An asynchronous operation (created via std::async, std::packaged_task, or std::promise) can provide a std::future object to the creator of that asynchronous operation The creator of the asynchronous operation can then use a variety of methods to query, wait for, or extract a value from the std. Checks if the future refers to a shared state Returned by std::promise::get_future (), std::packaged_task::get_future () or std::async ()) until the first time get () or share () is called If the future is the result of a call to std::async that used lazy evaluation, this function returns immediately without waiting This function may block for longer than timeout_duration due to scheduling or resource contention delays
The standard recommends that a steady clock is used to measure the duration. The get member function waits (by calling wait ()) until the shared state is ready, then retrieves the value stored in the shared state (if any) Right after calling this function, valid () is false If valid () is false before the call to this function, the behavior is undefined. Int64 if i understand the warning correctly, the object dtype is downcast to int64 Perhaps pandas wants me to do this explicitly, but i don't see how i could downcast a string to a numerical type before the replacement happens.
Access to the same shared state from multiple threads is safe if each thread does it through its own copy of a shared_future object. If the future is the result of a call to async that used lazy evaluation, this function returns immediately without waiting The behavior is undefined if valid () is false before the call to this function, or clock does not meet the clock requirements Specifies state of a future as returned by wait_for and wait_until functions of std::future and std::shared_future A future statement is a directive to the compiler that a particular module should be compiled using syntax or semantics that will be available in a specified future release of python The future statement is intended to ease migration to future versions of python that introduce incompatible changes to the language
In this case it does work In general, it probably doesn't I'm wondering how this break in backwards compatibility should in general be navigated Perhaps installing a previous version of cmake is the only way that always works That would mean that each project in the future should specify the cmake version on which it should be built.
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