class_docstr: Constructs a Conditional DETR feature extractor.
  
  This feature extractor inherits from [`FeatureExtractionMixin`] which contains most of the main methods. Users
  should refer to this superclass for more information regarding those methods.
  
  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`):
          Whether to resize the input to a certain `size`.
      size (`int`, *optional*, defaults to 800):
          Resize the input to the given size. Only has an effect if `do_resize` is set to `True`. If size is a
          sequence like `(width, height)`, output size will be matched to this. If size is an int, smaller edge of
          the image will be matched to this number. i.e, if `height > width`, then image will be rescaled to `(size *
          height / width, size)`.
      max_size (`int`, *optional*, defaults to `1333`):
          The largest size an image dimension can have (otherwise it's capped). Only has an effect if `do_resize` is
          set to `True`.
      do_normalize (`bool`, *optional*, defaults to `True`):
          Whether or not to normalize the input with mean and standard deviation.
      image_mean (`int`, *optional*, defaults to `[0.485, 0.456, 0.406]`):
          The sequence of means for each channel, to be used when normalizing images. Defaults to the ImageNet mean.
      image_std (`int`, *optional*, defaults to `[0.229, 0.224, 0.225]`):
          The sequence of standard deviations for each channel, to be used when normalizing images. Defaults to the
          ImageNet std.
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docstr: Converts the output of [`ConditionalDetrForObjectDetection`] into the format expected by the COCO api. Only
  supports PyTorch.
  
  Args:
      outputs ([`DetrObjectDetectionOutput`]):
          Raw outputs of the model.
      threshold (`float`, *optional*):
          Score threshold to keep object detection predictions.
      target_sizes (`torch.Tensor` or `List[Tuple[int, int]]`, *optional*, defaults to `None`):
          Tensor of shape `(batch_size, 2)` or list of tuples (`Tuple[int, int]`) containing the target size
          (height, width) of each image in the batch. If left to None, predictions will not be resized.
  
  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_object_detection(
      self,
      outputs,
      threshold: float = 0.5,
      target_sizes: Union[TensorType, List[Tuple]] = None,
  ):
      """
      Converts the output of [`ConditionalDetrForObjectDetection`] into the format expected by the COCO api. Only
      supports PyTorch.
  
      Args:
          outputs ([`DetrObjectDetectionOutput`]):
              Raw outputs of the model.
          threshold (`float`, *optional*):
              Score threshold to keep object detection predictions.
          target_sizes (`torch.Tensor` or `List[Tuple[int, int]]`, *optional*, defaults to `None`):
              Tensor of shape `(batch_size, 2)` or list of tuples (`Tuple[int, int]`) containing the target size
              (height, width) of each image in the batch. If left to None, predictions will not be resized.
  
      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.
      """
      out_logits, out_bbox = outputs.logits, outputs.pred_boxes
  
      if target_sizes is not None:
          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"
              )
  
      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 = topk_indexes // out_logits.shape[2]
      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
      if isinstance(target_sizes, List):
          img_h = torch.Tensor([i[0] for i in target_sizes])
          img_w = torch.Tensor([i[1] for i in target_sizes])
      else:
          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 = []
      for s, l, b in zip(scores, labels, boxes):
          score = s[s > threshold]
          label = l[s > threshold]
          box = b[s > threshold]
          results.append(
              {"scores": score, "labels": label, "boxes": box}
          )
  
      return results
  
imports: import pathlib
  import warnings
  from typing import Dict, List, Optional, Set, Tuple, Union
  import numpy
  from PIL import Image
  from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
  from ...image_utils import ImageFeatureExtractionMixin, is_torch_tensor
  from ...utils import TensorType, is_torch_available, logging
label_desc: Use image node_id 13 for calling the ConditionalDetrFeatureExtractor obj's underlying member method code with example usage: obj.post_process_object_detection(outputs, threshold, target_sizes) and returns: results