code: def forward_train(self, feats, targets):
      (
          anchor_points,
          stride_tensor,
          num_anchors_list,
      ) = self._generate_anchors(feats)
  
      cls_score_list, reg_dist_list, reg_angle_list = [], [], []
      for i, feat in enumerate(feats):
          avg_feat = F.adaptive_avg_pool2d(feat, (1, 1))
          cls_logit = self.pred_cls[i](
              self.stem_cls[i](feat, avg_feat) + feat
          )
          reg_dist = self.pred_reg[i](self.stem_reg[i](feat, avg_feat))
          reg_angle = self.pred_angle[i](
              self.stem_angle[i](feat, avg_feat)
          )
          # cls and reg
          cls_score = F.sigmoid(cls_logit)
          cls_score_list.append(
              cls_score.flatten(2).transpose([0, 2, 1])
          )
          reg_dist_list.append(reg_dist.flatten(2).transpose([0, 2, 1]))
          reg_angle_list.append(
              reg_angle.flatten(2).transpose([0, 2, 1])
          )
      cls_score_list = paddle.concat(cls_score_list, axis=1)
      reg_dist_list = paddle.concat(reg_dist_list, axis=1)
      reg_angle_list = paddle.concat(reg_angle_list, axis=1)
  
      return self.get_loss(
          [
              cls_score_list,
              reg_dist_list,
              reg_angle_list,
              anchor_points,
              num_anchors_list,
              stride_tensor,
          ],
          targets,
      )
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import register
  from ..losses import ProbIoULoss
  from ..initializer import bias_init_with_prob, constant_, normal_, vector_
  from ppdet.modeling.backbones.cspresnet import ConvBNLayer
  from ppdet.modeling.ops import get_static_shape, get_act_fn, anchor_generator
  from ppdet.modeling.layers import MultiClassNMS
label_desc: Use image node_id 6 for calling the PPYOLOERHead obj's underlying member method code with example usage: obj.forward_train(feats, targets) and returns: self