code: def __init__(
      self,
      *,
      padding_token: int,
      mask_token: int,
      no_mask_tokens: List[int],
      n_tokens: int,
      masking_prob: float = 0.15,
      randomize_prob: float = 0.1,
      no_change_prob: float = 0.1,
  ):
      """
      * `padding_token` is the padding token `[PAD]`.
        We will use this to mark the labels that shouldn't be used for loss calculation.
      * `mask_token` is the masking token `[MASK]`.
      * `no_mask_tokens` is a list of tokens that should not be masked.
      This is useful if we are training the MLM with another task like classification at the same time,
      and we have tokens such as `[CLS]` that shouldn't be masked.
      * `n_tokens` total number of tokens (used for generating random tokens)
      * `masking_prob` is the masking probability
      * `randomize_prob` is the probability of replacing with a random token
      * `no_change_prob` is the probability of replacing with original token
      """
      self.n_tokens = n_tokens
      self.no_change_prob = no_change_prob
      self.randomize_prob = randomize_prob
      self.masking_prob = masking_prob
      self.no_mask_tokens = no_mask_tokens + [padding_token, mask_token]
      self.padding_token = padding_token
      self.mask_token = mask_token
  
imports: from typing import List
  import torch
label_desc: Use image node_id 1 to create a new MLM object  with example: obj = MLM()