code: def __init__(
      self,
      channels,
      hidden_channels,
      kernel_size,
      dilation_rate,
      n_layers,
      p_dropout=0,
      gin_channels=0,
      mean_only=False,
  ):
      assert channels % 2 == 0, "channels should be divisible by 2"
      super().__init__()
      self.channels = channels
      self.hidden_channels = hidden_channels
      self.kernel_size = kernel_size
      self.dilation_rate = dilation_rate
      self.n_layers = n_layers
      self.half_channels = channels // 2
      self.mean_only = mean_only
  
      self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
      self.enc = WN(
          hidden_channels,
          kernel_size,
          dilation_rate,
          n_layers,
          p_dropout=p_dropout,
          gin_channels=gin_channels,
      )
      self.post = nn.Conv1d(
          hidden_channels, self.half_channels * (2 - mean_only), 1
      )
      self.post.weight.data.zero_()
      self.post.bias.data.zero_()
  
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 ResidualCouplingLayer object from inherited base classes: nn with example: obj = ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout, gin_channels, mean_only)