code: def generate_square_subsequent_mask(self, sz):
      mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)
      mask = (
          mask.float()
          .masked_fill(mask == 0, float("-inf"))
          .masked_fill(mask == 1, float(0.0))
      )
      return mask
  
imports: import math
  import sys
  import pytest
  import torch
  import torch.nn
  import torch.nn
  from fairscale.internal import torch_version
label_desc: Use image node_id 2 for calling the TransformerModel obj's underlying member method code with example usage: obj.generate_square_subsequent_mask(sz) and returns: mask