class_docstr: An NLTK interface to the VerbNet verb lexicon.
  
  From the VerbNet site: "VerbNet (VN) (Kipper-Schuler 2006) is the largest
  on-line verb lexicon currently available for English. It is a hierarchical
  domain-independent, broad-coverage verb lexicon with mappings to other
  lexical resources such as WordNet (Miller, 1990; Fellbaum, 1998), XTAG
  (XTAG Research Group, 2001), and FrameNet (Baker et al., 1998)."
  
  For details about VerbNet see:
  https://verbs.colorado.edu/~mpalmer/projects/verbnet.html
class_docstr_tok: ["An","NLTK","interface","to","the","VerbNet","verb","lexicon",".","From","the","VerbNet","site",":","``","VerbNet","(","VN",")","(","Kipper-Schuler","2006",")","is","the","largest","on-line","verb","lexicon","currently","available","for","English",".","It","is","a","hierarchical","domain-independent",",","broad-coverage","verb","lexicon","with","mappings","to","other","lexical","resources","such","as","WordNet","(","Miller",",","1990",";","Fellbaum",",","1998",")",",","XTAG","(","XTAG","Research","Group",",","2001",")",",","and","FrameNet","(","Baker","et","al.",",","1998",")",".","''","For","details","about","VerbNet","see",":","https",":","\/\/verbs.colorado.edu\/~mpalmer\/projects\/verbnet.html"]
docstr: Initialize the indexes ``_lemma_to_class``,
  ``_wordnet_to_class``, and ``_class_to_fileid`` by scanning
  through the corpus fileids.  This is fast if ElementTree
  uses the C implementation (<0.1 secs), but quite slow (>10 secs)
  if only the python implementation is available.
docstr_tok: ["Initialize","the","indexes","``","_lemma_to_class","``",",","``","_wordnet_to_class","``",",","and","``","_class_to_fileid","``","by","scanning","through","the","corpus","fileids",".","This","is","fast","if","ElementTree","uses","the","C","implementation","(","<","0.1","secs",")",",","but","quite","slow","(",">","10","secs",")","if","only","the","python","implementation","is","available","."]
code: def _index(self):
      """
      Initialize the indexes ``_lemma_to_class``,
      ``_wordnet_to_class``, and ``_class_to_fileid`` by scanning
      through the corpus fileids.  This is fast if ElementTree
      uses the C implementation (<0.1 secs), but quite slow (>10 secs)
      if only the python implementation is available.
      """
      for fileid in self._fileids:
          self._index_helper(self.xml(fileid), fileid)
  
imports: import re
  import textwrap
  from collections import defaultdict
  from nltk.corpus.reader.xmldocs import XMLCorpusReader
label_desc: Use image node_id 10 for calling the VerbnetCorpusReader obj's underlying member method code with example usage: obj._index() without return types