<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def df(self, y, f, var):
 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` """

        var = self._check_param(var)
        y, f = np.broadcast_arrays(y, f)
        return (y - f) / var