def to_normalized_batch(self):
assert all([<EOL>attr is None for attr_name, attr in self.__dict__.items()<EOL>if attr_name.endswith("<STR_LIT>")]),"<STR_LIT>""<STR_LIT>""<STR_LIT>"<EOL>images_unaug = nlib.normalize_images(self.images_unaug)<EOL>shapes = None<EOL>if images_unaug is not None:<EOL><INDENT>shapes = [image.shape for image in images_unaug]<EOL><DEDENT>return Batch(<EOL>images=images_unaug,<EOL>heatmaps=nlib.normalize_heatmaps(<EOL>self.heatmaps_unaug, shapes),<EOL>segmentation_maps=nlib.normalize_segmentation_maps(<EOL>self.segmentation_maps_unaug, shapes),<EOL>keypoints=nlib.normalize_keypoints(<EOL>self.keypoints_unaug, shapes),<EOL>bounding_boxes=nlib.normalize_bounding_boxes(<EOL>self.bounding_boxes_unaug, shapes),<EOL>polygons=nlib.normalize_polygons(<EOL>self.polygons_unaug, shapes),<EOL>line_strings=nlib.normalize_line_strings(<EOL>self.line_strings_unaug, shapes),<EOL>data=self.data<EOL>)<EOL>
Convert this unnormalized batch to an instance of Batch.

        As this method is intended to be called before augmentation, it
        assumes that none of the ``*_aug`` attributes is yet set.
        It will produce an AssertionError otherwise.

        The newly created Batch's ``*_unaug`` attributes will match the ones
        in this batch, just in normalized form.

        Returns
        -------
        imgaug.augmentables.batches.Batch
            The batch, with ``*_unaug`` attributes being normalized.