class_docstr: Residual Block Type 1. It has 3 convolutional layers in each convolutional block.
  
  Network::
  
      x -> lrelu -> conv1_1 -> conv1_2 -> conv1_3 -> z -> lrelu -> conv2_1 -> conv2_2 -> conv2_3 -> o -> + -> o
      |--------------------------------------------------------------------------------------------------|
  
  
  Args:
      channels (int): number of hidden channels for the convolutional layers.
      kernel_size (int): size of the convolution filter in each layer.
      dilations (list): list of dilation value for each conv layer in a block.
code: def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
      super().__init__()
      self.convs1 = nn.ModuleList(
          [
              weight_norm(
                  Conv1d(
                      channels,
                      channels,
                      kernel_size,
                      1,
                      dilation=dilation[0],
                      padding=get_padding(kernel_size, dilation[0]),
                  )
              ),
              weight_norm(
                  Conv1d(
                      channels,
                      channels,
                      kernel_size,
                      1,
                      dilation=dilation[1],
                      padding=get_padding(kernel_size, dilation[1]),
                  )
              ),
              weight_norm(
                  Conv1d(
                      channels,
                      channels,
                      kernel_size,
                      1,
                      dilation=dilation[2],
                      padding=get_padding(kernel_size, dilation[2]),
                  )
              ),
          ]
      )
  
      self.convs2 = nn.ModuleList(
          [
              weight_norm(
                  Conv1d(
                      channels,
                      channels,
                      kernel_size,
                      1,
                      dilation=1,
                      padding=get_padding(kernel_size, 1),
                  )
              ),
              weight_norm(
                  Conv1d(
                      channels,
                      channels,
                      kernel_size,
                      1,
                      dilation=1,
                      padding=get_padding(kernel_size, 1),
                  )
              ),
              weight_norm(
                  Conv1d(
                      channels,
                      channels,
                      kernel_size,
                      1,
                      dilation=1,
                      padding=get_padding(kernel_size, 1),
                  )
              ),
          ]
      )
  
imports: import torch
  import torchaudio
  from torch import nn
  from torch.nn import Conv1d, ConvTranspose1d
  from torch.nn import functional
  from torch.nn.utils.parametrizations import weight_norm
  from torch.nn.utils.parametrize import remove_parametrizations
  from TTS.utils.io import load_fsspec
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)