          :class:`~plugins.extract.pipeline.ExtractMedia`
              The :attr:`DetectedFaces` list will be populated for this class with the bounding
              boxes, landmarks and masks for the detected faces found in the frame.
          """
          for mask, face, feed_face in zip(batch["prediction"],
                                           batch["detected_faces"],
                                           batch["feed_faces"]):
              face.add_mask(self._storage_name,
                            mask,
                            feed_face.adjusted_matrix,
                            feed_face.interpolators[1],
                            storage_size=self._storage_size,
 
</s> remove             ``detected_faces``, ``filename``, ``feed_faces``
 </s> add             ``detected_faces``, ``filename``, ``feed_faces``, ``roi_masks`` </s> remove                                         image=item.get_image_copy(self.color_format),
 </s> add                                         image=image, </s> remove         return self._image.shape[:2]
 </s> add         return self._image_shape[:2] </s> remove 
 </s> add             if "source_frame_dims" not in metadata["source"]:
                logger.error("The faces need to be re-extracted as at least some of them do not "
                             "contain information required to correctly generate masks.")
                logger.error("You can re-extract the face-set by using the Alignments Tool's "
                             "Extract job.")
                break
        for mask, face, feed_face, roi_mask in zip(batch["prediction"],
                                                   batch["detected_faces"],
                                                   batch["feed_faces"],
                                                   batch["roi_masks"]):
            self._crop_out_of_bounds(mask, roi_mask)
          :class:`~plugins.extract.pipeline.ExtractMedia`
              The :attr:`DetectedFaces` list will be populated for this class with the bounding
              boxes, landmarks and masks for the detected faces found in the frame.
          """
        for mask, face, feed_face, roi_mask in zip(batch["prediction"],
                                                   batch["detected_faces"],
                                                   batch["feed_faces"],
                                                   batch["roi_masks"]):
            self._crop_out_of_bounds(mask, roi_mask)
        for mask, face, feed_face, roi_mask in zip(batch["prediction"],
                                                   batch["detected_faces"],
                                                   batch["feed_faces"],
                                                   batch["roi_masks"]):
            self._crop_out_of_bounds(mask, roi_mask)
        for mask, face, feed_face, roi_mask in zip(batch["prediction"],
                                                   batch["detected_faces"],
                                                   batch["feed_faces"],
                                                   batch["roi_masks"]):
            self._crop_out_of_bounds(mask, roi_mask)
              face.add_mask(self._storage_name,
                            mask,
                            feed_face.adjusted_matrix,
                            feed_face.interpolators[1],
                            storage_size=self._storage_size,
keep keep keep keep replace replace replace keep keep keep keep keep
 extract: mask - Delete any mask from outside of frame boundaries 
plugins/extract/mask/_base.py