code: def test_viterbi_tags_top_k(self):
      mask = torch.tensor([[True, True, True], [True, True, False]])
  
      best_paths = self.crf.viterbi_tags(self.logits, mask, top_k=2)
  
      # Ensure the top path matches not passing top_k
      top_path_and_score = [
          top_k_paths[0] for top_k_paths in best_paths
      ]
      assert top_path_and_score == self.crf.viterbi_tags(
          self.logits, mask
      )
  
      next_path_and_score = [
          top_k_paths[1] for top_k_paths in best_paths
      ]
      next_viterbi_tags = [x for x, _ in next_path_and_score]
  
      # Check that the next best viterbi tags are what I think they should be.
      assert next_viterbi_tags == [[4, 2, 3], [3, 2]]
  
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 8 for calling the TestConditionalRandomField obj's underlying member method code with example usage: obj.test_viterbi_tags_top_k() without return types