code: def __init__(
      self,
      text_column,
      mask,
      tokenizer=None,
      add_bos_token=Config.add_bos_token,
      add_eos_token=Config.add_eos_token,
      use_eos_token_for_bos=Config.use_eos_token_for_bos,
      max_seq_len=Config.max_seq_len,
      vocab_config=None,
      vocab=None,
      vocab_file_delimiter=" ",
      is_input=Config.is_input,
  ):
      super().__init__(
          text_column,
          tokenizer,
          add_bos_token,
          add_eos_token,
          use_eos_token_for_bos,
          max_seq_len,
          vocab_config,
          vocab,
          vocab_file_delimiter,
          is_input,
      )
      self.mask = mask
      self.vocab_builder = MaskedVocabBuilder(self.vocab_file_delimiter)
      self.vocab_builder.use_bos = add_bos_token
      self.vocab_builder.use_eos = add_eos_token
  
file_path: pytext/pytext/data/masked_tensorizer.py
imports: import torch
  from pytext.config.component import ComponentType, create_component
  from pytext.data.masked_util import MaskedVocabBuilder, MaskingFunction, RandomizedMaskingFunction
  from pytext.data.tensorizers import TokenTensorizer
  from pytext.data.utils import pad, pad_and_tensorize
  from pytext.utils import cuda
label_desc: Use image node_id 2 to create a new MaskedTokenTensorizer object from inherited base classes: TokenTensorizer with example: obj = MaskedTokenTensorizer(text_column, mask, tokenizer, add_bos_token, add_eos_token, use_eos_token_for_bos, max_seq_len, vocab_config, vocab, vocab_file_delimiter, is_input)