def METHOD_NAME(self):
    if hasattr(self, 'rbr_reparam'):
        return
    kernel, bias = self.get_equivalent_kernel_bias()
    self.rbr_reparam = nn.Conv2d(
        in_channels=self.rbr_dense.conv.in_channels,
        out_channels=self.rbr_dense.conv.out_channels,
        kernel_size=self.rbr_dense.conv.kernel_size,
        stride=self.rbr_dense.conv.stride,
        padding=self.rbr_dense.conv.padding,
        dilation=self.rbr_dense.conv.dilation,
        groups=self.rbr_dense.conv.groups,
        bias=True)
    self.rbr_reparam.weight.data = kernel
    self.rbr_reparam.bias.data = bias
    for para in self.parameters():
        para.detach_()
    self.__delattr__('rbr_dense')
    self.__delattr__('rbr_1x1')
    if hasattr(self, 'rbr_identity'):
        self.__delattr__('rbr_identity')
    if hasattr(self, 'id_tensor'):
        self.__delattr__('id_tensor')
    self.deploy = True