def forward(self, src_tokens, src_lengths):
        """Encode input sequence.
        :param torch.Tensor xs: input tensor
        :param torch.Tensor masks: input mask
        :return: position embedded tensor and mask
        :rtype Tuple[torch.Tensor, torch.Tensor]:
        """
        bsz, max_seq_len, _ = src_tokens.size()
        x = (
            src_tokens.view(bsz, max_seq_len, self.in_channels, self.input_dim)
            .transpose(1, 2)
            .contiguous()
        )
        x = self.conv(x)
        bsz, _, output_seq_len, _ = x.size()
        x = x.transpose(1, 2).transpose(0, 1).contiguous().view(output_seq_len, bsz, -1)
        x = self.out(x)
        x = self.embed_scale * x

        subsampling_factor = int(max_seq_len * 1.0 / output_seq_len + 0.5)
        input_len_0 = (src_lengths.float() / subsampling_factor).ceil().long()
        input_len_1 = x.size(0) * torch.ones([src_lengths.size(0)]).long().to(
            input_len_0.device
        )
        input_lengths = torch.min(input_len_0, input_len_1)

        encoder_padding_mask = lengths_to_padding_mask(input_lengths)

        positions = self.embed_positions(encoder_padding_mask).transpose(0, 1)
        x += positions
        x = F.dropout(x, p=self.dropout, training=self.training)

        for layer in self.transformer_layers:
            x = layer(x, encoder_padding_mask)

        if not encoder_padding_mask.any():
            maybe_encoder_padding_mask = None
        else:
            maybe_encoder_padding_mask = encoder_padding_mask

        return {
            "encoder_out": [x],
            "encoder_padding_mask": [maybe_encoder_padding_mask]
            if maybe_encoder_padding_mask is not None
            else [],
            "encoder_embedding": [],
            "encoder_states": [],
            "src_tokens": [],
            "src_lengths": [],
        }
Encode input sequence.
        :param torch.Tensor xs: input tensor
        :param torch.Tensor masks: input mask
        :return: position embedded tensor and mask
        :rtype Tuple[torch.Tensor, torch.Tensor]:
        
structured_prompting/fairseq-version/fairseq/fairseq/models/speech_to_text/convtransformer.py