code: def forward_eval(self, feats):
      cls_score_list, reg_box_list = [], []
      anchor_points, _, _ = self._generate_anchors(feats)
      for i, (feat, stride) in enumerate(zip(feats, self.fpn_strides)):
          b, _, h, w = paddle.shape(feat)
          l = h * w
          # cls
          avg_feat = F.adaptive_avg_pool2d(feat, (1, 1))
          cls_logit = self.pred_cls[i](
              self.stem_cls[i](feat, avg_feat) + feat
          )
          # reg
          reg_dist = self.pred_reg[i](self.stem_reg[i](feat, avg_feat))
          reg_xy, reg_wh = paddle.split(reg_dist, 2, axis=1)
          reg_xy = reg_xy * stride
          reg_wh = (F.elu(reg_wh) + 1.0) * stride
          reg_angle = self.pred_angle[i](
              self.stem_angle[i](feat, avg_feat)
          )
          reg_angle = self.angle_proj_conv(F.softmax(reg_angle, axis=1))
          reg_box = paddle.concat([reg_xy, reg_wh, reg_angle], axis=1)
          # cls and reg
          cls_score = F.sigmoid(cls_logit)
          cls_score_list.append(
              cls_score.reshape([b, self.num_classes, l])
          )
          reg_box_list.append(reg_box.reshape([b, 5, l]))
  
      cls_score_list = paddle.concat(cls_score_list, axis=-1)
      reg_box_list = paddle.concat(reg_box_list, axis=-1).transpose(
          [0, 2, 1]
      )
      reg_xy, reg_wha = paddle.split(reg_box_list, [2, 3], axis=-1)
      reg_xy = reg_xy + anchor_points
      reg_box_list = paddle.concat([reg_xy, reg_wha], axis=-1)
      return cls_score_list, reg_box_list
  
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 7 for calling the PPYOLOERHead obj's underlying member method code with example usage: obj.forward_eval(feats) and returns: cls_score_list,  reg_box_list