def METHOD_NAME(self, benchmark, drop, shared_dist_ctx, lt_ctx, context, chunked_emd):
    if context == 'dist':
        ctx = shared_dist_ctx
    elif context == 'inline':
        ctx = lt_ctx
    else:
        raise ValueError
    ds = chunked_emd
    def mask():
        return np.ones(ds.shape.sig, dtype=bool)
    udf = ApplyMasksUDF(mask_factories=[mask], backends=('numpy', ))
    # warmup executor
    ctx.run_udf(udf=udf, dataset=ds)
    if drop == "cold_cache":
        drop_cache([ds.path])
    elif drop == "warm_cache":
        warmup_cache([ds.path])
    else:
        raise ValueError("bad param")
    benchmark.pedantic(
        ctx.run_udf, kwargs=dict(udf=udf, dataset=ds),
        warmup_rounds=0,
        rounds=1,
        iterations=1
    )