How to use the tslearn.metrics.cdist_gak function in tslearn

def _get_kernel(self, X, Y=None):
        return cdist_gak(X, Y, sigma=self.sigma, n_jobs=self.n_jobs)

X = to_time_series_dataset(X)

        if fit_time:
            self._X_fit = X
            self.gamma_ = gamma_soft_dtw(X)
            self.classes_ = numpy.unique(y)

        if self.kernel in VARIABLE_LENGTH_METRICS:
            assert self.kernel == "gak"
            self.estimator_kernel_ = "precomputed"
            if fit_time:
                sklearn_X = cdist_gak(X,
                                      sigma=numpy.sqrt(self.gamma_ / 2.),
                                      n_jobs=self.n_jobs)
            else:
                sklearn_X = cdist_gak(X,
                                      self._X_fit,
                                      sigma=numpy.sqrt(self.gamma_ / 2.),
                                      n_jobs=self.n_jobs)
        else:
            self.estimator_kernel_ = self.kernel
            sklearn_X = _prepare_ts_datasets_sklearn(X)

        if y is None:
            return sklearn_X
        else:
            return sklearn_X, y

else:
            X, y = check_X_y(X, y, allow_nd=True,
                             force_all_finite=force_all_finite)
        X = check_dims(X, X_fit=None)
        X = to_time_series_dataset(X)

        if fit_time:
            self._X_fit = X
            self.gamma_ = gamma_soft_dtw(X)
            self.classes_ = numpy.unique(y)

        if self.kernel in VARIABLE_LENGTH_METRICS:
            assert self.kernel == "gak"
            self.estimator_kernel_ = "precomputed"
            if fit_time:
                sklearn_X = cdist_gak(X,
                                      sigma=numpy.sqrt(self.gamma_ / 2.),
                                      n_jobs=self.n_jobs)
            else:
                sklearn_X = cdist_gak(X,
                                      self._X_fit,
                                      sigma=numpy.sqrt(self.gamma_ / 2.),
                                      n_jobs=self.n_jobs)
        else:
            self.estimator_kernel_ = self.kernel
            sklearn_X = _prepare_ts_datasets_sklearn(X)

        if y is None:
            return sklearn_X
        else:
            return sklearn_X, y