code: def add_inference_layer(self, crf=False, predictions_op_name=None):
      assert (
          self._context_added,
          "Add context representation layer by method add_context_repr before adding inference layer",
      )
      self._inference_added = True
  
      with tf.device("/gpu:{}".format(self.use_gpu_device)):
          with tf.compat.v1.variable_scope("inference", reuse=None):
              self.crf = tf.constant(crf, dtype=tf.bool, name="crf")
  
              if crf:
                  transition_params = tf.compat.v1.get_variable(
                      "transition_params",
                      shape=[self.ntags, self.ntags],
                      initializer=tf.compat.v1.keras.initializers.VarianceScaling(
                          scale=1.0,
                          mode="fan_avg",
                          distribution="uniform",
                      ),
                      use_resource=False,
                  )
  
                  # CRF shape = (batch, sentence)
                  (
                      log_likelihood,
                      self.transition_params,
                  ) = crf_log_likelihood(
                      self.scores,
                      self.labels,
                      self.sentence_lengths,
                      transition_params,
                  )
  
                  tf.identity(log_likelihood, "log_likelihood")
                  tf.identity(
                      self.transition_params, "transition_params"
                  )
  
                  self.loss = tf.reduce_mean(
                      input_tensor=-log_likelihood
                  )
                  if predictions_op_name:
                      with tf.compat.v1.variable_scope(
                          "inference_tmp", reuse=None
                      ):
                          tmp_prediction, _ = crf_decode(
                              self.scores,
                              self.transition_params,
                              self.sentence_lengths,
                          )
  
                      self.prediction = tf.identity(
                          tmp_prediction, name=predictions_op_name
                      )
                  else:
                      self.prediction, _ = crf_decode(
                          self.scores,
                          self.transition_params,
                          self.sentence_lengths,
                      )
                      print(self.prediction)
              else:
                  # Softmax
                  losses = (
                      tf.nn.sparse_softmax_cross_entropy_with_logits(
                          logits=self.scores, labels=self.labels
                      )
                  )
                  # shape = (batch, sentence, ntags)
                  mask = tf.sequence_mask(self.sentence_lengths)
                  # apply mask
                  losses = tf.boolean_mask(tensor=losses, mask=mask)
  
                  self.loss = tf.reduce_mean(input_tensor=losses)
  
                  self.prediction = tf.math.argmax(
                      input=self.scores,
                      axis=-1,
                      name=predictions_op_name,
                  )
  
              tf.identity(self.loss, "loss")
  
imports: import math
  import random
  import sys
  import numpy
  import tensorflow
  from .sentence_grouper import SentenceGrouper
  from ..tf2contrib import *
label_desc: Use image node_id 8 for calling the NerModel obj's underlying member method code with example usage: obj.add_inference_layer(crf, predictions_op_name) without return types