code: def warm_up(self, vocabs):
      super().warm_up(vocabs)
  
      subword_type = self.opts.src_subword_type
      if self.opts.mask_length == "subword":
          if subword_type == "none":
              raise ValueError(
                  f"src_subword_type={subword_type} incompatible with "
                  f"mask_length={self.opts.mask_length}!"
              )
      is_joiner = (
          (subword_type == "bpe") if subword_type != "none" else None
      )
      self.bart_noise = BARTNoising(
          self.vocabs["src"].ids_to_tokens,
          mask_tok=DefaultTokens.MASK,
          mask_ratio=self.opts.mask_ratio,
          insert_ratio=self.opts.insert_ratio,
          permute_sent_ratio=self.opts.permute_sent_ratio,
          poisson_lambda=self.opts.poisson_lambda,
          replace_length=self.opts.replace_length,
          rotate_ratio=self.opts.rotate_ratio,
          mask_length=self.opts.mask_length,
          random_ratio=self.opts.random_ratio,
          is_joiner=is_joiner,
      )
  
imports: import math
  import numpy
  import torch
  from typing import Sequence, Callable
  from onmt.constants import DefaultTokens, SubwordMarker
  from onmt.transforms import register_transform
  from .transform import Transform
label_desc: Use image node_id 5 for calling the BARTNoiseTransform obj's underlying member method code with example usage: obj.warm_up(vocabs) without return types