class_docstr: SSDHead
  
  Args:
      num_classes (int): Number of classes
      in_channels (list): Number of channels per input feature
      anchor_generator (dict): Configuration of 'AnchorGeneratorSSD' instance
      kernel_size (int): Conv kernel size
      padding (int): Conv padding
      use_sepconv (bool): Use SepConvLayer if true
      conv_decay (float): Conv regularization coeff
      loss (object): 'SSDLoss' instance
      use_extra_head (bool): If use ResNet34 as baskbone, you should set `use_extra_head`=True
code: def forward(self, feats, image, gt_bbox=None, gt_class=None):
      if self.use_extra_head:
          assert len(feats) == 1, (
              "If you set use_extra_head=True, backbone feature "
              "list length should be 1."
          )
          feats = self.ssd_extra_head(feats[0])
      box_preds = []
      cls_scores = []
      for feat, box_conv, score_conv in zip(
          feats, self.box_convs, self.score_convs
      ):
          box_pred = box_conv(feat)
          box_pred = paddle.transpose(box_pred, [0, 2, 3, 1])
          box_pred = paddle.reshape(box_pred, [0, -1, 4])
          box_preds.append(box_pred)
  
          cls_score = score_conv(feat)
          cls_score = paddle.transpose(cls_score, [0, 2, 3, 1])
          cls_score = paddle.reshape(
              cls_score, [0, -1, self.num_classes]
          )
          cls_scores.append(cls_score)
  
      prior_boxes = self.anchor_generator(feats, image)
  
      if self.training:
          return self.get_loss(
              box_preds, cls_scores, gt_bbox, gt_class, prior_boxes
          )
      else:
          return (box_preds, cls_scores), prior_boxes
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import register
  from paddle.regularizer import L2Decay
  from paddle import ParamAttr
  from ..layers import AnchorGeneratorSSD
  from ..cls_utils import _get_class_default_kwargs
label_desc: Use image node_id 3 for calling the SSDHead obj's underlying member method code with example usage: obj.forward(feats, image, gt_bbox, gt_class) and returns: self, box_preds,  cls_scores,  prior_boxes