code: def __init__(self, cfg, resolution):
      super().__init__()
  
      self.resolution = resolution
      assert (
          len(self.resolution) == 3
      ), "MRD layer requires list with len=3, got {}".format(
          self.resolution
      )
      self.lrelu_slope = cfg.lrelu_slope
  
      norm_f = (
          weight_norm
          if cfg.use_spectral_norm == False
          else spectral_norm
      )
  
      self.convs = nn.ModuleList(
          [
              norm_f(nn.Conv2d(1, 32, (3, 9), padding=(1, 4))),
              norm_f(
                  nn.Conv2d(
                      32, 32, (3, 9), stride=(1, 2), padding=(1, 4)
                  )
              ),
              norm_f(
                  nn.Conv2d(
                      32, 32, (3, 9), stride=(1, 2), padding=(1, 4)
                  )
              ),
              norm_f(
                  nn.Conv2d(
                      32, 32, (3, 9), stride=(1, 2), padding=(1, 4)
                  )
              ),
              norm_f(nn.Conv2d(32, 32, (3, 3), padding=(1, 1))),
          ]
      )
      self.conv_post = norm_f(nn.Conv2d(32, 1, (3, 3), padding=(1, 1)))
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn import Conv2d
  from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm
  from nemo.core.classes.common import typecheck
  from nemo.core.classes.module import NeuralModule
  from nemo.core.neural_types.elements import AudioSignal, MelSpectrogramType, VoidType
  from nemo.core.neural_types.neural_type import NeuralType
label_desc: Use image node_id 1 to create a new DiscriminatorR object from inherited base classes: NeuralModule with example: obj = DiscriminatorR(cfg, resolution)