code: def predict(self, X, nsamples=200, likelihood_args=()):
          
          Ey, _ = self.predict_moments(X, nsamples, likelihood_args)
  
          return Ey
docs: Predict target values from Bayesian generalized linear regression.
  
          Parameters
          ----------
          X : ndarray
              (N*,d) array query input dataset (N* samples, d dimensions).
          nsamples : int, optional
              Number of samples for sampling the expected target values from the
              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
          -------
          Ey : ndarray
              The expected value of y* for the query inputs, X* of shape (N*,).