          ----------
          batch: :class:`AlignerBatch`
              The current batch from the model with :attr:`predictions` populated
          """
          logger.trace("Obtain points from prediction")  # type:ignore
          num_images, num_landmarks = batch.prediction.shape[:2]
          image_slice = np.repeat(np.arange(num_images)[:, None], num_landmarks, axis=1)
          landmark_slice = np.repeat(np.arange(num_landmarks)[None, :], num_images, axis=0)
          resolution = np.full((num_images, num_landmarks), 64, dtype='int32')
          subpixel_landmarks = np.ones((num_images, num_landmarks, 3), dtype='float32')
 
</s> remove         logger.debug("Transforming Points")
 </s> add         logger.trace("Transforming Points")  # type:ignore[attr-defined] </s> remove         logger.trace("Aligning faces around center")  # type:ignore
 </s> add         logger.trace("Aligning faces around center")  # type:ignore[attr-defined] </s> remove         logger.trace(retval.shape)  # type:ignore
 </s> add         logger.trace(retval.shape)  # type:ignore[attr-defined] </s> remove         logger.trace("Predicting Landmarks")  # type:ignore
 </s> add         logger.trace("Predicting Landmarks")  # type:ignore[attr-defined] </s> remove         logger.trace("Obtained points from prediction: %s", batch.landmarks)  # type:ignore
 </s> add         logger.trace("Obtained points from prediction: %s",  # type:ignore[attr-defined]
                     batch.landmarks)
        logger.trace("Obtain points from prediction")  # type:ignore[attr-defined]
          ----------
          batch: :class:`AlignerBatch`
              The current batch from the model with :attr:`predictions` populated
          """
        logger.trace("Obtain points from prediction")  # type:ignore[attr-defined]
          num_images, num_landmarks = batch.prediction.shape[:2]
          image_slice = np.repeat(np.arange(num_images)[:, None], num_landmarks, axis=1)
          landmark_slice = np.repeat(np.arange(num_landmarks)[None, :], num_images, axis=0)
          resolution = np.full((num_images, num_landmarks), 64, dtype='int32')
          subpixel_landmarks = np.ones((num_images, num_landmarks, 3), dtype='float32')
keep keep keep keep replace keep keep keep keep keep
 Bugfix - Extract - re-align with identity filtering enabled 
plugins/extract/align/fan.py