How to use the kmodes.util.pandas_to_numpy function in kmodes

def fit(self, X, y=None, **kwargs):
        """Compute k-modes clustering.

        Parameters
        ----------
        X : array-like, shape=[n_samples, n_features]
        """
        X = pandas_to_numpy(X)

        random_state = check_random_state(self.random_state)
        self._enc_cluster_centroids, self._enc_map, self.labels_, self.cost_, \
        self.n_iter_, self.epoch_costs_ = k_modes(
            X,
            self.n_clusters,
            self.max_iter,
            self.cat_dissim,
            self.init,
            self.n_init,
            self.verbose,
            random_state,
            self.n_jobs,
        )
        return self

def fit(self, X, y=None, categorical=None):
        """Compute k-prototypes clustering.

        Parameters
        ----------
        X : array-like, shape=[n_samples, n_features]
        categorical : Index of columns that contain categorical data
        """
        if categorical is not None:
            assert isinstance(categorical, (int, list, tuple)), "The 'categorical' \
                argument needs to be an integer with the index of the categorical \
                column in your data, or a list or tuple of several of them, \
                but it is a {}.".format(type(categorical))

        X = pandas_to_numpy(X)

        random_state = check_random_state(self.random_state)
        # If self.gamma is None, gamma will be automatically determined from
        # the data. The function below returns its value.
        self._enc_cluster_centroids, self._enc_map, self.labels_, self.cost_, \
        self.n_iter_, self.epoch_costs_, self.gamma = k_prototypes(
            X,
            categorical,
            self.n_clusters,
            self.max_iter,
            self.num_dissim,
            self.cat_dissim,
            self.gamma,
            self.init,
            self.n_init,
            self.verbose,

categorical : Indices of columns that contain categorical data

        Returns
        -------
        labels : array, shape [n_samples,]
            Index of the cluster each sample belongs to.
        """
        assert hasattr(self, '_enc_cluster_centroids'), "Model not yet fitted."

        if categorical is not None:
            assert isinstance(categorical, (int, list, tuple)), "The 'categorical' \
                argument needs to be an integer with the index of the categorical \
                column in your data, or a list or tuple of several of them, \
                but it is a {}.".format(type(categorical))

        X = pandas_to_numpy(X)
        Xnum, Xcat = _split_num_cat(X, categorical)
        Xnum, Xcat = check_array(Xnum), check_array(Xcat, dtype=None)
        Xcat, _ = encode_features(Xcat, enc_map=self._enc_map)
        return _labels_cost(Xnum, Xcat, self._enc_cluster_centroids,
                            self.num_dissim, self.cat_dissim, self.gamma)[0]

New data to predict.

        Returns
        -------
        labels : array, shape [n_samples,]
            Index of the cluster each sample belongs to.
        """

        assert hasattr(self, '_enc_cluster_centroids'), "Model not yet fitted."

        if self.verbose and self.cat_dissim == ng_dissim:
            print("Ng's dissimilarity measure was used to train this model, "
                  "but now that it is predicting the model will fall back to "
                  "using simple matching dissimilarity.")

        X = pandas_to_numpy(X)
        X = check_array(X, dtype=None)
        X, _ = encode_features(X, enc_map=self._enc_map)
        return _labels_cost(X, self._enc_cluster_centroids, self.cat_dissim)[0]