class_docstr: Single `Convolution -> BatchNormalization` pass is folded into one `Convolution`.
  
  If there is a `Convolution -> BatchNormalization` pass,
  fold the batch normalization parameters to the kernel
  and bias (if it exists) of the preceding convolution,
  then skip the batch normalization following the convolution.
  
  Supported folding functions: `Convolution`, `Deconvolution`, `Affine`.
  
  Examples:
  
  .. code-block:: python
  
     pred = Model(...)
  
     import nnabla.experimental.graph_converters as GC
  
     modifiers = [GC.BatchNormalizationFoldingModifier()]
     gc = GC.GraphConverter(modifiers)
     pred = gc.convert(pred)
class_docstr_tok: ["Single","`","Convolution","-",">","BatchNormalization","`","pass","is","folded","into","one","`","Convolution","`",".","If","there","is","a","`","Convolution","-",">","BatchNormalization","`","pass",",","fold","the","batch","normalization","parameters","to","the","kernel","and","bias","(","if","it","exists",")","of","the","preceding","convolution",",","then","skip","the","batch","normalization","following","the","convolution",".","Supported","folding","functions",":","`","Convolution","`",",","`","Deconvolution","`",",","`","Affine","`",".","Examples",":","..","code-block",":",":","python","pred","=","Model","(","...",")","import","nnabla.experimental.graph_converters","as","GC","modifiers","=","[","GC.BatchNormalizationFoldingModifier","(",")","]","gc","=","GC.GraphConverter","(","modifiers",")","pred","=","gc.convert","(","pred",")"]
code: def __new__(self, opposite=False, channel_last=False):
      modifiers = [AddBiasModifier()]
      if not opposite:
          modifiers.append(
              BatchNormalizationFoldingModifierInner(
                  channel_last=channel_last
              )
          )
      else:
          modifiers.append(
              BatchNormalizationFoldingOppositeModifierInner(
                  channel_last=channel_last
              )
          )
      return modifiers
  
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 1 for calling the BatchNormalizationFoldingModifier obj's underlying member method code with example usage: obj.__new__(opposite, channel_last) and returns: modifiers