def dlogpdf_dlink(self, link_f, y, Y_metadata=None):
s2_i = <NUM_LIT:1.0>/self.variance<EOL>grad = s2_i*y - s2_i*link_f<EOL>return grad<EOL>
Gradient of the pdf at y, given link(f) w.r.t link(f)

.. math::
    \\frac{d \\ln p(y_{i}|\\lambda(f_{i}))}{d\\lambda(f)} = \\frac{1}{\\sigma^{2}}(y_{i} - \\lambda(f_{i}))

:param link_f: latent variables link(f)
:type link_f: Nx1 array
:param y: data
:type y: Nx1 array
:param Y_metadata: Y_metadata not used in gaussian
:returns: gradient of log likelihood evaluated at points link(f)
:rtype: Nx1 array