def METHOD_NAME():
    batch_size = 1
    input_channel = 3
    h = 224
    w = 224
    output_channel = 64
    kh = 7
    kw = 7
    h_padding = 1
    w_padding = 1
    oh = h + h_padding * 2 - kh + 1
    ow = w + w_padding * 2 - kw + 1
    dshape = (batch_size, input_channel, h, w)
    weight = relay.var("weight", shape=(output_channel, input_channel, kh, kw))
    data = relay.var("data", shape=dshape)
    conv2d = relay.nn.conv2d(
        data, weight, channels=output_channel, kernel_size=(kh, kw), padding=(h_padding, w_padding)
    )
    func = relay.Function([data, weight], relay.Tuple(tvm.runtime.convert([conv2d])))
    func = run_opt_pass(func, transform.InferType())
    compute_count = analysis.get_total_mac_number(func)
    expect_count = batch_size * input_channel * oh * ow * output_channel * kh * kw
    assert compute_count == expect_count