class Random(_random.Random):

　　## -------------------- integer methods -------------------

　　def randrange(self, start, stop=None, step=1, _int=int):

　　def randint(self, a, b):

　　def _randbelow(self, n, int=int, maxsize=1 << BPF, type=type,

　　Method=_MethodType, BuiltinMethod=_BuiltinMethodType):

　　## -------------------- sequence methods -------------------

　　def choice(self, seq):

　　def shuffle(self, x, random=None):

　　def sample(self, population, k):

　　def choices(self, population, weights=None, *, cum_weights=None, k=1):

　　## -------------------- uniform distribution -------------------

　　def uniform(self, a, b):

　　## -------------------- triangular --------------------

　　def triangular(self, low=0.0, high=1.0, mode=None):

　　## -------------------- normal distribution --------------------

　　def normalvariate(self, mu, sigma):

　　## -------------------- lognormal distribution --------------------

　　def lognormvariate(self, mu, sigma):

　　## -------------------- exponential distribution --------------------

　　def expovariate(self, lambd):

　　## -------------------- von Mises distribution --------------------

　　def vonmisesvariate(self, mu, kappa):

　　## -------------------- gamma distribution --------------------

　　def gammavariate(self, alpha, beta):

　　## -------------------- Gauss (faster alternative) --------------------

　　def gauss(self, mu, sigma):

　　def betavariate(self, alpha, beta):

　　## -------------------- Pareto --------------------

　　def paretovariate(self, alpha):

　　## -------------------- Weibull --------------------

　　def weibullvariate(self, alpha, beta):