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 _compute_self_folded_parameters(self, bn_func, inputs, cnt):
      # Conversion
      eps_data = bn_func.info.args["eps"]
      beta_data = np.squeeze(bn_func.inputs[1].d)
      gamma_data = np.squeeze(bn_func.inputs[2].d)
      mean_data = np.squeeze(bn_func.inputs[3].d)
      var_data = np.squeeze(bn_func.inputs[4].d)
      sigma_data = np.sqrt(var_data + eps_data)
      c0_data = gamma_data / sigma_data
      c1_data = beta_data - (gamma_data * mean_data) / sigma_data
  
      # Reshape
      oshape = bn_func.inputs[1].shape
      c0_data = c0_data.reshape(oshape)
      c1_data = c1_data.reshape(oshape)
  
      # Inputs
      x = inputs[0]
  
      c0 = nn.parameter.get_parameter_or_create(
          "c0-{}-{}".format(self._name, cnt), c0_data.shape, c0_data
      )
      c1 = nn.parameter.get_parameter_or_create(
          "c1-{}-{}".format(self._name, cnt), c1_data.shape, c1_data
      )
  
      # Function call
      o = c0 * x + c1
  
      return o
  
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 3 for calling the BatchNormalizationSelfFoldingModifier obj's underlying member method code with example usage: obj._compute_self_folded_parameters(bn_func, inputs, cnt) and returns: o