def forward(self, src_tokens, src_lengths, **kwargs):

        """
        src_tokens: padded tensor (B, T, C * feat)
        src_lengths: tensor of original lengths of input utterances (B,)
        """
        B, T, _ = src_tokens.size()
        x = src_tokens.transpose(1, 2).contiguous()  # (B, feat, T) assuming C == 1

        for layer_idx in range(len(self.conv_layers)):
            x = self.conv_layers[layer_idx](x)
            x = F.glu(x, dim=1)
            x = self.dropouts[layer_idx](x)

        x = x.transpose(1, 2).contiguous()  # (B, T, 908)
        x = self.linear_layers[0](x)
        x = F.glu(x, dim=2)
        x = self.dropouts[-1](x)
        x = self.linear_layers[1](x)

        assert x.size(0) == B
        assert x.size(1) == T

        encoder_out = x.transpose(0, 1)  # (T, B, vocab_size)

        # need to debug this -- find a simpler/elegant way in pytorch APIs
        encoder_padding_mask = (
            torch.arange(T).view(1, T).expand(B, -1).to(x.device)
            >= src_lengths.view(B, 1).expand(-1, T)
        ).t()  # (B x T) -> (T x B)

        return {
            "encoder_out": encoder_out,  # (T, B, vocab_size)
            "encoder_padding_mask": encoder_padding_mask,  # (T, B)
        }

        src_tokens: padded tensor (B, T, C * feat)
        src_lengths: tensor of original lengths of input utterances (B,)
        
understand_icl/fairseq/examples/speech_recognition/models/w2l_conv_glu_enc.py