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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def predict_cdf(self, X, quantile, nsamples=200, likelihood_args=()):
 r""" Predictive cumulative density function of a Bayesian GLM. Parameters X : ndarray (N*,d) array query input dataset (N* samples, D dimensions). quantile : float The predictive probability, :math:`p(y^* \leq \text{quantile} | \mathbf{x}^*, \mathbf{X}, y)`. nsamples : int, optional Number of samples for sampling the predictive CDF. likelihood_args : sequence, optional sequence of arguments to pass to the likelihood function. These are non-learnable parameters. They can be scalars or arrays of length N*. nsamples : int, optional The number of samples to draw from the posterior in order to approximate the predictive mean and variance. Returns ------- p : ndarray The probability of y* <= quantile for the query inputs, X* of shape (N*,). p_min : ndarray The minimum sampled values of the predicted probability (same shape as p) p_max : ndarray The maximum sampled values of the predicted probability (same shape as p) """

        # Get latent function samples
        N = X.shape[0]
        ps = np.empty((N, nsamples))
        fsamples = self._sample_func(X, nsamples)

        # Push samples though likelihood cdf
        cdfarg = tuple(chain(atleast_list(self.like_hypers_), likelihood_args))
        for i, f in enumerate(fsamples):
            ps[:, i] = self.likelihood.cdf(quantile, f, *cdfarg)

        # Average transformed samples (MC integration)
        p = ps.mean(axis=1)
        p_min = ps.min(axis=1)
        p_max = ps.max(axis=1)

        return p, p_min, p_max