on_info >= (3, 9):
    def sample(population: Union[Sequence[_T], AbstractSet[_T]], k: int, *, counts: Optional[Iterable[_T]] = ...) -> List[_T]: ...

else:
    def sample(population: Union[Sequence[_T], AbstractSet[_T]], k: int) -> List[_T]: ...

def random() -> float: ...
def uniform(a: float, b: float) -> float: ...
def triangular(low: float = ..., high: float = ..., mode: Optional[float] = ...) -> float: ...
def betavariate(alpha: float, beta: float) -> float: ...
def expovariate(lambd: float) -> float: ...
def gammavariate(alpha: float, beta: float) -> float: ...
def gauss(mu: float, sigma: float) -> float: ...
def lognormvariate(mu: float, sigma: float) -> float: ...
def normalvariate(mu: float, sigma: float) -> float: ...
def vonmisesvariate(mu: float, kappa: float) -> float: ...
def paretovariate(alpha: float) -> float: ...
def weibullvariate(alpha: float, beta: float) -> float: ...
PK