class_docstr: Standalone version of 'train' function found in UnigramTagger; by
  defining as its own class, it is clearer that this lemmatizer is
  based on training data and not on dictionary.
class_docstr_tok: ["Standalone","version","of","'train","'","function","found","in","UnigramTagger",";","by","defining","as","its","own","class",",","it","is","clearer","that","this","lemmatizer","is","based","on","training","data","and","not","on","dictionary","."]
docstr: Setup for UnigramLemmatizer()
docstr_tok: ["Setup","for","UnigramLemmatizer","(",")"]
code: def __init__(
      self,
      train=None,
      model=None,
      backoff: Optional[SequentialBackoffTagger] = None,
      source: str = None,
      cutoff=0,
      verbose: bool = False,
  ):
      """
      Setup for UnigramLemmatizer()
      """
      SequentialBackoffLemmatizer.__init__(
          self, backoff=None, verbose=verbose
      )
      UnigramTagger.__init__(self, train, model, backoff, cutoff)
      self.train = train
      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 UnigramLemmatizer object from inherited base classes: SequentialBackoffLemmatizer, UnigramTagger with example: obj = UnigramLemmatizer(train, model, backoff, source, cutoff, verbose)