class_docstr: The DenoisingAutoEncoderDataset returns InputExamples in the format: texts=[noise_fn(sentence), sentence]
  It is used in combination with the DenoisingAutoEncoderLoss: Here, a decoder tries to re-construct the
  sentence without noise.
  
  :param sentences: A list of sentences
  :param noise_fn: A noise function: Given a string, it returns a string with noise, e.g. deleted words
code: def __len__(self):
      return len(self.sentences)
  
file_path: sentence-transformers/sentence_transformers/datasets/DenoisingAutoEncoderDataset.py
imports: from torch.utils.data import Dataset
  from typing import List
  from ..readers.InputExample import InputExample
  import numpy
  import nltk
  from nltk.tokenize.treebank import TreebankWordDetokenizer
label_desc: Use image node_id 3 for calling the DenoisingAutoEncoderDataset obj's underlying member method code with example usage: obj.__len__() and returns: len