def METHOD_NAME(self, input, num_filters, stride, is_first, name):
    stride1, stride2 = 1, stride
    conv_def = [[num_filters, 1, stride1, 'relu', name + "_branch2a"],
                [num_filters, 3, stride2, 'relu', name + "_branch2b"],
                [num_filters * 4, 1, 1, None, name + "_branch2c"]]
    residual = input
    for (c, k, s, act, _name) in conv_def:
        residual = self._conv_norm(
            input=residual,
            num_filters=c,
            filter_size=k,
            stride=s,
            act=act,
            name=_name)
    short = self._shortcut(
        input,
        num_filters * 4,
        stride,
        is_first=is_first,
        name=name + "_branch1")
    return fluid.layers.elementwise_add(
        x=short, y=residual, act='relu', name=name + ".add.output.5")