class_docstr: A language model on English Wikipedia.
  
  References and internal links are removed from the raw XML.
  
  Special pages (non-articles) are dropped.
  
  This more closely resembles plain text, though there are still some xml
  elements, like tables.
  
  Each article is prefixed by a line containing the title and length in
  characters - e.g.
  title: "Price of Tea in China" length: 12345
  During inference time, you can forward generate starting with such a header
  in order to obtain a randomly generated article with a given title and
  (approximate) length.
  
  Result is chopped arbitrarily into sequences of length 1024 tokens,
  without regard to article boundaries.
class_docstr_tok: ["A","language","model","on","English","Wikipedia",".","References","and","internal","links","are","removed","from","the","raw","XML",".","Special","pages","(","non-articles",")","are","dropped",".","This","more","closely","resembles","plain","text",",","though","there","are","still","some","xml","elements",",","like","tables",".","Each","article","is","prefixed","by","a","line","containing","the","title","and","length","in","characters","-","e.g",".","title",":","``","Price","of","Tea","in","China","''","length",":","12345","During","inference","time",",","you","can","forward","generate","starting","with","such","a","header","in","order","to","obtain","a","randomly","generated","article","with","a","given","title","and","(","approximate",")","length",".","Result","is","chopped","arbitrarily","into","sequences","of","length","1024","tokens",",","without","regard","to","article","boundaries","."]
docstr: Number of characters of training data to use for generating vocab.
docstr_tok: ["Number","of","characters","of","training","data","to","use","for","generating","vocab","."]
code: def max_chars_for_vocab(self):
      """Number of characters of training data to use for generating vocab."""
      # magic number for backwards compatibility
      return 21240483
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import subprocess
  import numpy
  from tensor2tensor.data_generators import generator_utils
  from tensor2tensor.data_generators import problem
  from tensor2tensor.data_generators import text_encoder
  from tensor2tensor.data_generators import text_problems
  from tensor2tensor.utils import registry
  import tensorflow.compat.v1
label_desc: Use image node_id 2 for calling the LanguagemodelWikiNorefV8kL1k obj's underlying member method code with example usage: obj.max_chars_for_vocab() and returns: int