code: def transform(self, X):
          
          selected = auto_select_categorical_features(X, threshold=self.threshold)
          _, X_sel, n_selected, _ = _X_selected(X, selected)
  
          if n_selected == 0:
              # No features selected.
              raise ValueError('No continuous feature was found!')
          else:
              pca = PCA(svd_solver=self.svd_solver, iterated_power=self.iterated_power, random_state=self.random_state)
              return pca.fit_transform(X_sel)
docs: Select continuous features and transform them using PCA.
  
          Parameters
          ----------
          X: numpy ndarray, {n_samples, n_components}
              New data, where n_samples is the number of samples and n_components is the number of components.
  
          Returns
          -------
          array-like, {n_samples, n_components}