code: def resize_annotation(
      annotation: Dict[str, Any],
      orig_size: Tuple[int, int],
      target_size: Tuple[int, int],
      threshold: float = 0.5,
      resample: PILImageResampling = PILImageResampling.NEAREST,
  ):
      """
      Resizes an annotation to a target size.
  
      Args:
          annotation (`Dict[str, Any]`):
              The annotation dictionary.
          orig_size (`Tuple[int, int]`):
              The original size of the input image.
          target_size (`Tuple[int, int]`):
              The target size of the image, as returned by the preprocessing `resize` step.
          threshold (`float`, *optional*, defaults to 0.5):
              The threshold used to binarize the segmentation masks.
          resample (`PILImageResampling`, defaults to `PILImageResampling.NEAREST`):
              The resampling filter to use when resizing the masks.
      """
      ratios = tuple(
          float(s) / float(s_orig)
          for s, s_orig in zip(target_size, orig_size)
      )
      ratio_height, ratio_width = ratios
  
      new_annotation = {}
      new_annotation["size"] = target_size
  
      for key, value in annotation.items():
          if key == "boxes":
              boxes = value
              scaled_boxes = boxes * np.asarray(
                  [
                      ratio_width,
                      ratio_height,
                      ratio_width,
                      ratio_height,
                  ],
                  dtype=np.float32,
              )
              new_annotation["boxes"] = scaled_boxes
          elif key == "area":
              area = value
              scaled_area = area * (ratio_width * ratio_height)
              new_annotation["area"] = scaled_area
          elif key == "masks":
              masks = value[:, None]
              masks = np.array(
                  [
                      resize(mask, target_size, resample=resample)
                      for mask in masks
                  ]
              )
              masks = masks.astype(np.float32)
              masks = masks[:, 0] > threshold
              new_annotation["masks"] = masks
          elif key == "size":
              new_annotation["size"] = target_size
          else:
              new_annotation[key] = value
  
      return new_annotation
  
imports: import io
  import pathlib
  from collections import defaultdict
  from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Tuple, Union
  import numpy
  from ...feature_extraction_utils import BatchFeature
  from ...image_processing_utils import BaseImageProcessor, get_size_dict
  from ...image_transforms import PaddingMode, center_to_corners_format, corners_to_center_format, id_to_rgb, pad, rescale, resize, rgb_to_id, to_channel_dimension_format
  from ...image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, AnnotationFormat, AnnotationType, ChannelDimension, ImageInput, PILImageResampling, get_image_size, infer_channel_dimension_format, is_scaled_image, make_list_of_images, to_numpy_array, valid_images, validate_annotations
  from ...utils import TensorType, is_flax_available, is_jax_tensor, is_scipy_available, is_tf_available, is_tf_tensor, is_torch_available, is_torch_tensor, is_vision_available, logging
label_desc: Use image node_id 17 for calling a global function with example usage: resize_annotation(annotation, orig_size, target_size, threshold, resample) and returns: new_annotation