def _decode_boxes(self, raw_boxes, anchors):
        """Converts the predictions into actual coordinates using
        the anchor boxes. Processes the entire batch at once.
        """
        boxes = torch.zeros_like(raw_boxes)

        x_center = raw_boxes[..., 0] / self.x_scale * \
            anchors[:, 2] + anchors[:, 0]
        y_center = raw_boxes[..., 1] / self.y_scale * \
            anchors[:, 3] + anchors[:, 1]

        w = raw_boxes[..., 2] / self.w_scale * anchors[:, 2]
        h = raw_boxes[..., 3] / self.h_scale * anchors[:, 3]

        boxes[..., 0] = y_center - h / 2.  # ymin
        boxes[..., 1] = x_center - w / 2.  # xmin
        boxes[..., 2] = y_center + h / 2.  # ymax
        boxes[..., 3] = x_center + w / 2.  # xmax

        for k in range(6):
            offset = 4 + k * 2
            keypoint_x = raw_boxes[..., offset] / \
                self.x_scale * anchors[:, 2] + anchors[:, 0]
            keypoint_y = raw_boxes[..., offset + 1] / \
                self.y_scale * anchors[:, 3] + anchors[:, 1]
            boxes[..., offset] = keypoint_x
            boxes[..., offset + 1] = keypoint_y

        return boxes
Converts the predictions into actual coordinates using
        the anchor boxes. Processes the entire batch at once.
        
face_alignment/detection/blazeface/net_blazeface.py