code: def __init__(self, channels, kernel_size, dilation):
      super().__init__()
      self.lrelu_slope = LRELU_SLOPE
      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.convs1.apply(init_weights)
  
      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),
                  )
              ),
          ]
      )
      self.convs2.apply(init_weights)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
  from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm
  from nemo.core.classes.common import typecheck
  from nemo.core.classes.module import NeuralModule
  from nemo.core.neural_types.elements import AudioSignal, MelSpectrogramType, VoidType
  from nemo.core.neural_types.neural_type import NeuralType
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)