class_docstr: Implements the default configuration for noising in UnsupervisedMT
  (github.com/facebookresearch/UnsupervisedMT)
class_docstr_tok: ["Implements","the","default","configuration","for","noising","in","UnsupervisedMT","(","github.com\/facebookresearch\/UnsupervisedMT",")"]
code: def noising(self, x, lengths):
      # 1. Word Shuffle
      noisy_src_tokens, noisy_src_lengths = self.word_shuffle.noising(
          x=x,
          lengths=lengths,
          max_shuffle_distance=self.max_word_shuffle_distance,
      )
      # 2. Word Dropout
      noisy_src_tokens, noisy_src_lengths = self.word_dropout.noising(
          x=noisy_src_tokens,
          lengths=noisy_src_lengths,
          dropout_prob=self.word_dropout_prob,
      )
      # 3. Word Blanking
      noisy_src_tokens, noisy_src_lengths = self.word_dropout.noising(
          x=noisy_src_tokens,
          lengths=noisy_src_lengths,
          dropout_prob=self.word_blanking_prob,
          blank_idx=self.dictionary.unk(),
      )
  
      return noisy_src_tokens
  
imports: import numpy
  import torch
  from fairseq.data import data_utils
label_desc: Use image node_id 2 for calling the UnsupervisedMTNoising obj's underlying member method code with example usage: obj.noising(x, lengths) and returns: noisy_src_tokens