def post_process_object_detection(
        self, outputs, threshold: float = 0.5, target_sizes: Union[TensorType, List[Tuple]] = None, top_k: int = 100
    ):
        """
        Converts the raw output of [`ConditionalDetrForObjectDetection`] into final bounding boxes in (top_left_x,
        top_left_y, bottom_right_x, bottom_right_y) format. 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*):
                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.
            top_k (`int`, *optional*, defaults to 100):
                Keep only top k bounding boxes before filtering by thresholding.

        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()
        prob = prob.view(out_logits.shape[0], -1)
        k_value = min(top_k, prob.size(1))
        topk_values, topk_indexes = torch.topk(prob, k_value, 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
        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).to(boxes.device)
        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

        Converts the raw output of [`ConditionalDetrForObjectDetection`] into final bounding boxes in (top_left_x,
        top_left_y, bottom_right_x, bottom_right_y) format. 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*):
                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.
            top_k (`int`, *optional*, defaults to 100):
                Keep only top k bounding boxes before filtering by thresholding.

        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.
        
dpkd/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py