code: def __init__(
      self,
      channels,
      hidden_channels,
      kernel_size,
      dilation_rate,
      num_layers,
      dropout_p=0,
      cond_channels=0,
      mean_only=False,
  ):
      assert channels % 2 == 0, "channels should be divisible by 2"
      super().__init__()
      self.half_channels = channels // 2
      self.mean_only = mean_only
      # input layer
      self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
      # coupling layers
      self.enc = WN(
          hidden_channels,
          hidden_channels,
          kernel_size,
          dilation_rate,
          num_layers,
          dropout_p=dropout_p,
          c_in_channels=cond_channels,
      )
      # output layer
      # Initializing last layer to 0 makes the affine coupling layers
      # do nothing at first.  This helps with training stability
      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 math
  import torch
  from torch import nn
  from TTS.tts.layers.glow_tts.glow import WN
  from TTS.tts.layers.glow_tts.transformer import RelativePositionTransformer
  from TTS.tts.utils.helpers import sequence_mask
label_desc: Use image node_id 1 to create a new ResidualCouplingBlock object from inherited base classes: nn with example: obj = ResidualCouplingBlock(channels, hidden_channels, kernel_size, dilation_rate, num_layers, dropout_p, cond_channels, mean_only)