class_docstr: Unfuse `FusedBatchNormalization` to `BatchNormalization -> Add2 -> Non-Linear` block.
  
  If there is a `FusedBatchNormalization` pass, remove the fused batch normalization
  and replace it with the block `BatchNormalization -> Add2 -> Non-Linear`.
  
  Supported Non-Linear functions: `relu`
  
  Examples:
  
  .. code-block:: python
  
     pred = Model(...)
  
     import nnabla.experimental.graph_converters as GC
  
     modifiers = [GC.UnfusedBatchNormalizationModifier()]
     gc = GC.GraphConverter(modifiers)
     pred = gc.convert(pred)
code: def modify(self, f, inputs):
      if f.info.type_name != "FusedBatchNormalization":
          return
  
      # Prepare BN args from FBN args
      args = f.info.args
      f_non_linear = args["nonlinearity"]
      del args["nonlinearity"]
  
      # Replace FBN to "BN -> Add2 -> Non-linear"
      h = PF.batch_normalization(inputs[0], **args)
      return (
          self._fct_set[f_non_linear](h)
          if len(inputs) == 5
          else self._fct_set[f_non_linear](inputs[5] + h)
      )
  
imports: import nnabla.functions
  import nnabla.parametric_functions
  from .graph_converter import FunctionModifier
label_desc: Use image node_id 2 for calling the UnfusedBatchNormalizationModifier obj's underlying member method code with example usage: obj.modify(f, inputs) and returns: unknown