    def parse_decoder_outputs(self, mel_outputs, gate_outputs, alignments):
        """ Prepares decoder outputs for output
        PARAMS
        ------
        mel_outputs:
        gate_outputs: gate output energies
        alignments:

        RETURNS
        -------
        mel_outputs:
        gate_outpust: gate output energies
        alignments:
        """
        # (T_out, B) -> (B, T_out)
        alignments = torch.stack(alignments).transpose(0, 1)
        # (T_out, B) -> (B, T_out)
        gate_outputs = torch.stack(gate_outputs).transpose(0, 1)
        gate_outputs = gate_outputs.contiguous()
        # (T_out, B, n_mel_channels) -> (B, T_out, n_mel_channels)
        mel_outputs = torch.stack(mel_outputs).transpose(0, 1).contiguous()
        # decouple frames per step
        mel_outputs = mel_outputs.view(
            mel_outputs.size(0), -1, self.n_mel_channels)
        # (B, T_out, n_mel_channels) -> (B, n_mel_channels, T_out)
        mel_outputs = mel_outputs.transpose(1, 2)

        return mel_outputs, gate_outputs, alignments
Prepares decoder outputs for output
PARAMS
------
mel_outputs:
gate_outputs: gate output energies
alignments:

RETURNS
-------
mel_outputs:
gate_outpust: gate output energies
alignments:
examples/textless_nlp/gslm/unit2speech/tacotron2/model.py