class_docstr: Language modeling on English wikipedia.
  
  "targets" is a sequence of sequence_length tokens - a fragment of an article.
  "inputs" is a copy of "targets", but with a random scramble_fraction of the
    tokens randomly permuted.
  
  This dataset is intended to test parallel (non-autoregressive) prediction
  of the target sequence given the input sequence.
class_docstr_tok: ["Language","modeling","on","English","wikipedia",".","``","targets","''","is","a","sequence","of","sequence_length","tokens","-","a","fragment","of","an","article",".","``","inputs","''","is","a","copy","of","``","targets","''",",","but","with","a","random","scramble_fraction","of","the","tokens","randomly","permuted",".","This","dataset","is","intended","to","test","parallel","(","non-autoregressive",")","prediction","of","the","target","sequence","given","the","input","sequence","."]
code: def scramble_fraction(self):
      raise NotImplementedError()
  
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 LanguagemodelWikiScramble obj's underlying member method code with example usage: obj.scramble_fraction() without return types