class_docstr: All functions are replaced to the same `new` function.
  
  Args:
      inputs (:obj:`dict`): Input variable mapping from the original input to another input. Default is the empty dictionary, so the new graph shares the original inputs.
  
  Examples:
  
  .. code-block:: python
  
     pred = Model(...)
     x = nn.Variable(...)
  
     import nnabla.experimental.graph_converters as GC
  
     modifiers = [GC.IdentityModifier({x0: x1})]
     gc = GC.GraphConverter(modifiers)
     pred = gc.convert(pred)
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code: def modify(self, f, inputs):
      # Replace only the initial inputs
      if inputs[0].parent:
          return
  
      # Check if replacement dict empty
      if not self._input_dict:
          return
  
      if f.inputs[0] in self._input_dict:
          inp_repl = self._input_dict[f.inputs[0]]
          if not self._copy_value:
              inps = inp_repl
          else:
              if inp_repl.shape != f.inputs[0].shape:
                  raise ValueError(
                      "Shape between the replaced input ({}) and original input ({}) differs when copy_value=True".format(
                          inp_repl.shape, f.inputs[0].shape
                      )
                  )
              inp_repl.d = f.inputs[0].d.copy()
              inp_repl.g = f.inputs[0].g.copy()
          inps = [inp_repl] + inputs[1:]
  
          self.init_map_func_inputs(f, inps)
  
          o = self._call_function(f.info.type_name, inps, f.info.args)
          return o
  
imports: from .graph_converter import FunctionModifier
label_desc: Use image node_id 2 for calling the IdentityModifier obj's underlying member method code with example usage: obj.modify(f, inputs) and returns: o