include stft discriminator features into feature loss

        self.final_conv = nn.Conv2d(layer_channels[-1], 1, (16, 1), dtype = torch.complex64) # todo: remove hardcoded 16

    def forward(self, x):

    def forward(self, x, return_intermediates = False):

        x = rearrange(x, 'b 1 n -> b n')

        x = torch.view_as_complex(torch.stft(x, 256))

        x = rearrange(x, 'b ... -> b 1 ...')

        intermediates = []

        x = self.init_conv(x)

        intermediates.append(x)

        for layer in self.layers:

            x = layer(x)

            intermediates.append(x)

        complex_logits = self.final_conv(x)

        if not return_intermediates:

            return complex_logits

        return complex_logits, intermediates

# sound stream

class Residual(nn.Module):

        discr_intermediates = []

        # adversarial loss for multi-scale discriminators

        real, fake = orig_x, recon_x

        # features from stft

        (stft_real_logits, stft_real_intermediates), (stft_fake_logits, stft_fake_intermediates) = map(partial(self.stft_discriminator, return_intermediates=True), (real, fake))

        discr_intermediates.append((stft_real_intermediates, stft_fake_intermediates))

        for discr, scale in zip(self.discriminators, self.discr_multi_scales):

            scaled_real, scaled_fake = map(lambda t: F.interpolate(t, scale_factor = scale), (real, fake))

            (real_logits, real_intermediates), (fake_logits, fake_intermediates) = map(partial(discr, return_intermediates = True), (scaled_real, scaled_fake))

            losses = [F.mse_loss(real_intermediate, fake_intermediate) for real_intermediate, fake_intermediate in zip(real_intermediates, fake_intermediates)]

            feature_losses.extend(losses)

        feature_loss = torch.stack(feature_losses).mean()

        # adversarial loss for stft discriminator

        stft_fake_logits = self.stft_discriminator(recon_x)

        adversarial_losses.append(hinge_gen_loss(stft_fake_logits.real))

        adversarial_losses.append(hinge_gen_loss(stft_fake_logits.imag))