class_docstr: A wrapper block for residual networks. It is used to wrap a single layer of the network.
  
  Example:
  layers = []
  for layer in layer_generator:
      layers.append(ResidualWrapper(layer))
  model = torch.nn.Sequential(*layers)
class_docstr_tok: ["A","wrapper","block","for","residual","networks",".","It","is","used","to","wrap","a","single","layer","of","the","network",".","Example",":","layers","=","[","]","for","layer","in","layer_generator",":","layers.append","(","ResidualWrapper","(","layer",")",")","model","=","torch.nn.Sequential","(","*","layers",")"]
code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      return x + self.module(x)
  
imports: import torch
  from torch import nn
label_desc: Use image node_id 2 for calling the ResidualWrapper obj's underlying member method code with example usage: obj.forward(x) and returns: unknown