class_docstr: Head block for Face detection network
  
  Args:
      num_classes (int): Number of output classes.
      in_channels (int): Number of input channels.
      anchor_generator(object): instance of anchor genertor method.
      kernel_size (int): kernel size of Conv2D in FaceHead.
      padding (int): padding of Conv2D in FaceHead.
      conv_decay (float): norm_decay (float): weight decay for conv layer weights.
      loss (object): loss of face detection model.
class_docstr_tok: ["Head","block","for","Face","detection","network","Args",":","num_classes","(","int",")",":","Number","of","output","classes",".","in_channels","(","int",")",":","Number","of","input","channels",".","anchor_generator","(","object",")",":","instance","of","anchor","genertor","method",".","kernel_size","(","int",")",":","kernel","size","of","Conv2D","in","FaceHead",".","padding","(","int",")",":","padding","of","Conv2D","in","FaceHead",".","conv_decay","(","float",")",":","norm_decay","(","float",")",":","weight","decay","for","conv","layer","weights",".","loss","(","object",")",":","loss","of","face","detection","model","."]
code: def forward(self, feats, image, gt_bbox=None, gt_class=None):
      box_preds = []
      cls_scores = []
      prior_boxes = []
      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
  
file_path: PaddleDetection/ppdet/modeling/heads/face_head.py
imports: import paddle
  import paddle.nn
  from ppdet.core.workspace import register
  from ..layers import AnchorGeneratorSSD
  from ..cls_utils import _get_class_default_kwargs
label_desc: Use image node_id 3 for calling the FaceHead 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