为什么类型注释会使Python代码变慢

这是我ipython年的测试结果.

int美元:

In [2]: %time for _  in range(1000): exec('a: int = 4')                                                                                                                         
CPU times: user 12.2 ms, sys: 12 µs, total: 12.2 ms
Wall time: 12.2 ms

In [3]: %time for _  in range(1000): exec('a = 4')                                                                                                                              
CPU times: user 9.5 ms, sys: 0 ns, total: 9.5 ms
Wall time: 9.54 ms

str美元:

In [4]: %time for _  in range(1000): exec('a: str = "hello"')                                                                                                                   
CPU times: user 13.3 ms, sys: 0 ns, total: 13.3 ms
Wall time: 13.4 ms

In [5]: %time for _  in range(1000): exec('a = "hello"')                                                                                                                        
CPU times: user 10.4 ms, sys: 0 ns, total: 10.4 ms
Wall time: 10.4 ms

list美元:

In [6]: %time for _  in range(1000): exec('a: list = [1,2, "hello"]')                                                                                                           
CPU times: user 19.1 ms, sys: 0 ns, total: 19.1 ms
Wall time: 21.5 ms

In [7]: %time for _  in range(1000): exec('a = [1,2, "hello"]')                                                                                                                 
CPU times: user 15.8 ms, sys: 0 ns, total: 15.8 ms
Wall time: 15.8 ms

我知道理论上list或int个注释应该没有任何区别，但它们没有任何功能.但我只是测试了这些类型，以确保使用类型提示会将执行速度降低约25%.这是为什么？据我所知，类型提示与执行无关.只是花更多的时间来解析它们，并将它们添加到__annotations__字典中，就可以在执行时间上产生如此巨大的差异？