def score(self, X, y=None):
if self.normalize:<EOL><INDENT>X = normalize(X)<EOL><DEDENT>check_is_fitted(self, "<STR_LIT>")<EOL>X = self._check_test_data(X)<EOL>return -_labels_inertia(X, self.cluster_centers_)[<NUM_LIT:1>]<EOL>
Inertia score (sum of all distances to closest cluster).

        Parameters
        ----------
        X : {array-like, sparse matrix}, shape = [n_samples, n_features]
            New data.

        Returns
        -------
        score : float
            Larger score is better.