@composite和flatmap。据我所知，前者可以做任何事，后者可以做。但是，numpy arrays策略的implementation谈到了一些隐性成本
# We support passing strategies as arguments for convenience,or at least
# for legacy reasons,but don't want to pay the perf cost of a composite
# strategy (i.e. repeated argument handling and validation) when it's not
# needed. So we get the best of both worlds by recursing with flatmap,# but only when it's actually needed.
表示收缩行为更糟，但是我不确定，并且在其他任何地方也找不到此文件。因此，什么时候应该使用@composite，什么时候flatmap以及什么时候应该像上面链接的实现中那样走这条中间路线？