class_docstr: An abstract class that wraps around the nltk.classify module.
  
  Expects that descendant classes include a class variable ``nltk_class``
  which is the class in the nltk.classify module to be wrapped.
  
  Example: ::
  
      class MyClassifier(NLTKClassifier):
          nltk_class = nltk.classify.svm.SvmClassifier
class_docstr_tok: ["An","abstract","class","that","wraps","around","the","nltk.classify","module",".","Expects","that","descendant","classes","include","a","class","variable","``","nltk_class","``","which","is","the","class","in","the","nltk.classify","module","to","be","wrapped",".","Example",":",":",":","class","MyClassifier","(","NLTKClassifier",")",":","nltk_class","=","nltk.classify.svm.SvmClassifier"]
docstr: Update the classifier with new training data and re-trains the
  classifier.
  
  :param new_data: New data as a list of tuples of the form
      ``(text, label)``.
docstr_tok: ["Update","the","classifier","with","new","training","data","and","re-trains","the","classifier",".",":","param","new_data",":","New","data","as","a","list","of","tuples","of","the","form","``","(","text",",","label",")","``","."]
code: def update(self, new_data, *args, **kwargs):
      """Update the classifier with new training data and re-trains the
      classifier.
  
      :param new_data: New data as a list of tuples of the form
          ``(text, label)``.
      """
      self.train_set += new_data
      self._word_set.update(_get_words_from_dataset(new_data))
      self.train_features = [
          (self.extract_features(d), c) for d, c in self.train_set
      ]
      try:
          self.classifier = self.nltk_class.train(
              self.train_features, *args, **kwargs
          )
      except AttributeError:  # Descendant has not defined nltk_class
          raise ValueError(
              "NLTKClassifier must have a nltk_class"
              " variable that is not None."
          )
      return True
  
imports: from __future__ import absolute_import
  from itertools import chain
  import nltk
  from textblob.compat import basestring
  from textblob.decorators import cached_property
  from textblob.exceptions import FormatError
  from textblob.tokenizers import word_tokenize
  from textblob.utils import strip_punc, is_filelike
  import textblob.formats
label_desc: Use image node_id 8 for calling the NLTKClassifier obj's underlying member method code with example usage: obj.update(new_data) and returns: True