class_docstr: The parameters of the batch normalization replaced simple scale and bias.
  
  Args:
      name (:obj:`str`): Prefix of the parameter scope.
  
  Examples:
  
  .. code-block:: python
  
     pred = Model(...)
  
     import nnabla.experimental.graph_converters as GC
  
     modifiers = [GC.BatchNormalizationSelfFoldingModifier()]
     gc = GC.GraphConverter(modifiers)
     pred = gc.convert(pred)
code: def modify(self, f, inputs):
      if f.info.type_name != "BatchNormalization":
          return
  
      with nn.parameter_scope(self._name):
          bn_func = f
          output = self._compute_self_folded_parameters(
              bn_func, inputs, self._cnt
          )
          self._cnt += 1
          return output
  
file_path: nnabla/python/src/nnabla/experimental/graph_converters/batch_normalization_self_folding.py
imports: import nnabla
  import numpy
  from .graph_converter import FunctionModifier
label_desc: Use image node_id 2 for calling the BatchNormalizationSelfFoldingModifier obj's underlying member method code with example usage: obj.modify(f, inputs) and returns: output