text: import torch
  import torch.nn as nn
  
  from .mpd import MultiPeriodDiscriminator
  from .mrd import MultiResolutionDiscriminator
  from omegaconf import OmegaConf
  
  class Discriminator(nn.Module):
      def __init__(self, hp):
          super(Discriminator, self).__init__()
          self.MRD = MultiResolutionDiscriminator(hp)
          self.MPD = MultiPeriodDiscriminator(hp)
  
      def forward(self, x):
          return self.MRD(x), self.MPD(x)
  
  if __name__ == '__main__':
      hp = OmegaConf.load('../config/default.yaml')
      model = Discriminator(hp)
  
      x = torch.randn(3, 1, 16384)
      print(x.shape)
  
      mrd_output, mpd_output = model(x)
      for features, score in mpd_output:
          for feat in features:
              print(feat.shape)
          print(score.shape)
  
      pytorch_total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
      print(pytorch_total_params)
  
  