docstr: Redisual Coupling blocks for VITS flow layers.
  
  Args:
      channels (int): Number of input and output tensor channels.
      hidden_channels (int): Number of hidden network channels.
      kernel_size (int): Kernel size of the WaveNet layers.
      dilation_rate (int): Dilation rate of the WaveNet layers.
      num_layers (int): Number of the WaveNet layers.
      num_flows (int, optional): Number of Residual Coupling blocks. Defaults to 4.
      cond_channels (int, optional): Number of channels of the conditioning tensor. Defaults to 0.
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code: def __init__(
      self,
      channels: int,
      hidden_channels: int,
      kernel_size: int,
      dilation_rate: int,
      num_layers: int,
      num_flows=4,
      cond_channels=0,
  ):
      """Redisual Coupling blocks for VITS flow layers.
  
      Args:
          channels (int): Number of input and output tensor channels.
          hidden_channels (int): Number of hidden network channels.
          kernel_size (int): Kernel size of the WaveNet layers.
          dilation_rate (int): Dilation rate of the WaveNet layers.
          num_layers (int): Number of the WaveNet layers.
          num_flows (int, optional): Number of Residual Coupling blocks. Defaults to 4.
          cond_channels (int, optional): Number of channels of the conditioning tensor. Defaults to 0.
      """
      super().__init__()
      self.channels = channels
      self.hidden_channels = hidden_channels
      self.kernel_size = kernel_size
      self.dilation_rate = dilation_rate
      self.num_layers = num_layers
      self.num_flows = num_flows
      self.cond_channels = cond_channels
  
      self.flows = nn.ModuleList()
      for _ in range(num_flows):
          self.flows.append(
              ResidualCouplingBlock(
                  channels,
                  hidden_channels,
                  kernel_size,
                  dilation_rate,
                  num_layers,
                  cond_channels=cond_channels,
                  mean_only=True,
              )
          )
  
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 ResidualCouplingBlocks object from inherited base classes: nn with example: obj = ResidualCouplingBlocks(channels, hidden_channels, kernel_size, dilation_rate, num_layers, num_flows, cond_channels)