class_docstr: Add bias to `Convolution` in BatchNormalization folding case if it doesn't have bias.
  
  Supported folding functions: `Convolution`, `Deconvolution`, `Affine`.
  
  Examples:
  
  .. code-block:: python
  
     pred = Model(...)
  
     import nnabla.experimental.graph_converters as GC
  
     modifiers = [GC.AddBiasModifier()]
     gc = GC.GraphConverter(modifiers)
     pred = gc.convert(pred)
class_docstr_tok: ["Add","bias","to","`","Convolution","`","in","BatchNormalization","folding","case","if","it","does","n't","have","bias",".","Supported","folding","functions",":","`","Convolution","`",",","`","Deconvolution","`",",","`","Affine","`",".","Examples",":","..","code-block",":",":","python","pred","=","Model","(","...",")","import","nnabla.experimental.graph_converters","as","GC","modifiers","=","[","GC.AddBiasModifier","(",")","]","gc","=","GC.GraphConverter","(","modifiers",")","pred","=","gc.convert","(","pred",")"]
code: def modify(self, f, inputs):
      fname = f.info.type_name
      if not fname in self._fct_set:
          return
  
      # Next or Previous func is not BatchNorm
      next_func = f.outputs[0].function_references[0]
      prev_func = f.inputs[0].parent
      if (
          prev_func == None
          or prev_func.info.type_name != "BatchNormalization"
      ) and next_func.info.type_name != "BatchNormalization":
          return
  
      x = inputs[0]
      w = inputs[1]
      b = inputs[2] if len(inputs) == 3 else None
  
      if b is not None:
          return
  
      scope = self.get_parameter_scope(w)
      n_outmaps = w.shape[1] if fname == "Affine" else w.shape[0]
      with nn.parameter_scope(scope):
          b = get_parameter_or_create(
              "b", (n_outmaps,), ConstantInitializer(), True, True
          )
      h = self.connect(f, x, w, b)
      return h
  
imports: import nnabla
  import nnabla.functions
  import numpy
  from nnabla.initializer import ConstantInitializer
  from nnabla.parameter import get_parameter_or_create
  from .graph_converter import FunctionModifier
label_desc: Use image node_id 2 for calling the AddBiasModifier obj's underlying member method code with example usage: obj.modify(f, inputs) and returns: h