class_docstr: This loss expects as input a batch consisting of damaged sentences and the corresponding original ones.
  The data generation process has already been implemented in readers/DenoisingAutoEncoderReader.py
  During training, the decoder reconstructs the original sentences from the encoded sentence embeddings.
  Here the argument 'decoder_name_or_path' indicates the pretrained model (supported by Huggingface) to be used as the decoder.
  Since decoding process is included, here the decoder should have a class called XXXLMHead (in the context of Huggingface's Transformers).
  Flag 'tie_encoder_decoder' indicates whether to tie the trainable parameters of encoder and decoder,
  which is shown beneficial to model performance while limiting the amount of required memory.
  Only when the encoder and decoder are from the same architecture, can the flag 'tie_encoder_decoder' works.
  For more information, please refer to the TSDAE paper.
code: def retokenize(self, sentence_features):
      input_ids = sentence_features["input_ids"]
      device = input_ids.device
      sentences_decoded = self.tokenizer_encoder.batch_decode(
          input_ids,
          skip_special_tokens=True,
          clean_up_tokenization_spaces=True,
      )
      retokenized = self.tokenizer_decoder(
          sentences_decoded,
          padding=True,
          truncation="longest_first",
          return_tensors="pt",
          max_length=None,
      ).to(device)
      return retokenized
  
file_path: sentence-transformers/sentence_transformers/losses/DenoisingAutoEncoderLoss.py
imports: from torch import nn, Tensor
  from typing import Iterable, Dict
  from sentence_transformers import SentenceTransformer
  from transformers import AutoConfig, AutoTokenizer, AutoModelForCausalLM, PreTrainedModel
  import logging
label_desc: Use image node_id 2 for calling the DenoisingAutoEncoderLoss obj's underlying member method code with example usage: obj.retokenize(sentence_features) and returns: retokenized