def _get_attn_subsequent_mask(self, size):
        """
        Get an attention mask to avoid using the subsequent info.

        Args:
            size: int

        Returns:
            (`LongTensor`):

            * subsequent_mask `[1 x size x size]`
        """
        attn_shape = (1, size, size)
        subsequent_mask = np.triu(np.ones(attn_shape), k=1).astype("uint8")
        subsequent_mask = torch.from_numpy(subsequent_mask)
        return subsequent_mask

        Get an attention mask to avoid using the subsequent info.

        Args:
            size: int

        Returns:
            (`LongTensor`):

            * subsequent_mask `[1 x size x size]`
        
dpkd/transformers/examples/research_projects/bertabs/modeling_bertabs.py