class_docstr: Constructs a Deformable 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: Converts the raw output of [`DeformableDetrForObjectDetection`] into final bounding boxes in (top_left_x,
  top_left_y, bottom_right_x, bottom_right_y) format. Only supports PyTorch.
  
  Args:
      outputs ([`DeformableDetrObjectDetectionOutput`]):
          Raw outputs of the model.
      target_sizes (`torch.Tensor` of shape `(batch_size, 2)`):
          Tensor containing the size (height, width) of each image of the batch. For evaluation, this must be the
          original image size (before any data augmentation). For visualization, this should be the image size
          after data augment, but before padding.
  Returns:
      `List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
      in the batch as predicted by the model.
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code: def post_process(self, outputs, target_sizes):
      """
      Converts the raw output of [`DeformableDetrForObjectDetection`] into final bounding boxes in (top_left_x,
      top_left_y, bottom_right_x, bottom_right_y) format. Only supports PyTorch.
  
      Args:
          outputs ([`DeformableDetrObjectDetectionOutput`]):
              Raw outputs of the model.
          target_sizes (`torch.Tensor` of shape `(batch_size, 2)`):
              Tensor containing the size (height, width) of each image of the batch. For evaluation, this must be the
              original image size (before any data augmentation). For visualization, this should be the image size
              after data augment, but before padding.
      Returns:
          `List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
          in the batch as predicted by the model.
      """
      logger.warning_once(
          "`post_process` is deprecated and will be removed in v5 of Transformers, please use"
          " `post_process_object_detection` instead, with `threshold=0.` for equivalent results.",
      )
  
      out_logits, out_bbox = outputs.logits, outputs.pred_boxes
  
      if len(out_logits) != len(target_sizes):
          raise ValueError(
              "Make sure that you pass in as many target sizes as the batch dimension of the logits"
          )
      if target_sizes.shape[1] != 2:
          raise ValueError(
              "Each element of target_sizes must contain the size (h, w) of each image of the batch"
          )
  
      prob = out_logits.sigmoid()
      topk_values, topk_indexes = torch.topk(
          prob.view(out_logits.shape[0], -1), 100, dim=1
      )
      scores = topk_values
      topk_boxes = torch.div(
          topk_indexes, out_logits.shape[2], rounding_mode="floor"
      )
      labels = topk_indexes % out_logits.shape[2]
      boxes = center_to_corners_format(out_bbox)
      boxes = torch.gather(
          boxes, 1, topk_boxes.unsqueeze(-1).repeat(1, 1, 4)
      )
  
      # and from relative [0, 1] to absolute [0, height] coordinates
      img_h, img_w = target_sizes.unbind(1)
      scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1)
      boxes = boxes * scale_fct[:, None, :]
  
      results = [
          {"scores": s, "labels": l, "boxes": b}
          for s, l, b in zip(scores, labels, boxes)
      ]
  
      return results
  
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 15 for calling the DeformableDetrImageProcessor obj's underlying member method code with example usage: obj.post_process(outputs, target_sizes) and returns: results