def d2logpdf_dlink2(self, link_f, y, Y_metadata=None):
<EOL>c = np.zeros_like(y)<EOL>if Y_metadata is not None and '<STR_LIT>' in Y_metadata.keys():<EOL><INDENT>c = Y_metadata['<STR_LIT>']<EOL><DEDENT>uncensored = (<NUM_LIT:1>-c)*(<NUM_LIT:1>/link_f**<NUM_LIT:2> -<NUM_LIT:2>*y**self.r/link_f**<NUM_LIT:3>)<EOL>censored = -c*<NUM_LIT:2>*y**self.r/link_f**<NUM_LIT:3><EOL>hess = uncensored + censored<EOL>return hess<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)} = -\\beta^{2}\\frac{d\\Psi(\\alpha_{i})}{d\\alpha_{i}}\\\\
    \\alpha_{i} = \\beta y_{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 which is not used in gamma 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))