code: def __init__(
      self,
      num_residual_channels: int,
      num_skip_channels: int,
      dilation: int,
      kernel_size: int,
      return_dense_output: bool,
  ):
      super().__init__()
      self.num_residual_channels = num_residual_channels
      self.num_skip_channels = num_skip_channels
      self.dilation = dilation
      self.kernel_size = kernel_size
      self.return_dense_output = return_dense_output
  
      # Modules
      self.conv_sigmoid = nn.Sequential(
          nn.Conv1d(
              in_channels=num_residual_channels,
              out_channels=num_residual_channels,
              kernel_size=kernel_size,
              dilation=dilation,
          ),
          nn.Sigmoid(),
      )
      self.conv_tanh = nn.Sequential(
          nn.Conv1d(
              in_channels=num_residual_channels,
              out_channels=num_residual_channels,
              kernel_size=kernel_size,
              dilation=dilation,
          ),
          nn.Tanh(),
      )
      self.conv_skip = nn.Conv1d(
          in_channels=num_residual_channels,
          out_channels=num_skip_channels,
          kernel_size=1,
      )
  
      if self.return_dense_output:
          self.conv_residual = nn.Conv1d(
              in_channels=num_residual_channels,
              out_channels=num_residual_channels,
              kernel_size=1,
          )
  
imports: from typing import List, Optional, Tuple
  import torch
  import torch.nn
  from gluonts.core.component import validated
  from gluonts.torch.modules.feature import FeatureEmbedder
  from gluonts.torch.modules.lookup_table import LookupValues
label_desc: Use image node_id 1 to create a new CausalDilatedResidualLayer object from inherited base classes: nn with example: obj = CausalDilatedResidualLayer(num_residual_channels, num_skip_channels, dilation, kernel_size, return_dense_output)