class_docstr: Single `BatchNormalization -> Convolution` pass is folded into one `Convolution`.
  
  If there is a `BatchNormalization -> Convolution` 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`.
class_docstr_tok: ["Single","`","BatchNormalization","-",">","Convolution","`","pass","is","folded","into","one","`","Convolution","`",".","If","there","is","a","`","BatchNormalization","-",">","Convolution","`","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","`","."]
code: def __init__(self, channel_last=False):
      super(
          BatchNormalizationFoldingOppositeModifierInner, self
      ).__init__()
      self._channel_last = channel_last
  
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 to create a new BatchNormalizationFoldingOppositeModifierInner object from inherited base classes: FunctionModifier, BatchNormBase with example: obj = BatchNormalizationFoldingOppositeModifierInner(channel_last)