code: def loglike(self, y, f, var=None):
          r
          # way faster than calling norm.logpdf
          var = self._check_param(var)
          y, f = np.broadcast_arrays(y, f)
          ll = - 0.5 * (np.log(2 * np.pi * var) + (y - f)**2 / var)
          return ll
docs: r"""
          Gaussian log likelihood.
  
          Parameters
          ----------
          y: ndarray
              array of 0, 1 valued integers of targets
          f: ndarray
              latent function from the GLM prior (:math:`\mathbf{f} =
              \boldsymbol\Phi \mathbf{w}`)
          var: float, ndarray, optional
              The variance of the distribution, if not input, the initial value
              of variance is used.
  
          Returns
          -------
          logp: ndarray
              the log likelihood of each y given each f under this
              likelihood.