def __call__(self, boxes, scores, deltas):
        """
        Args:
            boxes (list[Tensor]): A list of Tensors of predicted class-specific or class-agnostic
                boxes for each image. Element i has shape (Ri, K * 4) if doing
                class-specific regression, or (Ri, 4) if doing class-agnostic
                regression, where Ri is the number of predicted objects for image i.

            scores (list[Tensor]): A list of Tensors of predicted class scores for each image.
                Element i has shape (Ri, K + 1), where Ri is the number of predicted objects
                for image i. Compatible with the output of :meth:`FastRCNNOutputLayers.predict_probs`.

            deltas: refers to the 4-d (dx, dy, dw, dh) deltas that parameterize the box
                transform (see :class:`fast_rcnn_inference.box_transform`).

        Returns:
            proposals.
        """
        proposals = self.box_transform(boxes, deltas)
        if self.nms_on:
            return self.nms_wrapper(proposals, scores)
        else:
            return proposals

        Args:
            boxes (list[Tensor]): A list of Tensors of predicted class-specific or class-agnostic
                boxes for each image. Element i has shape (Ri, K * 4) if doing
                class-specific regression, or (Ri, 4) if doing class-agnostic
                regression, where Ri is the number of predicted objects for image i.

            scores (list[Tensor]): A list of Tensors of predicted class scores for each image.
                Element i has shape (Ri, K + 1), where Ri is the number of predicted objects
                for image i. Compatible with the output of :meth:`FastRCNNOutputLayers.predict_probs`.

            deltas: refers to the 4-d (dx, dy, dw, dh) deltas that parameterize the box
                transform (see :class:`fast_rcnn_inference.box_transform`).

        Returns:
            proposals.
        
examples/02_detectron2/modeling/roi_heads/fast_rcnn.py