class_docstr: Abstract base class for lemmatizers created as a subclass of
  NLTK's SequentialBackoffTagger. Lemmatizers in this class "[tag]
  words sequentially, left to right. Tagging of individual words is
  performed by the ``choose_tag()`` method, which should be defined
  by subclasses.  If a tagger is unable to determine a tag for the
  specified token, then its backoff tagger is consulted."
  
  See: https://www.nltk.org/_modules/nltk/tag/sequential.html#SequentialBackoffTagger
  
  :type _taggers: list
  :ivar _taggers: A list of all the taggers in the backoff chain,
      inc. self.
  :type _repr: Repr object
  :ivar _repr: An instance of Repr() from reprlib to handle list
      and dict length in subclass __repr__'s
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docstr: Determine an appropriate tag for the specified token, and
  return that tag.  If this tagger is unable to determine a tag
  for the specified token, then its backoff tagger is consulted.
  
  :rtype: tuple
  :type tokens: list
  :param tokens: The list of words that are being tagged.
  :type index: int
  :param index: The index of the word whose tag should be
      returned.
  :type history: list(str)
  :param history: A list of the tags for all words before index.
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code: def tag_one(
      self, tokens: List[str], index: int, history: List[str]
  ) -> Tuple:
      """Determine an appropriate tag for the specified token, and
      return that tag.  If this tagger is unable to determine a tag
      for the specified token, then its backoff tagger is consulted.
  
      :rtype: tuple
      :type tokens: list
      :param tokens: The list of words that are being tagged.
      :type index: int
      :param index: The index of the word whose tag should be
          returned.
      :type history: list(str)
      :param history: A list of the tags for all words before index.
      """
      lemma = None
      tagger = None
      for _tagger in self._taggers:
          lemma = _tagger.choose_tag(tokens, index, history)
          tagger = _tagger
          if lemma is not None and lemma != "":
              break
      return lemma, tagger
  
imports: import re
  import reprlib
  from typing import Dict, List, Optional, Tuple
  from nltk.tag.sequential import RegexpTagger, SequentialBackoffTagger, TaggerI, UnigramTagger
label_desc: Use image node_id 3 for calling the SequentialBackoffLemmatizer obj's underlying member method code with example usage: obj.tag_one(tokens, index, history) and returns: lemma,  tagger