch(
        self,
        "fit",
        {
            "onedal": self.__class__._onedal_fit,
            "sklearn": sklearn_SVR.METHOD_NAME,
        },
        X,
        y,
        sample_weight,
    )
    return self