class_docstr: Lemmatizer that returns a given token as its lemma. Like DefaultLemmatizer,
  useful as the final tagger in a chain, e.g. to assign a possible form to
  all remaining unlemmatized tokens, increasing the chance of a successful
  match.
  
  >>> identity_lemmatizer = IdentityLemmatizer()
  >>> list(identity_lemmatizer.lemmatize('arma virumque cano'.split()))
  [('arma', 'arma'), ('virumque', 'virumque'), ('cano', 'cano')]
class_docstr_tok: ["Lemmatizer","that","returns","a","given","token","as","its","lemma",".","Like","DefaultLemmatizer",",","useful","as","the","final","tagger","in","a","chain",",","e.g",".","to","assign","a","possible","form","to","all","remaining","unlemmatized","tokens",",","increasing","the","chance","of","a","successful","match",".",">",">",">","identity_lemmatizer","=","IdentityLemmatizer","(",")",">",">",">","list","(","identity_lemmatizer.lemmatize","(","'arma","virumque","cano'.split","(",")",")",")","[","(","'arma","'",",","'arma","'",")",",","(","'virumque","'",",","'virumque","'",")",",","(","'cano","'",",","'cano","'",")","]"]
code: def __init__(
      self,
      backoff: Optional[SequentialBackoffTagger] = None,
      verbose: bool = False,
  ):
      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 IdentityLemmatizer object from inherited base classes: SequentialBackoffLemmatizer with example: obj = IdentityLemmatizer(backoff, verbose)