class_docstr: Return a stemmed Arabic word after removing affixes. This an improved
  version of the previous algorithm, which reduces under-stemming errors.
  Typically used in Arabic search engine, information retrieval and NLP.
  
      >>> from nltk.stem import arlstem2
      >>> stemmer = ARLSTem2()
      >>> word = stemmer.stem('يعمل')
      >>> print(word)
      عمل
  
  :param token: The input Arabic word (unicode) to be stemmed
  :type token: unicode
  :return: A unicode Arabic word
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docstr: stem the present tense suffixes
docstr_tok: ["stem","the","present","tense","suffixes"]
code: def verb_t3(self, token):
      """
      stem the present tense suffixes
      """
      if len(token) > 5:
          for su3 in self.verb_suf3:
              if token.endswith(su3):
                  return token[:-3]
      if len(token) > 4:
          for su2 in self.verb_suf2:
              if token.endswith(su2):
                  return token[:-2]
      if len(token) > 3:
          for su1 in self.verb_suf1:
              if token.endswith(su1):
                  return token[:-1]
  
imports: import re
  from nltk.stem.api import StemmerI
label_desc: Use image node_id 13 for calling the ARLSTem2 obj's underlying member method code with example usage: obj.verb_t3(token) and returns: token, token, token