def box_predictor(
        self,
        image_feats: torch.FloatTensor,
        feature_map: torch.FloatTensor,
    ) -> torch.FloatTensor:
        """
        Args:
            image_feats:
                Features extracted from the image, returned by the `image_text_embedder` method.
            feature_map:
                A spatial re-arrangement of image_features, also returned by the `image_text_embedder` method.
        Returns:
            pred_boxes:
                List of predicted boxes (cxcywh normalized to 0, 1) nested within a dictionary.
        """
        # Bounding box detection head [batch_size, num_boxes, 4].
        pred_boxes = self.box_head(image_feats)

        # Compute the location of each token on the grid and use it to compute a bias for the bbox prediction
        pred_boxes += self.compute_box_bias(feature_map)
        pred_boxes = self.sigmoid(pred_boxes)
        return pred_boxes

        Args:
            image_feats:
                Features extracted from the image, returned by the `image_text_embedder` method.
            feature_map:
                A spatial re-arrangement of image_features, also returned by the `image_text_embedder` method.
        Returns:
            pred_boxes:
                List of predicted boxes (cxcywh normalized to 0, 1) nested within a dictionary.
        
dpkd/transformers/src/transformers/models/owlv2/modeling_owlv2.py