class_docstr: HiFiGAN Scale Discriminator.
  It is similar to `MelganDiscriminator` but with a specific architecture explained in the paper.
  
  Args:
      use_spectral_norm (bool): if `True` swith to spectral norm instead of weight norm.
code: def __init__(self, use_spectral_norm=False):
      super().__init__()
      norm_f = (
          nn.utils.spectral_norm
          if use_spectral_norm
          else nn.utils.parametrizations.weight_norm
      )
      self.convs = nn.ModuleList(
          [
              norm_f(nn.Conv1d(1, 128, 15, 1, padding=7)),
              norm_f(nn.Conv1d(128, 128, 41, 2, groups=4, padding=20)),
              norm_f(nn.Conv1d(128, 256, 41, 2, groups=16, padding=20)),
              norm_f(nn.Conv1d(256, 512, 41, 4, groups=16, padding=20)),
              norm_f(
                  nn.Conv1d(512, 1024, 41, 4, groups=16, padding=20)
              ),
              norm_f(
                  nn.Conv1d(1024, 1024, 41, 1, groups=16, padding=20)
              ),
              norm_f(nn.Conv1d(1024, 1024, 5, 1, padding=2)),
          ]
      )
      self.conv_post = norm_f(nn.Conv1d(1024, 1, 3, 1, padding=1))
  
imports: import torch
  from torch import nn
  from torch.nn import functional
label_desc: Use image node_id 1 to create a new DiscriminatorS object from inherited base classes: torch.nn with example: obj = DiscriminatorS(use_spectral_norm)