def fill_from_augmented_normalized_batch(self, batch_aug_norm):
<EOL>batch = UnnormalizedBatch(<EOL>images=self.images_unaug,<EOL>heatmaps=self.heatmaps_unaug,<EOL>segmentation_maps=self.segmentation_maps_unaug,<EOL>keypoints=self.keypoints_unaug,<EOL>bounding_boxes=self.bounding_boxes_unaug,<EOL>polygons=self.polygons_unaug,<EOL>line_strings=self.line_strings_unaug,<EOL>data=batch_aug_norm.data<EOL>)<EOL>batch.images_aug = nlib.invert_normalize_images(<EOL>batch_aug_norm.images_aug, self.images_unaug)<EOL>batch.heatmaps_aug = nlib.invert_normalize_heatmaps(<EOL>batch_aug_norm.heatmaps_aug, self.heatmaps_unaug)<EOL>batch.segmentation_maps_aug = nlib.invert_normalize_segmentation_maps(<EOL>batch_aug_norm.segmentation_maps_aug, self.segmentation_maps_unaug)<EOL>batch.keypoints_aug = nlib.invert_normalize_keypoints(<EOL>batch_aug_norm.keypoints_aug, self.keypoints_unaug)<EOL>batch.bounding_boxes_aug = nlib.invert_normalize_bounding_boxes(<EOL>batch_aug_norm.bounding_boxes_aug, self.bounding_boxes_unaug)<EOL>batch.polygons_aug = nlib.invert_normalize_polygons(<EOL>batch_aug_norm.polygons_aug, self.polygons_unaug)<EOL>batch.line_strings_aug = nlib.invert_normalize_line_strings(<EOL>batch_aug_norm.line_strings_aug, self.line_strings_unaug)<EOL>return batch<EOL>
Fill this batch with (normalized) augmentation results.

This method receives a (normalized) Batch instance, takes all
``*_aug`` attributes out if it and assigns them to this
batch *in unnormalized form*. Hence, the datatypes of all ``*_aug``
attributes will match the datatypes of the ``*_unaug`` attributes.

Parameters
----------
batch_aug_norm: imgaug.augmentables.batches.Batch
    Batch after normalization and augmentation.

Returns
-------
imgaug.augmentables.batches.UnnormalizedBatch
    New UnnormalizedBatch instance. All ``*_unaug`` attributes are
    taken from the old UnnormalizedBatch (without deepcopying them)
    and all ``*_aug`` attributes are taken from `batch_normalized`
    converted to unnormalized form.