code: def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)):
      super(ResBlock2, self).__init__()
      self.h = h
      self.convs = 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]),
                  )
              ),
          ]
      )
      self.convs.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
label_desc: Use image node_id 1 to create a new ResBlock2 object from inherited base classes: torch.nn with example: obj = ResBlock2(h, channels, kernel_size, dilation)