def METHOD_NAME(self, X) -> np.ndarray:
    """Predicts labels probabilities for sequences in X.
    Default behaviour is to call _predict and set the predicted class probability
    to 1, other class probabilities to 0. Override if better estimates are
    obtainable.
    Parameters
    ----------
    X : guaranteed to be of a type in self.get_tag("X_inner_mtype")
        if self.get_tag("X_inner_mtype") = "numpy3D":
            3D np.ndarray of shape = [n_instances, n_channels, series_length]
        for list of other mtypes, see datatypes.SCITYPE_REGISTER
    Returns
    -------
    y : 2D array of shape [n_instances, n_classes] - predicted class probabilities
        1st dimension indices correspond to instance indices in X
        2nd dimension indices correspond to possible labels (integers)
        (i, j)-th entry is predictive probability that i-th instance is of class j
    """
    preds = self._predict(X)
    n_instances = len(preds)
    n_clusters = self.n_clusters
    if n_clusters is None:
        n_clusters = max(preds) + 1
    dists = np.zeros((X.shape[0], n_clusters))
    for i in range(n_instances):
        dists[i, preds[i]] = 1
    return dists