code: def score(self, X, y=None):
          
          if self.normalize:
              X = normalize(X)
  
          check_is_fitted(self, "cluster_centers_")
          X = self._check_test_data(X)
          return -_labels_inertia(X, self.cluster_centers_)[1]
docs: 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.