class_docstr: Class for augmenting source and target sentences
  with inline tags (placeholders).
  
  It requires a prepared tab-delimited dictionary of source-target
  words and phrases. A dictionary can be created with
  tools such as fastalign and it should ideally contain enough long
  phrases (of 3-4 words or more) for more realistic applications
  of start and end tags. A dictionary with 20.000-30.000 entries
  should give sufficient number of matches.
class_docstr_tok: ["Class","for","augmenting","source","and","target","sentences","with","inline","tags","(","placeholders",")",".","It","requires","a","prepared","tab-delimited","dictionary","of","source-target","words","and","phrases",".","A","dictionary","can","be","created","with","tools","such","as","fastalign","and","it","should","ideally","contain","enough","long","phrases","(","of","3-4","words","or","more",")","for","more","realistic","applications","of","start","and","end","tags",".","A","dictionary","with","20.000-30.000","entries","should","give","sufficient","number","of","matches","."]
code: def _create_automaton(self):
      automaton = ahocorasick.Automaton()
      for entry in self.internal_dictionary:
          automaton.add_word(entry[0], (entry[0], entry[1]))
  
      automaton.make_automaton()
      return automaton
  
imports: from onmt.utils.logging import logger
  from onmt.transforms import register_transform
  from .transform import Transform
  import random
  import ahocorasick
  import string
label_desc: Use image node_id 3 for calling the InlineTagger obj's underlying member method code with example usage: obj._create_automaton() and returns: automaton