class_docstr: Standalone version of 'model' function found in UnigramTagger; by
  defining as its own class, it is clearer that this lemmatizer is
  based on dictionary lookup and does not use training data.
class_docstr_tok: ["Standalone","version","of","'model","'","function","found","in","UnigramTagger",";","by","defining","as","its","own","class",",","it","is","clearer","that","this","lemmatizer","is","based","on","dictionary","lookup","and","does","not","use","training","data","."]
docstr: Setup for DictLemmatizer().
  :type lemmas: dict
  :param lemmas: Dictionary with form {TOKEN: LEMMA} to be used
      as foor 'lookup'-style lemmatization
  :param backoff: Next lemmatizer in backoff chain.
docstr_tok: ["Setup","for","DictLemmatizer","(",")",".",":","type","lemmas",":","dict",":","param","lemmas",":","Dictionary","with","form","{","TOKEN",":","LEMMA","}","to","be","used","as","foor","'lookup'-style","lemmatization",":","param","backoff",":","Next","lemmatizer","in","backoff","chain","."]
code: def __init__(
      self,
      lemmas: Dict[str, str],
      backoff: Optional[SequentialBackoffTagger] = None,
      source: str = None,
      verbose: bool = False,
  ):
      """
      Setup for DictLemmatizer().
      :type lemmas: dict
      :param lemmas: Dictionary with form {TOKEN: LEMMA} to be used
          as foor 'lookup'-style lemmatization
      :param backoff: Next lemmatizer in backoff chain.
      """
      SequentialBackoffLemmatizer.__init__(
          self, backoff, verbose=verbose
      )
      self.lemmas = lemmas
      self.source = source
  
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 DictLemmatizer object from inherited base classes: SequentialBackoffLemmatizer with example: obj = DictLemmatizer(lemmas, backoff, source, verbose)