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 __repr__(self):
      return f"<{type(self).__name__}>"
  
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 IdentityLemmatizer obj's underlying member method code with example usage: obj.__repr__() and returns: str, type, str