def METHOD_NAME(self, inputs):
    self._validate_inputs(inputs)
    images = inputs.get("images", None)
    labels = inputs.get("labels", None)
    bounding_boxes = inputs.get("bounding_boxes", None)
    segmentation_masks = inputs.get("segmentation_masks", None)
    images, lambda_sample, permutation_order = self._mixup(images)
    if labels is not None:
        labels = self._update_labels(
            tf.cast(labels, dtype=self.compute_dtype),
            lambda_sample,
            permutation_order,
        )
        inputs["labels"] = labels
    if bounding_boxes is not None:
        bounding_boxes = self._update_bounding_boxes(
            bounding_boxes, permutation_order
        )
        inputs["bounding_boxes"] = bounding_boxes
    inputs["images"] = images
    if segmentation_masks is not None:
        segmentation_masks = self._update_segmentation_masks(
            segmentation_masks, lambda_sample, permutation_order
        )
        inputs["segmentation_masks"] = segmentation_masks
    return inputs