async def METHOD_NAME(c, s, a, b):
    df = pd.DataFrame(
        {"x": np.random.random(1000), "y": np.random.random(1000)},
        index=np.arange(1000),
    )
    ldf = dd.from_pandas(df, npartitions=10)
    rdf = await c.persist(ldf)
    assert rdf.divisions == ldf.divisions
    remote = c.compute(rdf)
    result = await remote
    assert_frame_equal(result, ldf.compute(scheduler="sync"))
    exprs = [
        lambda df: df.x.mean(),
        lambda df: df.y.std(),
        lambda df: df.assign(z=df.x + df.y).drop_duplicates(),
        lambda df: df.index,
        lambda df: df.x,
        lambda df: df.x.cumsum(),
        lambda df: df.groupby(["x", "y"]).count(),
        lambda df: df.loc[50:75],
    ]
    for f in exprs:
        local = f(ldf).compute(scheduler="sync")
        remote = c.compute(f(rdf))
        remote = await remote
        assert_equal(local, remote)