How to use the clinica.pipelines.machine_learning.voxel_based_io.revert_mask function in clinica

def save_weights_as_nifti(self, weights, output_dir):

        if self._images is None:
            self.get_images()

        output_filename = path.join(output_dir, 'weights.nii.gz')
        data = vbio.revert_mask(weights, self._data_mask, self._orig_shape)
        vbio.weights_to_nifti(data, self._images[0], output_filename)

def save_weights_as_nifti(self, weights, output_dir):

        if self._images is None:
            self.get_images()

        output_filename = path.join(output_dir, 'weights.nii.gz')
        data = vbio.revert_mask(weights, self._data_mask, self._orig_shape)
        vbio.weights_to_nifti(data, self._images[0], output_filename)

def save_weights_as_nifti(self, weights, output_dir):

        if self._images is None:
            self.get_images()

        output_filename = path.join(output_dir, 'weights.nii.gz')
        data = vbio.revert_mask(weights, self._data_mask, self._orig_shape)

        features = data / abs(data).max()

        img = nib.load(self._images[0])

        output_image = nib.Nifti1Image(features, img.affine)

        nib.save(output_image, output_filename)

evaluation = evaluate_prediction(y, y_hat)

            print('\nTrue positive %0.2f' % len(evaluation['predictions'][0]))
            print('True negative %0.2f' % len(evaluation['predictions'][1]))
            print('False positive %0.2f' % len(evaluation['predictions'][2]))
            print('False negative %0.2f' % len(evaluation['predictions'][3]))

            print('Accuracy %0.2f' % evaluation['accuracy'])
            print('Balanced accuracy %0.2f' % evaluation['balanced_accuracy'])
            print('Sensitivity %0.2f' % evaluation['sensitivity'])
            print('Specificity %0.2f' % evaluation['specificity'])
            print('Positive predictive value %0.2f' % evaluation['ppv'])
            print('Negative predictive value %0.2f \n' % evaluation['npv'])

            if save_weights or save_features_image:
                weights_orig = revert_mask(coefficients, data_mask, orig_shape)

            if save_weights:
                np.save(join(output_directory, classification_str + '__intersect'), intersect)
                np.save(join(output_directory, classification_str + '__weights'), weights_orig)

            if save_features_image:
                weights_to_nifti(weights_orig, image_list[0], join(output_directory, classification_str + '__features_image.nii'))

            if save_subject_classification:
                save_subjects_prediction(current_subjects, current_diagnosis, y, y_hat, join(output_directory, classification_str + '__subjects.csv'))

            results[(dx1, dx2)] = evaluate_prediction(y, y_hat)

    results_to_csv(results, dx_filter, join(output_directory, 'resume' + ('_positive' if positive else '') + '.csv'))