code: def predict_logpdf(self, X, y, nsamples=200, likelihood_args=()):
          r
          X, y = check_X_y(X, y)
  
          # Get latent function samples
          N = X.shape[0]
          ps = np.empty((N, nsamples))
          fsamples = self._sample_func(X, nsamples)
  
          # Push samples though likelihood pdf
          llargs = tuple(chain(atleast_list(self.like_hypers_), likelihood_args))
          for i, f in enumerate(fsamples):
              ps[:, i] = self.likelihood.loglike(y, f, *llargs)
  
          # Average transformed samples (MC integration)
          logp = ps.mean(axis=1)
          logp_min = ps.min(axis=1)
          logp_max = ps.max(axis=1)
  
          return logp, logp_min, logp_max
docs: r"""
          Predictive log-probability density function of a Bayesian GLM.
  
          Parameters
          ----------
          X : ndarray
              (N*,d) array query input dataset (N* samples, D dimensions).
          y : float or ndarray
              The test observations of shape (N*,) to evaluate under,
              :math:`\log p(y^* |\mathbf{x}^*, \mathbf{X}, y)`.
          nsamples : int, optional
              Number of samples for sampling the log predictive distribution.
          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*.
  
          Returns
          -------
          logp : ndarray
             The log probability of y* given X* of shape (N*,).
          logp_min : ndarray
              The minimum sampled values of the predicted log probability (same
              shape as p)
          logp_max : ndarray
              The maximum sampled values of the predicted log probability (same
              shape as p)