class_docstr: Data collator used denoising language modeling task in BART.
  The implementation is based on
  https://github.com/pytorch/fairseq/blob/1bba712622b8ae4efb3eb793a8a40da386fe11d0/fairseq/data/denoising_dataset.py.
  
  BART Original hyperparams: https://github.com/facebookresearch/fairseq/issues/1899#issuecomment-1069429320
code: def __init__(self, config, tokenizer, set):
      self.config = config
      self.tokenizer = tokenizer
      self.set = set
      self.mask_ratio = config["mask_ratio"] or 0.3
      self.poisson_lambda = config["poisson_lambda"] or 3.5
      self.permutate_sentence_ratio = (
          config["permutate_sentence_ratio"] or 1.0
      )
      self.poisson_distribution = torch.distributions.Poisson(
          rate=self.poisson_lambda
      )
  
file_path: TextBox/textbox/data/denoising_dataset.py
imports: import math
  import torch
  import random
  from typing import List, Dict
  from nltk import sent_tokenize
  from textbox.data.misc import _pad_sequence
label_desc: Use image node_id 1 to create a new DenoisingCollate object  with example: obj = DenoisingCollate(config, tokenizer, set)