def d2logpdf_dlink2(self, link_f, y, Y_metadata=None):
return -y/(link_f**<NUM_LIT:2>)<EOL>
Hessian at y, given link(f), w.r.t link(f)
i.e. second derivative logpdf at y given link(f_i) and link(f_j)  w.r.t link(f_i) and link(f_j)
The hessian will be 0 unless i == j

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

:param link_f: latent variables link(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: Diagonal of hessian matrix (second derivative of likelihood evaluated at points f)
:rtype: Nx1 array

.. Note::
    Will return diagonal of hessian, since every where else it is 0, as the likelihood factorizes over cases
    (the distribution for y_i depends only on link(f_i) not on link(f_(j!=i))