code: def df(self, y, f, var):
          r
          var = self._check_param(var)
          y, f = np.broadcast_arrays(y, f)
          return (y - f) / var
docs: r"""
          Derivative of Gaussian log likelihood w.r.t.\  f.
  
          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
          -------
          df: ndarray
              the derivative :math:`\partial \log p(y|f) / \partial f`