class_docstr: Ying decoder module.
docstr: Initialize the YingDecoder module.
  
  Args:
      hidden_channels (int): Number of hidden channels.
      kernel_size (int): Size of the convolutional kernel.
      dilation_rate (int): Dilation rate of the convolutional layers.
      n_layers (int): Number of convolutional layers.
      yin_start (int): Start point of the yin target signal.
      yin_scope (int): Scope of the yin target signal.
      yin_shift_range (int): Maximum number of frames to shift the yin
                             target signal.
      gin_channels (int, optional): Number of global conditioning channels.
                                    Defaults to 0.
code: def __init__(
      self,
      hidden_channels,
      kernel_size,
      dilation_rate,
      n_layers,
      yin_start,
      yin_scope,
      yin_shift_range,
      gin_channels=0,
  ):
      """Initialize the YingDecoder module.
  
      Args:
          hidden_channels (int): Number of hidden channels.
          kernel_size (int): Size of the convolutional kernel.
          dilation_rate (int): Dilation rate of the convolutional layers.
          n_layers (int): Number of convolutional layers.
          yin_start (int): Start point of the yin target signal.
          yin_scope (int): Scope of the yin target signal.
          yin_shift_range (int): Maximum number of frames to shift the yin
                                 target signal.
          gin_channels (int, optional): Number of global conditioning channels.
                                        Defaults to 0.
      """
      super().__init__()
      self.in_channels = yin_scope
      self.out_channels = yin_scope
      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.yin_start = yin_start
      self.yin_scope = yin_scope
      self.yin_shift_range = yin_shift_range
  
      self.pre = nn.Conv1d(self.in_channels, hidden_channels, 1)
      self.dec = modules.WN(
          hidden_channels,
          kernel_size,
          dilation_rate,
          n_layers,
          gin_channels=gin_channels,
      )
      self.proj = nn.Conv1d(hidden_channels, self.out_channels, 1)
  
file_path: espnet/espnet2/gan_svs/pits/ying_decoder.py
imports: import torch
  import torch.nn
  import espnet2.gan_svs.pits.modules
label_desc: Use image node_id 1 to create a new YingDecoder object from inherited base classes: nn with example: obj = YingDecoder(hidden_channels, kernel_size, dilation_rate, n_layers, yin_start, yin_scope, yin_shift_range, gin_channels)