class_docstr: Wrap the target function and operate as ``FunctionNode``
  
  Arguments:
      name (str): name of the function node
      func (func): the target function
      args (list): args for the function
      kwargs (dict): kwargs for the function
      arg_vars (list): list of `chainer.Variable`s in `args` and `kwargs`
      attributes (list): parameters to be set node's attributes
class_docstr_tok: ["Wrap","the","target","function","and","operate","as","``","FunctionNode","``","Arguments",":","name","(","str",")",":","name","of","the","function","node","func","(","func",")",":","the","target","function","args","(","list",")",":","args","for","the","function","kwargs","(","dict",")",":","kwargs","for","the","function","arg_vars","(","list",")",":","list","of","`","chainer.Variable","`","s","in","`","args","`","and","`","kwargs","`","attributes","(","list",")",":","parameters","to","be","set","node","'s","attributes"]
code: def reconstruct_return_value(self, outputs):
      def f(skeleton):
          if isinstance(skeleton, tuple):
              return tuple(f(e) for e in skeleton)
          elif isinstance(skeleton, list):
              return [f(e) for e in skeleton]
          elif isinstance(skeleton, dict):
              return {k: f(v) for k, v in skeleton.items()}
          else:
              return outputs[skeleton]
  
      return f(self.skeleton)
  
file_path: chainer/onnx_chainer/replace_func.py
imports: import inspect
  import chainer
label_desc: Use image node_id 5 for calling the WrappedFunctionNode obj's underlying member method code with example usage: obj.reconstruct_return_value(outputs) and returns: f, tuple, unknown, unknown, outputs