code: def naive_most_likely_sequence(self, logits, mask):
      # We iterate over all possible tag sequences and use self.score
      # to check the likelihood of each. The most likely sequence should be the
      # same as what we get from viterbi_tags.
      most_likely_tags = []
      best_scores = []
  
      for logit, mas in zip(logits, mask):
          mask_indices = mas.nonzero(as_tuple=False).squeeze()
          logit = torch.index_select(logit, 0, mask_indices)
          sequence_length = logit.shape[0]
          most_likely, most_likelihood = None, -float("inf")
          for tags in itertools.product(
              range(5), repeat=sequence_length
          ):
              score = self.score(logit.data, tags)
              if score > most_likelihood:
                  most_likely, most_likelihood = tags, score
          # Convert tuple to list; otherwise == complains.
          most_likely_tags.append(list(most_likely))
          best_scores.append(most_likelihood)
  
      return most_likely_tags, best_scores
  
imports: import itertools
  import math
  from pytest import approx, raises
  import torch
  from numpy.testing import assert_allclose
  from allennlp.modules import ConditionalRandomField
  from allennlp.modules.conditional_random_field import ConditionalRandomFieldWeightEmission, ConditionalRandomFieldWeightTrans, ConditionalRandomFieldWeightLannoy
  from allennlp.modules.conditional_random_field.conditional_random_field import allowed_transitions
  from allennlp.common.checks import ConfigurationError
  from allennlp.common.testing import AllenNlpTestCase
label_desc: Use image node_id 3 for calling the TestConditionalRandomField obj's underlying member method code with example usage: obj.naive_most_likely_sequence(logits, mask) and returns: most_likely_tags,  best_scores