    def __call__(self, boxes: Tensor, scores: Tensor) -> Tensor:
        """Performs shape inference and returns an output tensor."""
        self._attrs["inputs"] = [boxes, scores]
        self._set_depth()
        self._extract_exec_path(boxes)
        output_shape = self._infer_shapes(boxes, scores)

        x = boxes
        num_detections = Tensor(
            [output_shape[0], IntImm(1)], dtype="int64", src_ops={self}
        )
        detection_boxes = Tensor(
            output_shape,
            src_ops={self},
            dtype=x._attrs["dtype"],
        )
        detection_scores = Tensor(
            output_shape[:-1],
            src_ops={self},
            dtype=x._attrs["dtype"],
        )
        detection_classes = Tensor(output_shape[:-1], dtype="int64", src_ops={self})
        output = (num_detections, detection_boxes, detection_scores, detection_classes)
        self._attrs["outputs"] = [
            num_detections,
            detection_boxes,
            detection_scores,
            detection_classes,
        ]
        return output
Performs shape inference and returns an output tensor.
python/aitemplate/compiler/ops/vision_ops/nms/efficient_nms.py