class_docstr: Object-oriented handling of the residual path
  
  This supports scaling of the residual path, as proposed by DeepNet_
  .. _DeepNet: https://arxiv.org/pdf/2203.00555v1.pdf
  
  .. Note: the wrapped layers must accept all the inputs as a single list
class_docstr_tok: ["Object-oriented","handling","of","the","residual","path","This","supports","scaling","of","the","residual","path",",","as","proposed","by","DeepNet_","..","_DeepNet",":","https",":","\/\/arxiv.org\/pdf\/2203.00555v1.pdf","..","Note",":","the","wrapped","layers","must","accept","all","the","inputs","as","a","single","list"]
code: def forward(self, inputs: List[torch.Tensor], **kwargs):
      if self.scale is not None:
          residue = inputs[0] * self.scale
      else:
          residue = inputs[0]
  
      if self.wrap_inputs:
          return residue + self.layer(inputs=inputs, **kwargs)
  
      else:
          return residue + self.layer(*inputs, **kwargs)
  
imports: from enum import Enum
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.nn
  from xformers import _is_triton_available
  from collections import namedtuple
label_desc: Use image node_id 2 for calling the Residual obj's underlying member method code with example usage: obj.forward(inputs) and returns: unknown, unknown