code: def __init__(self, channels, num_res_blocks, kernel_size):
      super(ResidualStack, self).__init__()
  
      assert (
          kernel_size - 1
      ) % 2 == 0, " [!] kernel_size has to be odd."
      base_padding = (kernel_size - 1) // 2
  
      self.blocks = nn.ModuleList()
      for idx in range(num_res_blocks):
          layer_kernel_size = kernel_size
          layer_dilation = layer_kernel_size**idx
          layer_padding = base_padding * layer_dilation
          self.blocks += [
              nn.Sequential(
                  nn.LeakyReLU(0.2),
                  nn.ReflectionPad1d(layer_padding),
                  weight_norm(
                      nn.Conv1d(
                          channels,
                          channels,
                          kernel_size=kernel_size,
                          dilation=layer_dilation,
                          bias=True,
                      )
                  ),
                  nn.LeakyReLU(0.2),
                  weight_norm(
                      nn.Conv1d(
                          channels, channels, kernel_size=1, bias=True
                      )
                  ),
              )
          ]
  
      self.shortcuts = nn.ModuleList(
          [
              weight_norm(
                  nn.Conv1d(
                      channels, channels, kernel_size=1, bias=True
                  )
              )
              for i in range(num_res_blocks)
          ]
      )
  
imports: from torch import nn
  from torch.nn.utils import weight_norm
label_desc: Use image node_id 1 to create a new ResidualStack object from inherited base classes: nn with example: obj = ResidualStack(channels, num_res_blocks, kernel_size)