//  [python]
def _fit(self, X: np.ndarray) -> np.ndarray:
        pred = self._cluster_and_decide(X)
        self.children: Tuple["DivisiveCluster"] = cast(
            Tuple["DivisiveCluster"], tuple()
        )

        uni_labels = np.unique(pred)
        labels = pred.reshape((-1, 1)).copy()
        if len(uni_labels) > 1:
            for ul in uni_labels:
                inds = pred == ul
                new_X = X[inds]
                dc = DivisiveCluster(
                    cluster_method=self.cluster_method,
                    max_components=self.max_components,
                    min_split=self.min_split,
                    max_level=self.max_level,
                    cluster_kws=self.cluster_kws,
                    delta_criter=self.delta_criter,
                )
                dc.parent = self
                if (
                    len(new_X) > self.max_components
                    and len(new_X) >= self.min_split
                    and self.depth + 1 < self.max_level
                ):
                    child_labels = dc._fit(new_X)
                    while labels.shape[1] <= child_labels.shape[1]:
                        labels = np.column_stack(
                            (labels, np.zeros((len(X), 1), dtype=int))
                        )
                    labels[inds, 1 : child_labels.shape[1] + 1] = child_labels
                else:
                    # make a "GaussianMixture" model for clusters
                    # that were not fitted
                    if self.cluster_method == "gmm":
                        cluster_idx = len(dc.parent.children) - 1
                        parent_model = dc.parent.model_
                        model = GaussianMixture()
                        model.weights_ = np.array([1])
                        model.means_ = parent_model.means_[cluster_idx].reshape(1, -1)
                        model.covariance_type = parent_model.covariance_type
                        if model.covariance_type == "tied":
                            model.covariances_ = parent_model.covariances_
                            model.precisions_ = parent_model.precisions_
                            model.precisions_cholesky_ = (
                                parent_model.precisions_cholesky_
                            )
                        else:
                            cov_types = ["spherical", "diag", "full"]
                            n_features = model.means_.shape[-1]
                            cov_shapes = [
                                (1,),
                                (1, n_features),
                                (1, n_features, n_features),
                            ]
                            cov_shape_idx = cov_types.index(model.covariance_type)
                            model.covariances_ = parent_model.covariances_[
                                cluster_idx
                            ].reshape(cov_shapes[cov_shape_idx])
                            model.precisions_ = parent_model.precisions_[
                                cluster_idx
                            ].reshape(cov_shapes[cov_shape_idx])
                            model.precisions_cholesky_ = (
                                parent_model.precisions_cholesky_[cluster_idx].reshape(
                                    cov_shapes[cov_shape_idx]
                                )
                            )

                        dc.model_ = model

        return labels