models/parallel_wavegan.py [490:506]:
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                real_loss = self.criterion["mse"](p, p.new_ones(p.size()))
                fake_loss = self.criterion["mse"](p_, p_.new_zeros(p_.size()))
                dis_loss = real_loss + fake_loss
            else:
                # for multi-scale discriminator
                real_loss = 0.0
                fake_loss = 0.0
                for i in range(len(p)):
                    real_loss += self.criterion["mse"](
                        p[i][-1], p[i][-1].new_ones(p[i][-1].size())
                    )
                    fake_loss += self.criterion["mse"](
                        p_[i][-1], p_[i][-1].new_zeros(p_[i][-1].size())
                    )
                real_loss /= i + 1
                fake_loss /= i + 1
                dis_loss = real_loss + fake_loss
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models/parallel_wavegan.py [611:627]:
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            real_loss = self.criterion["mse"](p, p.new_ones(p.size()))
            fake_loss = self.criterion["mse"](p_, p_.new_zeros(p_.size()))
            dis_loss = real_loss + fake_loss
        else:
            # for multi-scale discriminator
            real_loss = 0.0
            fake_loss = 0.0
            for i in range(len(p)):
                real_loss += self.criterion["mse"](
                    p[i][-1], p[i][-1].new_ones(p[i][-1].size())
                )
                fake_loss += self.criterion["mse"](
                    p_[i][-1], p_[i][-1].new_zeros(p_[i][-1].size())
                )
            real_loss /= i + 1
            fake_loss /= i + 1
            dis_loss = real_loss + fake_loss
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