def METHOD_NAME(conv_w, conv_b, bn_rm, bn_rv, bn_eps, bn_w, bn_b):
    '''fuse weights and bias of conv and bn'''
    if conv_b is None:
        conv_b = paddle.zeros_like(bn_rm)
    if bn_w is None:
        bn_w = paddle.ones_like(bn_rm)
    if bn_b is None:
        bn_b = paddle.zeros_like(bn_rm)
    bn_var_rsqrt = paddle.rsqrt(bn_rv + bn_eps)
    conv_w = conv_w * \
        (bn_w * bn_var_rsqrt).reshape([-1] + [1] * (len(conv_w.shape) - 1))
    conv_b = (conv_b - bn_rm) * bn_var_rsqrt * bn_w + bn_b
    return conv_w, conv_b