    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,)
        """

        enc_out = super().forward(src_tokens, src_lengths)
        x = self.fc_out(enc_out["encoder_out"])
        # x = F.log_softmax(x, dim=-1)
        # Note: no need this line, because model.get_normalized_prob will call
        # log_softmax
        return {
            "encoder_out": x,  # (T, B, C)
            "encoder_padding_mask": enc_out["encoder_padding_mask"],  # (T, B)
        }
src_tokens: padded tensor (B, T, C * feat)
src_lengths: tensor of original lengths of input utterances (B,)
examples/speech_recognition/models/vggtransformer.py