def METHOD_NAME():
    # Load a resnet model.
    mod, params = get_workload()
    # Apply flexible dispatch pass.
    mod = relay.transform.FlexibleShapeDispatch(axis=0, buckets=[1, 4], auto_pad=True)(mod)
    # Compile and confirm result supports multiple shapes.
    exe = relay.vm.compile(mod, "llvm", params=params)
    vm = runtime.vm.VirtualMachine(exe, tvm.cpu())
    # Evaluate various batch sizes
    batch_1 = np.random.normal(size=[1, 3, 224, 224]).astype("float32")
    assert list(vm.invoke("main", batch_1).shape) == [1, 1000]
    batch_4 = np.random.normal(size=[4, 3, 224, 224]).astype("float32")
    assert list(vm.invoke("main", batch_4).shape) == [4, 1000]
    # Apply autopadding to an input.
    batch_3 = np.random.normal(size=[3, 3, 224, 224]).astype("float32")
    assert list(vm.invoke("main", batch_3).shape) == [3, 1000]