def d2logpdf_dlink2(self, link_f, y, Y_metadata=None):
<EOL>c = np.zeros_like(link_f)<EOL>if Y_metadata is not None and '<STR_LIT>' in Y_metadata.keys():<EOL><INDENT>c = Y_metadata['<STR_LIT>']<EOL><DEDENT>y_link_f = y/link_f<EOL>y_link_f_r = y_link_f**self.r<EOL>censored = c*(-self.r*y_link_f_r*(y_link_f_r + self.r + <NUM_LIT:1>)/((link_f**<NUM_LIT:2>)*(y_link_f_r + <NUM_LIT:1>)**<NUM_LIT:2>))<EOL>uncensored = (<NUM_LIT:1>-c)*(-self.r*(<NUM_LIT:2>*self.r*y_link_f_r + y_link_f**(<NUM_LIT:2>*self.r) - <NUM_LIT:1>) / ((link_f**<NUM_LIT:2>)*(<NUM_LIT:1>+ y_link_f_r)**<NUM_LIT:2>))<EOL>hess = censored + uncensored<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::


:param link_f: latent variables link(f)
:type link_f: Nx1 array
:param y: data
:type y: Nx1 array
:param Y_metadata: includes censoring information in dictionary key 'censored'
: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))