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 categorical feature was found!')
          else:
              ohe = OneHotEncoder(categorical_features='all', sparse=False, minimum_fraction=self.minimum_fraction)
              return ohe.fit_transform(X_sel)
docs: Select categorical features and transform them using OneHotEncoder.
  
          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}