class_docstr: A ``WordStemmer`` lemmatizes words.  This means that we map words to their root form, so that,
  e.g., "have", "has", and "had" all have the same internal representation.
  
  You should think carefully about whether and how much stemming you want in your model.  Kind of
  the whole point of using word embeddings is so that you don't have to do this, but in a highly
  inflected language, or in a low-data setting, you might need it anyway.  The default
  ``WordStemmer`` does nothing, just returning the work token as-is.
docstr: Returns a new ``Token`` with ``word.text`` replaced by a stemmed word.
docstr_tok: ["Returns","a","new","``","Token","``","with","``","word.text","``","replaced","by","a","stemmed","word","."]
code: def stem_word(self, word):
      """
      Returns a new ``Token`` with ``word.text`` replaced by a stemmed word.
      """
      raise NotImplementedError
  
file_path: magnitude/pymagnitude/third_party/allennlp/data/tokenizers/word_stemmer.py
imports: from __future__ import absolute_import
  from nltk.stem import PorterStemmer
  from allennlp.common import Registrable
  from allennlp.data.tokenizers.token import Token
label_desc: Use image node_id 1 for calling the WordStemmer obj's underlying member method code with example usage: obj.stem_word(word) without return types