def METHOD_NAME():
    batch_size = 1
    dshape = (batch_size, 64, 56, 56)
    weight_conv = relay.var("weight_conv", shape=(64, 64, 3, 3))
    data1 = relay.var("data1", shape=dshape)
    data2 = relay.var("data2", shape=dshape)
    weight_dense = relay.var("weight_dense", shape=(1, 56 * 56 * 64))
    conv2d_1 = relay.nn.conv2d(data1, weight_conv, channels=64, kernel_size=(3, 3), padding=(1, 1))
    conv2d_2 = relay.nn.conv2d(data2, weight_conv, channels=64, kernel_size=(3, 3), padding=(1, 1))
    add = relay.add(conv2d_1, conv2d_2)
    flattened = relay.nn.batch_flatten(add)
    dense_1 = relay.nn.dense(flattened, weight_dense)
    func = relay.Function(
        [data1, data2, weight_conv, weight_dense],
        relay.Tuple(tvm.runtime.convert([conv2d_1, conv2d_2, dense_1, add, flattened])),
    )
    # alter the CONV 2D data layout to test
    func = run_opt_pass(func, transform.AlterOpLayout())
    compute_count = analysis.get_total_mac_number(func)
    expect_count = 231411712
    assert compute_count == expect_count