class_docstr: Residual block of type 1 for HiFiGAN Vocoder :cite:`NEURIPS2020_c5d73680`.
  Args:
      channels (int): Number of channels in the input features.
      kernel_size (int, optional): Kernel size for 1D convolutions. (Default: ``3``)
      dilation (tuple of 3 ``int``, optional): Dilations for each 1D convolution. (Default: ``(1, 3, 5)``)
      lrelu_slope (float): Slope of leaky ReLUs in activations.
code: def __init__(
      self,
      channels: int,
      kernel_size: int = 3,
      dilation: Tuple[int, int, int] = (1, 3, 5),
      lrelu_slope: float = 0.1,
  ):
      super(ResBlock1, self).__init__()
      self.convs1 = nn.ModuleList(
          [
              Conv1d(
                  channels,
                  channels,
                  kernel_size,
                  1,
                  dilation=dilation[0],
                  padding=get_padding(kernel_size, dilation[0]),
              ),
              Conv1d(
                  channels,
                  channels,
                  kernel_size,
                  1,
                  dilation=dilation[1],
                  padding=get_padding(kernel_size, dilation[1]),
              ),
              Conv1d(
                  channels,
                  channels,
                  kernel_size,
                  1,
                  dilation=dilation[2],
                  padding=get_padding(kernel_size, dilation[2]),
              ),
          ]
      )
  
      self.convs2 = nn.ModuleList(
          [
              Conv1d(
                  channels,
                  channels,
                  kernel_size,
                  1,
                  dilation=1,
                  padding=get_padding(kernel_size, 1),
              ),
              Conv1d(
                  channels,
                  channels,
                  kernel_size,
                  1,
                  dilation=1,
                  padding=get_padding(kernel_size, 1),
              ),
              Conv1d(
                  channels,
                  channels,
                  kernel_size,
                  1,
                  dilation=1,
                  padding=get_padding(kernel_size, 1),
              ),
          ]
      )
      self.lrelu_slope = lrelu_slope
  
imports: from typing import Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn import Conv1d, ConvTranspose1d
label_desc: Use image node_id 1 to create a new ResBlock1 object from inherited base classes: torch.nn with example: obj = ResBlock1(channels, kernel_size, dilation, lrelu_slope)