code: def subsequent_mask(size):
      """Generate a square mask for the sequence. The masked positions are filled with float('-inf').
      Unmasked positions are filled with float(0.0).
      """
      mask = paddle.ones([1, size, size], dtype="float32")
      mask_inf = paddle.triu(
          paddle.full(
              shape=[1, size, size], dtype="float32", fill_value="-inf"
          ),
          diagonal=1,
      )
      mask = mask + mask_inf
      padding_mask = paddle.equal(
          mask, paddle.to_tensor(1, dtype=mask.dtype)
      )
      return padding_mask
  
imports: import copy
  import math
  import paddle
  import paddle.nn
  import paddle.nn.functional
label_desc: Use image node_id 1 for calling a global function with example usage: subsequent_mask(size) and returns: padding_mask