code: def predict(self, X, **kwargs):
          
  
          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]
docs: Predict the closest cluster each sample in X belongs to.
  
          Parameters
          ----------
          X : array-like, shape = [n_samples, n_features]
              New data to predict.
  
          Returns
          -------
          labels : array, shape [n_samples,]
              Index of the cluster each sample belongs to.