class_docstr: Identity rule for skipping layer manipulation and propagating the
  relevance over a layer. Only valid for modules with same dimensions for
  inputs and outputs.
  
  Can be used for BatchNorm2D.
class_docstr_tok: ["Identity","rule","for","skipping","layer","manipulation","and","propagating","the","relevance","over","a","layer",".","Only","valid","for","modules","with","same","dimensions","for","inputs","and","outputs",".","Can","be","used","for","BatchNorm2D","."]
code: def _create_backward_hook_input(self, inputs):
      def _backward_hook_input(grad):
          return self.relevance_output[grad.device]
  
      return _backward_hook_input
  
imports: from abc import ABC, abstractmethod
  import torch
  from ..._utils.common import _format_tensor_into_tuples
label_desc: Use image node_id 1 for calling the IdentityRule obj's underlying member method code with example usage: obj._create_backward_hook_input(inputs) and returns: _backward_hook_input, self