code: def _tag_most_likely(examples):
      
      tokenized_examples = [_tokenize_by_character_class(example) for example in examples]
  
      # We currently need the tokenized_examples to all have the same length, so drop instances that have a length
      # that does not equal the mode of lengths within tokenized_examples
      token_lengths = [len(e) for e in tokenized_examples]
      token_lengths_mode = _mode(token_lengths)
      tokenized_examples = [example for example in tokenized_examples if len(example) == token_lengths_mode]
  
      # Now, we iterate through the tokens, assigning date elements based on their likelihood. In cases where
      # the assignments are unlikely for all date elements, assign filler.
      most_likely = []
      for token_index in range(0, token_lengths_mode):
          tokens = [token[token_index] for token in tokenized_examples]
          probabilities = _percent_match(DATE_ELEMENTS, tokens)
          max_prob = max(probabilities)
          if max_prob < 0.5:
              most_likely.append(Filler(_mode(tokens)))
          else:
              if probabilities.count(max_prob) == 1:
                  most_likely.append(DATE_ELEMENTS[probabilities.index(max_prob)])
              else:
                  choices = []
                  for index, prob in enumerate(probabilities):
                      if prob == max_prob:
                          choices.append(DATE_ELEMENTS[index])
                  most_likely.append(_most_restrictive(choices))
  
      return most_likely
docs: Return a list of date elements by choosing the most likely element for a token within examples (context-free).