class_docstr: A wrapper around a sequence of simple (string) tokens, which is
  intended to support initial exploration of texts (via the
  interactive console).  Its methods perform a variety of analyses
  on the text's contexts (e.g., counting, concordancing, collocation
  discovery), and display the results.  If you wish to write a
  program which makes use of these analyses, then you should bypass
  the ``Text`` class, and use the appropriate analysis function or
  class directly instead.
  
  A ``Text`` is typically initialized from a given document or
  corpus.  E.g.:
  
  >>> import nltk.corpus
  >>> from nltk.text import Text
  >>> moby = Text(nltk.corpus.gutenberg.words('melville-moby_dick.txt'))
docstr: :seealso: nltk.prob.FreqDist
docstr_tok: [":","seealso",":","nltk.prob.FreqDist"]
code: def vocab(self):
      """
      :seealso: nltk.prob.FreqDist
      """
      if "_vocab" not in self.__dict__:
          # print("Building vocabulary index...")
          self._vocab = FreqDist(self)
      return self._vocab
  
imports: import re
  import sys
  import unicodedata
  from collections import Counter, defaultdict, namedtuple
  from functools import reduce
  from math import log
  from nltk.collocations import BigramCollocationFinder
  from nltk.lm import MLE
  from nltk.lm.preprocessing import padded_everygram_pipeline
  from nltk.metrics import BigramAssocMeasures, f_measure
  from nltk.probability import ConditionalFreqDist
  from nltk.probability import FreqDist
  from nltk.tokenize import sent_tokenize
  from nltk.util import LazyConcatenation, cut_string, tokenwrap
label_desc: Use image node_id 17 for calling the Text obj's underlying member method code with example usage: obj.vocab() and returns: self