code: def get_loss_from_assign(
      self,
      pred_scores,
      pred_distri,
      pred_bboxes,
      anchor_points_s,
      assigned_labels,
      assigned_bboxes,
      assigned_scores,
      alpha_l,
  ):
      # cls loss
      if self.use_varifocal_loss:
          one_hot_label = F.one_hot(
              assigned_labels, self.num_classes + 1
          )[..., :-1]
          loss_cls = self._varifocal_loss(
              pred_scores, assigned_scores, one_hot_label
          )
      else:
          loss_cls = self._focal_loss(
              pred_scores, assigned_scores, alpha_l
          )
  
      assigned_scores_sum = assigned_scores.sum()
      if paddle.distributed.get_world_size() > 1:
          paddle.distributed.all_reduce(assigned_scores_sum)
          assigned_scores_sum /= paddle.distributed.get_world_size()
      assigned_scores_sum = paddle.clip(assigned_scores_sum, min=1.0)
      loss_cls /= assigned_scores_sum
  
      if self.for_distill:
          self.distill_pairs["pred_cls_scores"] = pred_scores
          self.distill_pairs["pos_num"] = assigned_scores_sum
          self.distill_pairs["assigned_scores"] = assigned_scores
  
          one_hot_label = F.one_hot(
              assigned_labels, self.num_classes + 1
          )[..., :-1]
          self.distill_pairs["target_labels"] = one_hot_label
  
      loss_l1, loss_iou, loss_dfl = self._bbox_loss(
          pred_distri,
          pred_bboxes,
          anchor_points_s,
          assigned_labels,
          assigned_bboxes,
          assigned_scores,
          assigned_scores_sum,
      )
      loss = (
          self.loss_weight["class"] * loss_cls
          + self.loss_weight["iou"] * loss_iou
          + self.loss_weight["dfl"] * loss_dfl
      )
      out_dict = {
          "loss": loss,
          "loss_cls": loss_cls,
          "loss_iou": loss_iou,
          "loss_dfl": loss_dfl,
          "loss_l1": loss_l1,
      }
      return out_dict
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import register
  from paddle import ParamAttr
  from paddle.nn.initializer import KaimingNormal
  from paddle.nn.initializer import Normal, Constant
  from ..bbox_utils import batch_distance2bbox
  from ..losses import GIoULoss
  from ..initializer import bias_init_with_prob, constant_, normal_
  from ..assigners.utils import generate_anchors_for_grid_cell
  from ppdet.modeling.backbones.cspresnet import ConvBNLayer, RepVggBlock
  from ppdet.modeling.ops import get_static_shape, get_act_fn
  from ppdet.modeling.layers import MultiClassNMS
label_desc: Use image node_id 16 for calling the PPYOLOEHead obj's underlying member method code with example usage: obj.get_loss_from_assign(pred_scores, pred_distri, pred_bboxes, anchor_points_s, assigned_labels, assigned_bboxes, assigned_scores, alpha_l) and returns: out_dict