code: def transform_instance_annotations(
      annotation, transforms, image_size, *, keypoint_hflip_indices=None
  ):
      """
      Apply transforms to box, segmentation and keypoints annotations of a single instance.
  
      It will use `transforms.apply_box` for the box, and
      `transforms.apply_coords` for segmentation polygons & keypoints.
      If you need anything more specially designed for each data structure,
      you'll need to implement your own version of this function or the transforms.
  
      Args:
          annotation (dict): dict of instance annotations for a single instance.
              It will be modified in-place.
          transforms (TransformList):
          image_size (tuple): the height, width of the transformed image
          keypoint_hflip_indices (ndarray[int]): see `create_keypoint_hflip_indices`.
  
      Returns:
          dict:
              the same input dict with fields "bbox", "segmentation", "keypoints"
              transformed according to `transforms`.
              The "bbox_mode" field will be set to XYXY_ABS.
      """
      try_import_pycocotools()
      import pycocotools.mask as mask_util
  
      # bbox is 1d (per-instance bounding box)
      bbox = BoxMode.convert(
          annotation["bbox"], annotation["bbox_mode"], BoxMode.XYXY_ABS
      )
      # clip transformed bbox to image size
      bbox = transforms.apply_box([bbox])[0].clip(min=0)
      annotation["bbox"] = np.minimum(
          bbox, list(image_size + image_size)[::-1]
      )
      annotation["bbox_mode"] = BoxMode.XYXY_ABS
  
      if "segmentation" in annotation:
          # each instance contains 1 or more polygons
          segm = annotation["segmentation"]
          if isinstance(segm, list):
              # polygons
              polygons = [np.asarray(p).reshape(-1, 2) for p in segm]
              annotation["segmentation"] = [
                  p.reshape(-1)
                  for p in transforms.apply_polygons(polygons)
              ]
          elif isinstance(segm, dict):
              # RLE
              mask = mask_util.decode(segm)
              mask = transforms.apply_segmentation(mask)
              assert tuple(mask.shape[:2]) == image_size
              annotation["segmentation"] = mask
          else:
              raise ValueError(
                  "Cannot transform segmentation of type '{}'!"
                  "Supported types are: polygons as list[list[float] or ndarray],"
                  " COCO-style RLE as a dict.".format(type(segm))
              )
  
      if "keypoints" in annotation:
          keypoints = transform_keypoint_annotations(
              annotation["keypoints"],
              transforms,
              image_size,
              keypoint_hflip_indices,
          )
          annotation["keypoints"] = keypoints
  
      if "beziers" in annotation:
          beziers = transform_beziers_annotations(
              annotation["beziers"], transforms
          )
          annotation["beziers"] = beziers
  
      return annotation
  
imports: import os
  import logging
  import numpy
  from PIL import Image
  import torch
  from ..structures import BitMasks, Boxes, BoxMode, Instances, Keypoints, PolygonMasks, polygons_to_bitmask
  from ..transforms import instance_transforms
  from ..registry.catalog import MetadataCatalog
label_desc: Use image node_id 10 for calling a global function with example usage: transform_instance_annotations(annotation, transforms, image_size) and returns: annotation