class_docstr: A Naive Bayes classifier.  Naive Bayes classifiers are
  paramaterized by two probability distributions:
  
    - P(label) gives the probability that an input will receive each
      label, given no information about the input's features.
  
    - P(fname=fval|label) gives the probability that a given feature
      (fname) will receive a given value (fval), given that the
      label (label).
  
  If the classifier encounters an input with a feature that has
  never been seen with any label, then rather than assigning a
  probability of 0 to all labels, it will ignore that feature.
  
  The feature value 'None' is reserved for unseen feature values;
  you generally should not use 'None' as a feature value for one of
  your own features.
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code: def labels(self):
      return self._labels
  
file_path: nltk/nltk/classify/naivebayes.py
imports: from collections import defaultdict
  from nltk.classify.api import ClassifierI
  from nltk.probability import DictionaryProbDist, ELEProbDist, FreqDist, sum_logs
label_desc: Use image node_id 2 for calling the NaiveBayesClassifier obj's underlying member method code with example usage: obj.labels() and returns: self