code: def train(self):
      face_loss = fluid.layers.ssd_loss(
          self.face_mbox_loc,
          self.face_mbox_conf,
          self.face_box,
          self.gt_label,
          self.prior_boxes,
          self.box_vars,
          overlap_threshold=0.35,
          neg_overlap=0.35,
      )
      face_loss.persistable = True
      head_loss = fluid.layers.ssd_loss(
          self.head_mbox_loc,
          self.head_mbox_conf,
          self.head_box,
          self.gt_label,
          self.prior_boxes,
          self.box_vars,
          overlap_threshold=0.35,
          neg_overlap=0.35,
      )
      head_loss.persistable = True
      face_loss = fluid.layers.reduce_sum(face_loss)
      face_loss.persistable = True
      head_loss = fluid.layers.reduce_sum(head_loss)
      head_loss.persistable = True
      total_loss = face_loss + head_loss
      total_loss.persistable = True
      return face_loss, head_loss, total_loss
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  import six
  import paddle.fluid
  from paddle.fluid.param_attr import ParamAttr
  from paddle.fluid.initializer import Xavier
  from paddle.fluid.initializer import Constant
  from paddle.fluid.initializer import Bilinear
  from paddle.fluid.regularizer import L2Decay
label_desc: Use image node_id 11 for calling the PyramidBox obj's underlying member method code with example usage: obj.train() and returns: face_loss,  head_loss,  total_loss