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 __init__(self):
      super(UnfusedBatchNormalizationModifier, self).__init__()
      self._fct_set = {"relu": F.relu}
  
imports: import nnabla.functions
  import nnabla.parametric_functions
  from .graph_converter import FunctionModifier
label_desc: Use image node_id 1 to create a new UnfusedBatchNormalizationModifier object from inherited base classes: FunctionModifier with example: obj = UnfusedBatchNormalizationModifier()