def dlogpdf_dlink(self, link_f, y, Y_metadata=None):
return y/link_f - <NUM_LIT:1><EOL>
Gradient of the log likelihood function at y, given link(f) w.r.t link(f)

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

:param link_f: latent variables (f)
:type link_f: Nx1 array
:param y: data
:type y: Nx1 array
:param Y_metadata: Y_metadata which is not used in poisson distribution
:returns: gradient of likelihood evaluated at points
:rtype: Nx1 array