code: def compute_log_likelihood(
      logits, tags, transitions, boundary_values
  ):
      # Now compute the log-likelihood manually
      manual_log_likelihood = 0.0
  
      # For each instance, manually compute the numerator
      # (which is just the score for the logits and actual tags)
      # and the denominator
      # (which is the log-sum-exp of the scores
      # for the logits across all possible tags)
      for logits_i, tags_i in zip(logits, tags):
          numerator = score_logits(
              logits_i, tags_i, transitions, boundary_values
          )
          all_scores = [
              score_logits(
                  logits_i, tags_j, transitions, boundary_values
              )
              for tags_j in itertools.product(range(5), repeat=3)
          ]
          denominator = math.log(
              sum(math.exp(score) for score in all_scores)
          )
          # And include them in the manual calculation.
          manual_log_likelihood += numerator - denominator
  
      return manual_log_likelihood
  
imports: import itertools
  import os
  import math
  import tempfile
  import pytest
  import numpy
  import tensorflow
  from tensorflow_addons.layers.crf import CRF
  from tensorflow_addons.text.crf import crf_log_likelihood
  from tensorflow_addons.utils import test_utils
label_desc: Use image node_id 14 for calling a global function with example usage: compute_log_likelihood(logits, tags, transitions, boundary_values) and returns: manual_log_likelihood