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 __init__(
      self,
      tags_dictionary_path,
      max_tags,
      paired_start_tag,
      paired_end_tag,
      isolated_tag,
      src_delimiter,
      tag_corpus_ratio=0.1,
  ):
      self.max_tags = max_tags
      self.tag_corpus_ratio = tag_corpus_ratio
      self.src_delimiter = src_delimiter
      self.internal_dictionary = self._create_internal_dictionary(
          tags_dictionary_path
      )
      self.paired_stag_prefix, self.paired_stag_suffix = map(
          str, paired_start_tag.split("#")
      )
      self.paired_etag_prefix, self.paired_etag_suffix = map(
          str, paired_end_tag.split("#")
      )
      self.isolated_tag_prefix, self.isolated_tag_suffix = map(
          str, isolated_tag.split("#")
      )
  
      self.automaton = self._create_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 1 to create a new InlineTagger object from inherited base classes: object with example: obj = InlineTagger(tags_dictionary_path, max_tags, paired_start_tag, paired_end_tag, isolated_tag, src_delimiter, tag_corpus_ratio)