code: def __init__(self, input_size, hidden_size, factor, dilation):
      super().__init__()
      assert isinstance(dilation, (list, tuple))
      assert len(dilation) == 4
  
      self.factor = factor
      self.res_block = Conv1d(input_size, hidden_size, 1)
      self.main_block = nn.ModuleList(
          [
              Conv1d(
                  input_size,
                  hidden_size,
                  3,
                  dilation=dilation[0],
                  padding=dilation[0],
              ),
              Conv1d(
                  hidden_size,
                  hidden_size,
                  3,
                  dilation=dilation[1],
                  padding=dilation[1],
              ),
          ]
      )
      self.out_block = nn.ModuleList(
          [
              Conv1d(
                  hidden_size,
                  hidden_size,
                  3,
                  dilation=dilation[2],
                  padding=dilation[2],
              ),
              Conv1d(
                  hidden_size,
                  hidden_size,
                  3,
                  dilation=dilation[3],
                  padding=dilation[3],
              ),
          ]
      )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.utils import weight_norm
label_desc: Use image node_id 1 to create a new UBlock object from inherited base classes: nn with example: obj = UBlock(input_size, hidden_size, factor, dilation)