    def forward(self, forward_input):
        """
        forward_input[0] = mel_spectrogram:  batch x n_mel_channels x frames
        forward_input[1] = audio: batch x time
        """
        spect, audio = forward_input

        #  Upsample spectrogram to size of audio
        spect = self.upsample(spect)
        assert(spect.size(2) >= audio.size(1))
        if spect.size(2) > audio.size(1):
            spect = spect[:, :, :audio.size(1)]

        spect = spect.unfold(2, self.n_group, self.n_group).permute(0, 2, 1, 3)
        spect = spect.contiguous().view(spect.size(0), spect.size(1), -1).permute(0, 2, 1)

        audio = audio.unfold(1, self.n_group, self.n_group).permute(0, 2, 1)
        output_audio = []
        log_s_list = []
        log_det_W_list = []

        for k in range(self.n_flows):
            if k % self.n_early_every == 0 and k > 0:
                output_audio.append(audio[:,:self.n_early_size,:])
                audio = audio[:,self.n_early_size:,:]

            audio, log_det_W = self.convinv[k](audio)
            log_det_W_list.append(log_det_W)

            n_half = int(audio.size(1)/2)
            audio_0 = audio[:,:n_half,:]
            audio_1 = audio[:,n_half:,:]

            output = self.WN[k]((audio_0, spect))
            log_s = output[:, n_half:, :]
            b = output[:, :n_half, :]
            audio_1 = torch.exp(log_s)*audio_1 + b
            log_s_list.append(log_s)

            audio = torch.cat([audio_0, audio_1],1)

        output_audio.append(audio)
        return torch.cat(output_audio,1), log_s_list, log_det_W_list
forward_input[0] = mel_spectrogram:  batch x n_mel_channels x frames
forward_input[1] = audio: batch x time
examples/textless_nlp/gslm/unit2speech/glow.py