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 modify(self, f, inputs):
      outputs = f.outputs[0]
  
      # Not end
      if len(outputs.function_references) == 0:
          return
  
      # Check fused bn block start
      if not self._block and self._is_fused_bn_block(f, inputs):
          self._block = True
  
      # Remove BatchNormalization
      if self._block and f.info.type_name == "BatchNormalization":
          self._bn_args = f.info.args
          self._name = self.get_parameter_scope(inputs[0])
          return inputs[0]
  
      # Remove Add2
      if self._block and f.info.type_name == "Add2":
          self._add2_input1 = inputs[1]
          return inputs[0]
  
      # Remove non linear function then connect fused bn
      if self._block and f.info.type_name in self._fct_set:
          f_non_linear = self._fct_set[f.info.type_name]
          h = PF.fused_batch_normalization(
              inputs[0],
              self._add2_input1,
              nonlinearity=f_non_linear,
              **self._bn_args,
              name="fused{}-{}".format(self._name, self._cnt),
          )
          self._cnt += 1
          self._block = False
          return h
  
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 2 for calling the FusedBatchNormalizationModifier obj's underlying member method code with example usage: obj.modify(f, inputs) and returns: inputs, inputs, h