code: def build_fcn_net(self, inp, use_dice=False):
      def dtype_getter(getter, name, dtype=None, *args, **kwargs):
          var = getter(name, dtype=self.model_dtype, *args, **kwargs)
          return var
  
      with tf.variable_scope(
          "fcn", custom_getter=dtype_getter, dtype=self.model_dtype
      ):
          bn1 = tf.layers.batch_normalization(inputs=inp, name="bn1")
          dnn1 = tf.layers.dense(bn1, 200, activation=None, name="f1")
          if use_dice:
              dnn1 = dice(
                  dnn1, name="dice_1", data_type=self.model_dtype
              )
          else:
              dnn1 = prelu(dnn1, "prelu1")
  
          dnn2 = tf.layers.dense(dnn1, 80, activation=None, name="f2")
          if use_dice:
              dnn2 = dice(
                  dnn2, name="dice_2", data_type=self.model_dtype
              )
          else:
              dnn2 = prelu(dnn2, "prelu2")
          dnn3 = tf.layers.dense(dnn2, 2, activation=None, name="f3")
          self.y_hat = tf.nn.softmax(dnn3) + 0.00000001
  
          with tf.name_scope("Metrics"):
              # Cross-entropy loss and optimizer initialization
              ctr_loss = -tf.reduce_mean(
                  tf.log(self.y_hat) * self.target_ph
              )
              self.loss = ctr_loss
              if self.use_negsampling:
                  self.loss += self.aux_loss
              tf.summary.scalar("loss", self.loss)
              self.optim = tf.train.AdamOptimizer(learning_rate=self.lr)
              self.optimizer = self.optim.minimize(self.loss)
              # Accuracy metric
              self.accuracy = tf.reduce_mean(
                  tf.cast(
                      tf.equal(tf.round(self.y_hat), self.target_ph),
                      self.model_dtype,
                  )
              )
              tf.summary.scalar("accuracy", self.accuracy)
  
          self.merged = tf.summary.merge_all()
  
imports: import tensorflow
  from rnn import dynamic_rnn
  from utils import *
  from bigdl.dllib.utils.log4Error import *
label_desc: Use image node_id 2 for calling the Model obj's underlying member method code with example usage: obj.build_fcn_net(inp, use_dice) and returns: var