class_docstr: Tokenize according to rules specific to a given language.
code: def compute_indices(text: str, tokens):
      indices = []
      for i, token in enumerate(tokens):
          if 1 <= i:
              current_index = indices[-1] + len(tokens[i - 1])
              if token == "-ne":
                  indices.append(
                      current_index
                      + text[current_index:].find(token[1:])
                  )
              else:
                  indices.append(
                      current_index + text[current_index:].find(token)
                  )
          else:
              indices.append(text.find(token))
      return indices
  
file_path: cltk/src/cltk/tokenizers/lat/lat.py
imports: import re
  from typing import List, Tuple
  from nltk.tokenize.punkt import PunktLanguageVars, PunktParameters
  from cltk.sentence.lat import LatinPunktSentenceTokenizer
  from cltk.tokenizers.lat.params import ABBREVIATIONS, latin_exceptions
  from cltk.tokenizers.lat.params import latin_replacements
  from cltk.tokenizers.word import WordTokenizer
label_desc: Use image node_id 3 for calling the LatinWordTokenizer obj's underlying member method code with example usage: obj.compute_indices(text, tokens) and returns: indices