code: def __init__(self, use_spectral_norm=False):
      super(DiscriminatorS, self).__init__()
      norm_f = (
          weight_norm if use_spectral_norm == False else spectral_norm
      )
      self.convs = nn.ModuleList(
          [
              norm_f(Conv1d(1, 128, 15, 1, padding=7)),
              norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)),
              norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)),
              norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)),
              norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)),
              norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)),
              norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
          ]
      )
      self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
  from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm
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)