class_docstr: Constructs a Detr image processor.
  
  Args:
      format (`str`, *optional*, defaults to `"coco_detection"`):
          Data format of the annotations. One of "coco_detection" or "coco_panoptic".
      do_resize (`bool`, *optional*, defaults to `True`):
          Controls whether to resize the image's (height, width) dimensions to the specified `size`. Can be
          overridden by the `do_resize` parameter in the `preprocess` method.
      size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge": 800, "longest_edge": 1333}`):
          Size of the image's (height, width) dimensions after resizing. Can be overridden by the `size` parameter in
          the `preprocess` method.
      resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
          Resampling filter to use if resizing the image.
      do_rescale (`bool`, *optional*, defaults to `True`):
          Controls whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the
          `do_rescale` parameter in the `preprocess` method.
      rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
          Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the
          `preprocess` method.
      do_normalize:
          Controls whether to normalize the image. Can be overridden by the `do_normalize` parameter in the
          `preprocess` method.
      image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_DEFAULT_MEAN`):
          Mean values to use when normalizing the image. Can be a single value or a list of values, one for each
          channel. Can be overridden by the `image_mean` parameter in the `preprocess` method.
      image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_DEFAULT_STD`):
          Standard deviation values to use when normalizing the image. Can be a single value or a list of values, one
          for each channel. Can be overridden by the `image_std` parameter in the `preprocess` method.
      do_pad (`bool`, *optional*, defaults to `True`):
          Controls whether to pad the image to the largest image in a batch and create a pixel mask. Can be
          overridden by the `do_pad` parameter in the `preprocess` method.
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docstr: Prepare an annotation for feeding into DETR model.
docstr_tok: ["Prepare","an","annotation","for","feeding","into","DETR","model","."]
code: def prepare_annotation(
      self,
      image: np.ndarray,
      target: Dict,
      format: Optional[AnnotationFormat] = None,
      return_segmentation_masks: bool = None,
      masks_path: Optional[Union[str, pathlib.Path]] = None,
      input_data_format: Optional[Union[str, ChannelDimension]] = None,
  ) -> Dict:
      """
      Prepare an annotation for feeding into DETR model.
      """
      format = format if format is not None else self.format
  
      if format == AnnotationFormat.COCO_DETECTION:
          return_segmentation_masks = (
              False
              if return_segmentation_masks is None
              else return_segmentation_masks
          )
          target = prepare_coco_detection_annotation(
              image,
              target,
              return_segmentation_masks,
              input_data_format=input_data_format,
          )
      elif format == AnnotationFormat.COCO_PANOPTIC:
          return_segmentation_masks = (
              True
              if return_segmentation_masks is None
              else return_segmentation_masks
          )
          target = prepare_coco_panoptic_annotation(
              image,
              target,
              masks_path=masks_path,
              return_masks=return_segmentation_masks,
              input_data_format=input_data_format,
          )
      else:
          raise ValueError(f"Format {format} is not supported.")
      return target
  
imports: import pathlib
  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 3 for calling the YolosImageProcessor obj's underlying member method code with example usage: obj.prepare_annotation(image, target, format, return_segmentation_masks, masks_path, input_data_format) and returns: target