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.
class_docstr_tok: ["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","."]
docstr: Parameters
  ----------
  x : torch.tensor
  
  Returns
  -------
  torch.tensor
docstr_tok: ["Parameters","--","--","--","--","--","x",":","torch.tensor","Returns","--","--","--","-","torch.tensor"]
code: def forward(self, x: torch.tensor) -> torch.tensor:
      """
      Parameters
      ----------
      x : torch.tensor
  
      Returns
      -------
      torch.tensor
      """
      y = self.block(x)
      pad = (x.shape[-1] - y.shape[-1]) // 2
      if pad > 0:
          x = x[..., pad:-pad]
      return x + y
  
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 2 for calling the ResidualUnit obj's underlying member method code with example usage: obj.forward(x) and returns: unknown