def METHOD_NAME(self, p, t, gt_box, anchor, downsample, scale=1.,
                eps=1e-10):
    na = len(anchor)
    b, c, h, w = p.shape
    if self.iou_aware_loss:
        ioup, p = p[:, 0:na, :, :], p[:, na:, :, :]
        ioup = ioup.unsqueeze(-1)
    p = p.reshape((b, na, -1, h, w)).transpose((0, 1, 3, 4, 2))
    x, y = p[:, :, :, :, 0:1], p[:, :, :, :, 1:2]
    w, h = p[:, :, :, :, 2:3], p[:, :, :, :, 3:4]
    obj, pcls = p[:, :, :, :, 4:5], p[:, :, :, :, 5:]
    self.distill_pairs.append([x, y, w, h, obj, pcls])
    t = t.transpose((0, 1, 3, 4, 2))
    tx, ty = t[:, :, :, :, 0:1], t[:, :, :, :, 1:2]
    tw, th = t[:, :, :, :, 2:3], t[:, :, :, :, 3:4]
    tscale = t[:, :, :, :, 4:5]
    tobj, tcls = t[:, :, :, :, 5:6], t[:, :, :, :, 6:]
    tscale_obj = tscale * tobj
    loss = dict()
    x = scale * F.sigmoid(x) - 0.5 * (scale - 1.)
    y = scale * F.sigmoid(y) - 0.5 * (scale - 1.)
    if abs(scale - 1.) < eps:
        loss_x = F.binary_cross_entropy(x, tx, reduction='none')
        loss_y = F.binary_cross_entropy(y, ty, reduction='none')
        loss_xy = tscale_obj * (loss_x + loss_y)
    else:
        loss_x = paddle.abs(x - tx)
        loss_y = paddle.abs(y - ty)
        loss_xy = tscale_obj * (loss_x + loss_y)
    loss_xy = loss_xy.sum([1, 2, 3, 4]).mean()
    loss_w = paddle.abs(w - tw)
    loss_h = paddle.abs(h - th)
    loss_wh = tscale_obj * (loss_w + loss_h)
    loss_wh = loss_wh.sum([1, 2, 3, 4]).mean()
    loss['loss_xy'] = loss_xy
    loss['loss_wh'] = loss_wh
    if self.iou_loss is not None:
        # warn: do not modify x, y, w, h in place
        box, tbox = [x, y, w, h], [tx, ty, tw, th]
        pbox = bbox_transform(box, anchor, downsample)
        gbox = bbox_transform(tbox, anchor, downsample)
        loss_iou = self.iou_loss(pbox, gbox)
        loss_iou = loss_iou * tscale_obj
        loss_iou = loss_iou.sum([1, 2, 3, 4]).mean()
        loss['loss_iou'] = loss_iou
    if self.iou_aware_loss is not None:
        box, tbox = [x, y, w, h], [tx, ty, tw, th]
        pbox = bbox_transform(box, anchor, downsample)
        gbox = bbox_transform(tbox, anchor, downsample)
        loss_iou_aware = self.iou_aware_loss(ioup, pbox, gbox)
        loss_iou_aware = loss_iou_aware * tobj
        loss_iou_aware = loss_iou_aware.sum([1, 2, 3, 4]).mean()
        loss['loss_iou_aware'] = loss_iou_aware
    box = [x, y, w, h]
    loss_obj = self.obj_loss(box, gt_box, obj, tobj, anchor, downsample)
    loss_obj = loss_obj.sum(-1).mean()
    loss['loss_obj'] = loss_obj
    loss_cls = self.cls_loss(pcls, tcls) * tobj
    loss_cls = loss_cls.sum([1, 2, 3, 4]).mean()
    loss['loss_cls'] = loss_cls
    return loss