class_docstr: CNN discriminator of SEGAN
  
  Arguments
  ---------
  kernel_size : int
      Size of the convolutional kernel.
code: def __init__(self, kernel_size):
      super().__init__()
      self.Layers = torch.nn.ModuleList()
      self.Norms = torch.nn.ModuleList()
      Channels = [
          2,
          16,
          32,
          32,
          64,
          64,
          128,
          128,
          256,
          256,
          512,
          1024,
          1,
      ]
      # Create encoder and decoder layers.
      for i in range(len(Channels) - 1):
          if i != len(Channels) - 2:
              self.Layers.append(
                  nn.Conv1d(
                      in_channels=Channels[i],
                      out_channels=Channels[i + 1],
                      kernel_size=kernel_size,
                      stride=2,
                      padding=floor(kernel_size / 2),  # same
                  )
              )
              self.Norms.append(
                  nn.BatchNorm1d(
                      num_features=Channels[
                          i + 1
                      ]  # not sure what the last dim should be here
                  )
              )
          # output convolution
          else:
              self.Layers.append(
                  nn.Conv1d(
                      in_channels=Channels[i],
                      out_channels=Channels[i + 1],
                      kernel_size=1,
                      stride=1,
                      padding=0,  # same
                  )
              )
              self.Layers.append(
                  nn.Linear(
                      in_features=8,
                      out_features=1,
                  )  # Channels[i+1],
              )
  
file_path: speechbrain/speechbrain/lobes/models/segan_model.py
imports: import torch
  import torch.nn
  import torch.utils.data
  import torch.nn.functional
  from math import floor
label_desc: Use image node_id 1 to create a new Discriminator object from inherited base classes: torch.nn with example: obj = Discriminator(kernel_size)