class_docstr: Block `BatchNormalization -> Add2 -> Non-Linear` pass is fused into one `FusedBatchNormalization`.
  
  If there is a block `BatchNormalization -> Add2 -> Non-Linear` pass,
  remove all the block functions and replace the whole block to `FusedBatchNormalization`.
  
  Examples:
  
  .. code-block:: python
  
     pred = Model(...)
  
     import nnabla.experimental.graph_converters as GC
  
     modifiers = [GC.FusedBatchNormalizationModifier()]
     gc = GC.GraphConverter(modifiers)
     pred = gc.convert(pred)
code: def __init__(self):
      super(FusedBatchNormalizationModifier, self).__init__()
      self._name = ""
      self._block = False
      self._bn_args = None
      self._add2_input1 = None
      self._cnt = 1
      self._fct_set = {"ReLU": "relu"}
  
file_path: nnabla/python/src/nnabla/experimental/graph_converters/fused_batch_normalization.py
imports: import nnabla.parametric_functions
  from .graph_converter import FunctionModifier
label_desc: Use image node_id 1 to create a new FusedBatchNormalizationModifier object from inherited base classes: FunctionModifier with example: obj = FusedBatchNormalizationModifier()