def METHOD_NAME(self, X) -> np.ndarray:
    """Predict class probabilities for n instances in X.
    Parameters
    ----------
    X : 3D np.ndarray
        The data to make predictions for, shape = (n_instances, n_channels,
        n_timepoints).
    Returns
    -------
    y : 2D np.ndarray
        Predicted probabilities using the ordering in classes_ shape = (
        n_instances, n_classes_).
    """
    m = getattr(self._estimator, "predict_proba", None)
    if callable(m):
        return self._estimator.predict_proba(self._transformer.transform(X))
    else:
        dists = np.zeros((X.shape[0], self.n_classes_))
        preds = self._estimator.predict(self._transformer.transform(X))
        for i in range(0, X.shape[0]):
            dists[i, self._class_dictionary[preds[i]]] = 1
        return dists