    def forward(
        self,
        xs: torch.Tensor,
        xs_lens: torch.Tensor,
        decoding_chunk_size: int = 0,
        num_decoding_left_chunks: int = -1,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """Embed positions in tensor.

        Args:
            xs: padded input tensor (B, T, D)
            xs_lens: input length (B)
            decoding_chunk_size: decoding chunk size for dynamic chunk
                0: default for training, use random dynamic chunk.
                <0: for decoding, use full chunk.
                >0: for decoding, use fixed chunk size as set.
            num_decoding_left_chunks: number of left chunks, this is for decoding,
            the chunk size is decoding_chunk_size.
                >=0: use num_decoding_left_chunks
                <0: use all left chunks
        Returns:
            encoder output tensor xs, and subsampled masks
            xs: padded output tensor (B, T' ~= T/subsample_rate, D)
            masks: torch.Tensor batch padding mask after subsample
                (B, 1, T' ~= T/subsample_rate)
        """
        T = xs.size(1)
        masks = ~make_pad_mask(xs_lens, T).unsqueeze(1)  # (B, 1, T)
        if self.global_cmvn is not None:
            xs = self.global_cmvn(xs)
        xs, pos_emb, masks = self.embed(xs, masks)
        mask_pad = masks  # (B, 1, T/subsample_rate)
        chunk_masks = add_optional_chunk_mask(
            xs,
            masks,
            self.use_dynamic_chunk,
            self.use_dynamic_left_chunk,
            decoding_chunk_size,
            self.static_chunk_size,
            num_decoding_left_chunks,
        )
        for layer in self.encoders:
            xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad)
        if self.normalize_before:
            xs = self.after_norm(xs)
        # Here we assume the mask is not changed in encoder layers, so just
        # return the masks before encoder layers, and the masks will be used
        # for cross attention with decoder later
        return xs, masks
Embed positions in tensor.

Args:
xs: padded input tensor (B, T, D)
xs_lens: input length (B)
decoding_chunk_size: decoding chunk size for dynamic chunk
0: default for training, use random dynamic chunk.
<0: for decoding, use full chunk.
>0: for decoding, use fixed chunk size as set.
num_decoding_left_chunks: number of left chunks, this is for decoding,
the chunk size is decoding_chunk_size.
>=0: use num_decoding_left_chunks
<0: use all left chunks
Returns:
encoder output tensor xs, and subsampled masks
xs: padded output tensor (B, T' ~= T/subsample_rate, D)
masks: torch.Tensor batch padding mask after subsample
(B, 1, T' ~= T/subsample_rate)
modules/wenet_extractor/transformer/encoder.py