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: normalize the word by removing diacritics, replace hamzated Alif
  with Alif bare, replace AlifMaqsura with Yaa and remove Waaw at the
  beginning.
docstr_tok: ["normalize","the","word","by","removing","diacritics",",","replace","hamzated","Alif","with","Alif","bare",",","replace","AlifMaqsura","with","Yaa","and","remove","Waaw","at","the","beginning","."]
code: def norm(self, token):
      """
      normalize the word by removing diacritics, replace hamzated Alif
      with Alif bare, replace AlifMaqsura with Yaa and remove Waaw at the
      beginning.
      """
      # strip Arabic diacritics
      token = self.re_diacritics.sub("", token)
      # replace Hamzated Alif with Alif bare
      token = self.re_hamzated_alif.sub("\u0627", token)
      # replace alifMaqsura with Yaa
      token = self.re_alifMaqsura.sub("\u064A", token)
      # strip the Waaw from the word beginning if the remaining is
      # tri-literal at least
      if token.startswith("\u0648") and len(token) > 3:
          token = token[1:]
      return token
  
imports: import re
  from nltk.stem.api import StemmerI
label_desc: Use image node_id 4 for calling the ARLSTem2 obj's underlying member method code with example usage: obj.norm(token) and returns: token