code: def dlogpdf_dlink(self, link_f, y, Y_metadata=None):
          
          # c = Y_metadata['censored']
          # for debugging
          # c = np.zeros((y.shape[0],))
          c = np.zeros_like(link_f)
  
          if Y_metadata is not None and 'censored' in Y_metadata.keys():
              c = Y_metadata['censored']
  
          #y_link_f = y/link_f
          #y_link_f_r = y_link_f**self.r
          y_link_f_r = np.clip(y**self.r, 1e-150, 1e200) / np.clip(link_f**self.r, 1e-150, 1e200)
  
          #In terms of link_f
          # uncensored = (1-c)*( (2*self.r*y**r)/(link_f**self.r + y**self.r) - link_f*self.r)
          uncensored = (1-c)*self.r*(y_link_f_r - 1)/(link_f*(1 + y_link_f_r))
          censored = c*(self.r*y_link_f_r/(link_f*y_link_f_r + link_f))
          return uncensored + censored
docs: Gradient of the log likelihood function at y, given link(f) w.r.t link(f)
  
          .. math::
  
          :param link_f: latent variables (f)
          :type link_f: Nx1 array
          :param y: data
          :type y: Nx1 array
          :param Y_metadata: includes censoring information in dictionary key 'censored'
          :returns: gradient of likelihood evaluated at points
          :rtype: Nx1 array