code: def auxiliary_net(self, in_, stag="auxiliary_net"):
      bn1 = tf.layers.batch_normalization(
          inputs=in_, name="bn1" + stag, reuse=tf.AUTO_REUSE
      )
      dnn1 = tf.layers.dense(
          bn1,
          100,
          activation=None,
          name="f1" + stag,
          reuse=tf.AUTO_REUSE,
      )
      dnn1 = tf.nn.sigmoid(dnn1)
      dnn2 = tf.layers.dense(
          dnn1,
          50,
          activation=None,
          name="f2" + stag,
          reuse=tf.AUTO_REUSE,
      )
      dnn2 = tf.nn.sigmoid(dnn2)
      dnn3 = tf.layers.dense(
          dnn2,
          2,
          activation=None,
          name="f3" + stag,
          reuse=tf.AUTO_REUSE,
      )
      y_hat = tf.nn.softmax(dnn3) + 0.00000001
      return y_hat
  
imports: import tensorflow
  from rnn import dynamic_rnn
  from utils import *
  from bigdl.dllib.utils.log4Error import *
label_desc: Use image node_id 4 for calling the Model obj's underlying member method code with example usage: obj.auxiliary_net(in_, stag) and returns: y_hat