code: def auxiliary_loss(
      self, h_states, click_seq, noclick_seq, mask, stag=None
  ):
      def dtype_getter(getter, name, dtype=None, *args, **kwargs):
          var = getter(name, dtype=self.model_dtype, *args, **kwargs)
          return var
  
      with tf.variable_scope(
          "aux_loss", custom_getter=dtype_getter, dtype=self.model_dtype
      ):
          mask = tf.cast(mask, self.model_dtype)
          click_input_ = tf.concat([h_states, click_seq], -1)
          noclick_input_ = tf.concat([h_states, noclick_seq], -1)
          click_prop_ = self.auxiliary_net(click_input_, stag=stag)[
              :, :, 0
          ]
          noclick_prop_ = self.auxiliary_net(noclick_input_, stag=stag)[
              :, :, 0
          ]
          click_loss_ = (
              -tf.reshape(
                  tf.log(click_prop_), [-1, tf.shape(click_seq)[1]]
              )
              * mask
          )
          noclick_loss_ = (
              -tf.reshape(
                  tf.log(1.0 - noclick_prop_),
                  [-1, tf.shape(noclick_seq)[1]],
              )
              * mask
          )
          loss_ = tf.reduce_mean(click_loss_ + noclick_loss_)
          return loss_
  
imports: import tensorflow
  from rnn import dynamic_rnn
  from utils import *
  from bigdl.dllib.utils.log4Error import *
label_desc: Use image node_id 3 for calling the Model obj's underlying member method code with example usage: obj.auxiliary_loss(h_states, click_seq, noclick_seq, mask, stag) and returns: var, loss_