class_docstr: Network module wrapped by residual connection
  
  Args
  ----------
  residual_network: nn.Module
      the main module to be wrapped
  skip_network: nn.Module, optional
      the auxiliary module to guarantee valid addition
class_docstr_tok: ["Network","module","wrapped","by","residual","connection","Args","--","--","--","--","--","residual_network",":","nn.Module","the","main","module","to","be","wrapped","skip_network",":","nn.Module",",","optional","the","auxiliary","module","to","guarantee","valid","addition"]
code: def forward(self, x: Tensor, *args, **kwargs) -> Tensor:
      skip = self.skip_network(x)
      x = self.residual_network(x, *args, **kwargs)
      return skip + x
  
imports: from typing import List, Optional
  from collections import deque
  import math
  import torch
  from torch import Tensor
  from torch import nn
  from torch.distributions import Distribution
  from torch.distributions import Normal
label_desc: Use image node_id 2 for calling the ResidualBlock obj's underlying member method code with example usage: obj.forward(x) and returns: unknown