class_docstr: Class for storing lemmas, stems and evaluation metrics.
docstr: Count (UI, OI) when stemming is done by truncating words at 'cutlength'.
  
  :param words: Words used for the analysis
  :param cutlength: Words are stemmed by cutting them at this length
  :type words: set(str) or list(str)
  :type cutlength: int
  :return: Understemming and overstemming indexes
  :rtype: tuple(int, int)
docstr_tok: ["Count","(","UI",",","OI",")","when","stemming","is","done","by","truncating","words","at","'cutlength","'",".",":","param","words",":","Words","used","for","the","analysis",":","param","cutlength",":","Words","are","stemmed","by","cutting","them","at","this","length",":","type","words",":","set","(","str",")","or","list","(","str",")",":","type","cutlength",":","int",":","return",":","Understemming","and","overstemming","indexes",":","rtype",":","tuple","(","int",",","int",")"]
code: def _get_truncation_indexes(self, words, cutlength):
      """Count (UI, OI) when stemming is done by truncating words at \'cutlength\'.
  
      :param words: Words used for the analysis
      :param cutlength: Words are stemmed by cutting them at this length
      :type words: set(str) or list(str)
      :type cutlength: int
      :return: Understemming and overstemming indexes
      :rtype: tuple(int, int)
      """
  
      truncated = _truncate(words, cutlength)
      gumt, gdmt, gwmt, gdnt = _calculate(self.lemmas, truncated)
      ui, oi = _indexes(gumt, gdmt, gwmt, gdnt)[:2]
      return (ui, oi)
  
file_path: nltk/nltk/metrics/paice.py
imports: from math import sqrt
label_desc: Use image node_id 3 for calling the Paice obj's underlying member method code with example usage: obj._get_truncation_indexes(words, cutlength) and returns: ui,  oi