code: def __init__(
      self,
      hidden_channels,
      kernel_size,
      dilation_rate,
      n_layers,
      gin_channels=0,
      p_dropout=0,
  ):
      super(WN, self).__init__()
      assert kernel_size % 2 == 1
      self.hidden_channels = hidden_channels
      self.kernel_size = (kernel_size,)
      self.dilation_rate = dilation_rate
      self.n_layers = n_layers
      self.gin_channels = gin_channels
      self.p_dropout = p_dropout
  
      self.in_layers = torch.nn.ModuleList()
      self.res_skip_layers = torch.nn.ModuleList()
      self.drop = nn.Dropout(p_dropout)
  
      if gin_channels != 0:
          cond_layer = torch.nn.Conv1d(
              gin_channels, 2 * hidden_channels * n_layers, 1
          )
          self.cond_layer = torch.nn.utils.parametrizations.weight_norm(
              cond_layer, name="weight"
          )
  
      for i in range(n_layers):
          dilation = dilation_rate**i
          padding = int((kernel_size * dilation - dilation) / 2)
          in_layer = torch.nn.Conv1d(
              hidden_channels,
              2 * hidden_channels,
              kernel_size,
              dilation=dilation,
              padding=padding,
          )
          in_layer = torch.nn.utils.parametrizations.weight_norm(
              in_layer, name="weight"
          )
          self.in_layers.append(in_layer)
  
          # last one is not necessary
          if i < n_layers - 1:
              res_skip_channels = 2 * hidden_channels
          else:
              res_skip_channels = hidden_channels
  
          res_skip_layer = torch.nn.Conv1d(
              hidden_channels, res_skip_channels, 1
          )
          res_skip_layer = torch.nn.utils.parametrizations.weight_norm(
              res_skip_layer, name="weight"
          )
          self.res_skip_layers.append(res_skip_layer)
  
imports: import torch
  from torch import nn
  from torch.nn import Conv1d
  from torch.nn import functional
  from torch.nn.utils.parametrizations import weight_norm
  from torch.nn.utils.parametrize import remove_parametrizations
  import TTS.vc.modules.freevc.commons
  from TTS.vc.modules.freevc.commons import get_padding, init_weights
label_desc: Use image node_id 1 to create a new WN object from inherited base classes: torch.nn with example: obj = WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels, p_dropout)