code: def __call__(self, x: torch.Tensor):
      """
      * `x` is the batch of input token sequences.
       It's a tensor of type `long` with shape `[seq_len, batch_size]`.
      """
  
      # Mask `masking_prob` of tokens
      full_mask = (
          torch.rand(x.shape, device=x.device) < self.masking_prob
      )
      # Unmask `no_mask_tokens`
      for t in self.no_mask_tokens:
          full_mask &= x != t
  
      # A mask for tokens to be replaced with original tokens
      unchanged = full_mask & (
          torch.rand(x.shape, device=x.device) < self.no_change_prob
      )
      # A mask for tokens to be replaced with a random token
      random_token_mask = full_mask & (
          torch.rand(x.shape, device=x.device) < self.randomize_prob
      )
      # Indexes of tokens to be replaced with random tokens
      random_token_idx = torch.nonzero(random_token_mask, as_tuple=True)
      # Random tokens for each of the locations
      random_tokens = torch.randint(
          0, self.n_tokens, (len(random_token_idx[0]),), device=x.device
      )
      # The final set of tokens that are going to be replaced by `[MASK]`
      mask = full_mask & ~random_token_mask & ~unchanged
  
      # Make a clone of the input for the labels
      y = x.clone()
  
      # Replace with `[MASK]` tokens;
      # note that this doesn't include the tokens that will have the original token unchanged and
      # those that get replace with a random token.
      x.masked_fill_(mask, self.mask_token)
      # Assign random tokens
      x[random_token_idx] = random_tokens
  
      # Assign token `[PAD]` to all the other locations in the labels.
      # The labels equal to `[PAD]` will not be used in the loss.
      y.masked_fill_(~full_mask, self.padding_token)
  
      # Return the masked input and the labels
      return x, y
  
imports: from typing import List
  import torch
label_desc: Use image node_id 2 for calling the MLM obj's underlying member method code with example usage: obj.__call__(x) and returns: x,  y