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","."]
code: def __repr__(self):
      if self.source:
          return f"<{type(self).__name__}: {self.source}>"
      else:
          return (
              f"<{type(self).__name__}: {self.repr.repr(self.train)}>"
          )
  
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 2 for calling the UnigramLemmatizer obj's underlying member method code with example usage: obj.__repr__() and returns: str, type, str, self, str, str, type, str, self, str