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: Args:
  Converts the output of [`ConditionalDetrForObjectDetection`] into the format expected by the COCO api. Only
  supports PyTorch.
      outputs ([`ConditionalDetrObjectDetectionOutput`]):
          Raw outputs of the model.
      target_sizes (`torch.Tensor` of shape `(batch_size, 2)`):
          Tensor containing the size (h, w) 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):
      """
      Args:
      Converts the output of [`ConditionalDetrForObjectDetection`] into the format expected by the COCO api. Only
      supports PyTorch.
          outputs ([`ConditionalDetrObjectDetectionOutput`]):
              Raw outputs of the model.
          target_sizes (`torch.Tensor` of shape `(batch_size, 2)`):
              Tensor containing the size (h, w) 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.
      """
      warnings.warn(
          "`post_process` is deprecated and will be removed in v5 of Transformers, please use"
          " `post_process_object_detection`",
          FutureWarning,
      )
  
      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), 300, 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
      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 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 12 for calling the ConditionalDetrFeatureExtractor obj's underlying member method code with example usage: obj.post_process(outputs, target_sizes) and returns: results