code: def __init__(self, channels, num_res_blocks, kernel_size, name):
      super(ResidualStack, self).__init__(name=name)
  
      assert (
          kernel_size - 1
      ) % 2 == 0, " [!] kernel_size has to be odd."
      base_padding = (kernel_size - 1) // 2
  
      self.blocks = []
      num_layers = 2
      for idx in range(num_res_blocks):
          layer_kernel_size = kernel_size
          layer_dilation = layer_kernel_size**idx
          layer_padding = base_padding * layer_dilation
          block = [
              tf.keras.layers.LeakyReLU(0.2),
              ReflectionPad1d(layer_padding),
              tf.keras.layers.Conv2D(
                  filters=channels,
                  kernel_size=(kernel_size, 1),
                  dilation_rate=(layer_dilation, 1),
                  use_bias=True,
                  padding="valid",
                  name=f"blocks.{idx}.{num_layers}",
              ),
              tf.keras.layers.LeakyReLU(0.2),
              tf.keras.layers.Conv2D(
                  filters=channels,
                  kernel_size=(1, 1),
                  use_bias=True,
                  name=f"blocks.{idx}.{num_layers + 2}",
              ),
          ]
          self.blocks.append(block)
      self.shortcuts = [
          tf.keras.layers.Conv2D(
              channels,
              kernel_size=1,
              use_bias=True,
              name=f"shortcuts.{i}",
          )
          for i in range(num_res_blocks)
      ]
  
file_path: TTS/TTS/vocoder/tf/layers/melgan.py
imports: import tensorflow
label_desc: Use image node_id 1 to create a new ResidualStack object from inherited base classes: tf.keras.layers with example: obj = ResidualStack(channels, num_res_blocks, kernel_size, name)