class_docstr: A residual unit module for convolutional neural networks.
  
  Parameters
  ----------
  dim : int, optional
      The number of channels in the input tensor. Default is 16.
  dilation : int, optional
      The dilation rate for the convolutional layers. Default is 1.
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docstr: Initializes the ResidualUnit
  Parameters
  ----------
  dim : int, optional, by default 16
  dilation : int, optional, by default 1
docstr_tok: ["Initializes","the","ResidualUnit","Parameters","--","--","--","--","--","dim",":","int",",","optional",",","by","default","16","dilation",":","int",",","optional",",","by","default","1"]
code: def __init__(self, dim: int = 16, dilation: int = 1):
      """
      Initializes the ResidualUnit
      Parameters
      ----------
      dim : int, optional, by default 16
      dilation : int, optional, by default 1
      """
      super().__init__()
      pad = ((7 - 1) * dilation) // 2
      self.block = nn.Sequential(
          Snake1d(dim),
          WNConv1d(
              dim, dim, kernel_size=7, dilation=dilation, padding=pad
          ),
          Snake1d(dim),
          WNConv1d(dim, dim, kernel_size=1),
      )
  
imports: import math
  from pathlib import Path
  from typing import List, Union
  import logging
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.utils.parametrizations import weight_norm
label_desc: Use image node_id 1 to create a new ResidualUnit object from inherited base classes: nn with example: obj = ResidualUnit(dim, dilation)