How to use the pymc3.distributions.distribution.generate_samples function in pymc3

def random(self, point=None, size=None, repeat=None):
        alpha, m = draw_values([self.alpha, self.m],
                               point=point)
        return generate_samples(self._random, alpha, m,
                                dist_shape=self.shape,
                                size=size)

Parameters
        ----------
        point : dict, optional
            Dict of variable values on which random values are to be
            conditioned (uses default point if not specified).
        size : int, optional
            Desired size of random sample (returns one sample if not
            specified).

        Returns
        -------
        array
        """
        mu, alpha, psi = draw_values(
            [self.mu, self.alpha, self.psi], point=point, size=size)
        g = generate_samples(
            self._random,
            mu=mu,
            alpha=alpha,
            dist_shape=self.shape,
            size=size
        )
        g[g == 0] = np.finfo(float).eps  # Just in case
        g, psi = broadcast_distribution_samples([g, psi], size=size)
        return stats.poisson.rvs(g) * (np.random.random(g.shape) < psi)

Parameters
        ----------
        point : dict, optional
            Dict of variable values on which random values are to be
            conditioned (uses default point if not specified).
        size : int, optional
            Desired size of random sample (returns one sample if not
            specified).

        Returns
        -------
        array
        """
        n, p, psi = draw_values([self.n, self.p, self.psi], point=point, size=size)
        g = generate_samples(stats.binom.rvs, n, p,
                             dist_shape=self.shape,
                             size=size)
        g, psi = broadcast_distribution_samples([g, psi], size=size)
        return g * (np.random.random(g.shape) < psi)

def random(self, point=None, size=None, repeat=None):
        n, p = draw_values([self.n, self.p], point=point)
        return generate_samples(st.binom.rvs, n=n, p=p,
                                dist_shape=self.shape,
                                size=size)

----------
        point : dict, optional
            Dict of variable values on which random values are to be
            conditioned (uses default point if not specified).
        size : int, optional
            Desired size of random sample (returns one sample if not
            specified).

        Returns
        -------
        array
        """

        lower, upper = draw_values([self.lower, self.upper],
                                   point=point, size=size)
        return generate_samples(stats.uniform.rvs, loc=lower,
                                scale=upper - lower,
                                dist_shape=self.shape,
                                size=size)

----------
        point : dict, optional
            Dict of variable values on which random values are to be
            conditioned (uses default point if not specified).
        size : int, optional
            Desired size of random sample (returns one sample if not
            specified).

        Returns
        -------
        array
        """
        alpha, m = draw_values([self.alpha, self.m],
                               point=point, size=size)
        alpha, m = broadcast_distribution_samples([alpha, m], size=size)
        return generate_samples(self._random, alpha, m,
                                dist_shape=self.shape,
                                size=size)

def random(self, point=None, size=None, repeat=None):
        nu, mu, lam = draw_values([self.nu, self.mu, self.lam],
                                  point=point)
        return generate_samples(stats.t.rvs, nu, loc=mu, scale=lam**-0.5,
                                dist_shape=self.shape,
                                size=size)

Parameters
        ----------
        point : dict, optional
            Dict of variable values on which random values are to be
            conditioned (uses default point if not specified).
        size : int, optional
            Desired size of random sample (returns one sample if not
            specified).

        Returns
        -------
        array
        """
        beta = draw_values([self.beta], point=point, size=size)[0]
        return generate_samples(self._random, beta,
                                dist_shape=self.shape,
                                size=size)

def random(self, point=None, size=None, repeat=None):
        mu, lam, alpha = draw_values([self.mu, self.lam, self.alpha],
                                     point=point)
        return generate_samples(self._random,
                                mu, lam, alpha,
                                dist_shape=self.shape,
                                size=size)

Parameters
        ----------
        point : dict, optional
            Dict of variable values on which random values are to be
            conditioned (uses default point if not specified).
        size : int, optional
            Desired size of random sample (returns one sample if not
            specified).

        Returns
        -------
        array
        """
        a = draw_values([self.a], point=point, size=size)[0]
        samples = generate_samples(self._random,
                                   a=a,
                                   dist_shape=self.shape,
                                   size=size)
        return samples