def METHOD_NAME():
    batch_size = 1
    dshape = (batch_size, 64, 56, 56)
    weight_conv = relay.var("weight_depthwiseconv", shape=(64, 1, 3, 3))
    data1 = relay.var("data1", shape=dshape)
    data2 = relay.var("data2", shape=dshape)
    depthwise_conv2d_1 = relay.nn.conv2d(
        data1, weight_conv, kernel_size=(3, 3), padding=(1, 1), groups=64
    )
    depthwise_conv2d_2 = relay.nn.conv2d(
        data2, weight_conv, kernel_size=(3, 3), padding=(1, 1), groups=64
    )
    add = relay.add(depthwise_conv2d_1, depthwise_conv2d_2)
    func = relay.Function(
        [data1, data2, weight_conv],
        relay.Tuple(tvm.runtime.convert([depthwise_conv2d_1, depthwise_conv2d_2, add])),
    )
    func = run_opt_pass(func, transform.InferType())
    compute_count = analysis.get_total_mac_number(func)
    assert compute_count == 2 * np.prod(dshape) * 3 * 3