def METHOD_NAME():
    def verify_sort(shape, axis, is_ascend, is_dyn=False, in_dtype="float32"):
        if is_dyn:
            x = relay.var("x", relay.TensorType([relay.Any()] * len(shape), in_dtype))
        else:
            x = relay.var("x", relay.TensorType(shape, in_dtype))
        z = relay.sort(x, axis=axis, is_ascend=is_ascend)
        func = relay.Function([x], z)
        x_data = np.random.uniform(size=shape).astype(in_dtype)
        if is_ascend:
            ref_res = np.sort(x_data, axis=axis)
        else:
            ref_res = -np.sort(-x_data, axis=axis)
        if is_dyn:
            backend = "vm"
        else:
            backend = "graph"
        for target, dev in tvm.testing.enabled_targets():
            mod = tvm.ir.IRModule.from_expr(func)
            op_res = relay.create_executor(backend, mod=mod, device=dev, target=target).evaluate()(
                x_data
            )
            tvm.testing.assert_allclose(op_res.numpy(), ref_res, rtol=1e-5)
    for is_dyn in [False, True]:
        verify_sort((2, 3, 4), axis=0, is_ascend=False, is_dyn=is_dyn)
        verify_sort((1, 4, 6), axis=1, is_ascend=True, is_dyn=is_dyn)
        verify_sort((3, 5, 6), axis=-1, is_ascend=False, is_dyn=is_dyn)
        verify_sort((3, 2000, 6), axis=1, is_ascend=False, is_dyn=is_dyn)
        verify_sort((1, 122640), axis=1, is_ascend=False, is_dyn=is_dyn)
        verify_sort((1, 122640), axis=1, is_ascend=False, is_dyn=is_dyn, in_dtype="float16")