def post_process(self, outputs, target_sizes):
        """
        Converts the raw output of [`YolosForObjectDetection`] into final bounding boxes in (top_left_x, top_left_y,
        bottom_right_x, bottom_right_y) format. Only supports PyTorch.

        Args:
            outputs ([`YolosObjectDetectionOutput`]):
                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 = nn.functional.softmax(out_logits, -1)
        scores, labels = prob[..., :-1].max(-1)

        # convert to [x0, y0, x1, y1] format
        boxes = center_to_corners_format(out_bbox)
        # 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).to(boxes.device)
        boxes = boxes * scale_fct[:, None, :]

        results = [{"scores": s, "labels": l, "boxes": b} for s, l, b in zip(scores, labels, boxes)]
        return results

        Converts the raw output of [`YolosForObjectDetection`] into final bounding boxes in (top_left_x, top_left_y,
        bottom_right_x, bottom_right_y) format. Only supports PyTorch.

        Args:
            outputs ([`YolosObjectDetectionOutput`]):
                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.
        
dpkd/transformers/src/transformers/models/yolos/image_processing_yolos.py