        def _expand_mask(
            mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None
        ):
            """
            Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
            """
            bsz, src_len = mask.size()
            tgt_len = tgt_len if tgt_len is not None else src_len

            expanded_mask = (
                mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
            )

            inverted_mask = 1.0 - expanded_mask

            return inverted_mask.masked_fill(
                inverted_mask.to(torch.bool), torch.finfo(dtype).min
            )
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
models/vc/autoregressive_transformer/global_encoder.py