code: def get_bboxes_single(
      self,
      anchors,
      cls_scores,
      bbox_preds,
      im_shape,
      scale_factor,
      rescale=True,
  ):
      assert len(cls_scores) == len(bbox_preds)
      mlvl_bboxes = []
      mlvl_scores = []
      for anchor, cls_score, bbox_pred in zip(
          anchors, cls_scores, bbox_preds
      ):
          cls_score = cls_score.reshape([-1, self.num_classes])
          bbox_pred = bbox_pred.reshape([-1, 4])
          if (
              self.nms_pre is not None
              and cls_score.shape[0] > self.nms_pre
          ):
              max_score = cls_score.max(axis=1)
              _, topk_inds = max_score.topk(self.nms_pre)
              bbox_pred = bbox_pred.gather(topk_inds)
              anchor = anchor.gather(topk_inds)
              cls_score = cls_score.gather(topk_inds)
  
          bbox_pred = delta2bbox_v2(
              bbox_pred,
              anchor,
              self.delta_mean,
              self.delta_std,
              max_shape=im_shape,
              ctr_clip=self.ctr_clip,
          ).squeeze()
          mlvl_bboxes.append(bbox_pred)
          mlvl_scores.append(F.sigmoid(cls_score))
      mlvl_bboxes = paddle.concat(mlvl_bboxes)
      mlvl_bboxes = paddle.squeeze(mlvl_bboxes)
      if rescale:
          mlvl_bboxes = mlvl_bboxes / paddle.concat(
              [scale_factor[::-1], scale_factor[::-1]]
          )
      mlvl_scores = paddle.concat(mlvl_scores)
      mlvl_scores = mlvl_scores.transpose([1, 0])
      return mlvl_bboxes, mlvl_scores
  
imports: import math
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle import ParamAttr
  from paddle.regularizer import L2Decay
  from paddle.nn.initializer import Normal, Constant
  from ppdet.modeling.layers import MultiClassNMS
  from ppdet.core.workspace import register
  from ppdet.modeling.bbox_utils import delta2bbox_v2
label_desc: Use image node_id 4 for calling the YOLOFHead obj's underlying member method code with example usage: obj.get_bboxes_single(anchors, cls_scores, bbox_preds, im_shape, scale_factor, rescale) and returns: mlvl_bboxes,  mlvl_scores