class_docstr: Lemmatizer that assigns the same lemma to every token. Useful as the final
  tagger in chain, e.g. to assign 'UNK' to all remaining unlemmatized tokens.
  :type lemma: str
  :param lemma: Lemma to assign to each token
  
  >>> default_lemmatizer = DefaultLemmatizer('UNK')
  >>> list(default_lemmatizer.lemmatize('arma virumque cano'.split()))
  [('arma', 'UNK'), ('virumque', 'UNK'), ('cano', 'UNK')]
class_docstr_tok: ["Lemmatizer","that","assigns","the","same","lemma","to","every","token",".","Useful","as","the","final","tagger","in","chain",",","e.g",".","to","assign","'UNK","'","to","all","remaining","unlemmatized","tokens",".",":","type","lemma",":","str",":","param","lemma",":","Lemma","to","assign","to","each","token",">",">",">","default_lemmatizer","=","DefaultLemmatizer","(","'UNK","'",")",">",">",">","list","(","default_lemmatizer.lemmatize","(","'arma","virumque","cano'.split","(",")",")",")","[","(","'arma","'",",","'UNK","'",")",",","(","'virumque","'",",","'UNK","'",")",",","(","'cano","'",",","'UNK","'",")","]"]
code: def __init__(
      self,
      lemma: str = None,
      backoff: Optional[SequentialBackoffTagger] = None,
      verbose: bool = False,
  ):
      self.lemma = lemma
      SequentialBackoffLemmatizer.__init__(
          self, backoff=None, verbose=verbose
      )
  
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 1 to create a new DefaultLemmatizer object from inherited base classes: SequentialBackoffLemmatizer with example: obj = DefaultLemmatizer(lemma, backoff, verbose)