class_docstr: Brill's transformational rule-based tagger.  Brill taggers use an
  initial tagger (such as ``tag.DefaultTagger``) to assign an initial
  tag sequence to a text; and then apply an ordered list of
  transformational rules to correct the tags of individual tokens.
  These transformation rules are specified by the ``TagRule``
  interface.
  
  Brill taggers can be created directly, from an initial tagger and
  a list of transformational rules; but more often, Brill taggers
  are created by learning rules from a training corpus, using one
  of the TaggerTrainers available.
docstr: Return a named statistic collected during training, or a dictionary of all
  available statistics if no name given
  
  :param statistic: name of statistic
  :type statistic: str
  :return: some statistic collected during training of this tagger
  :rtype: any (but usually a number)
docstr_tok: ["Return","a","named","statistic","collected","during","training",",","or","a","dictionary","of","all","available","statistics","if","no","name","given",":","param","statistic",":","name","of","statistic",":","type","statistic",":","str",":","return",":","some","statistic","collected","during","training","of","this","tagger",":","rtype",":","any","(","but","usually","a","number",")"]
code: def train_stats(self, statistic=None):
      """
      Return a named statistic collected during training, or a dictionary of all
      available statistics if no name given
  
      :param statistic: name of statistic
      :type statistic: str
      :return: some statistic collected during training of this tagger
      :rtype: any (but usually a number)
      """
      if statistic is None:
          return self._training_stats
      else:
          return self._training_stats.get(statistic)
  
imports: from collections import Counter, defaultdict
  from nltk import jsontags
  from nltk.tag import TaggerI
  from nltk.tbl import Feature, Template
label_desc: Use image node_id 5 for calling the BrillTagger obj's underlying member method code with example usage: obj.train_stats(statistic) and returns: self, self