code: def loglike(self, x, y):
          
          nuis = self._nuis_pdf(y)
          log_nuis = np.where( nuis > 0., np.log(nuis), -1e2)
          vals = -self._lnlfn.interp(x * y ) + \
              log_nuis - \
              self._nuis_log_norm
          return vals
docs: Evaluate the 2-D log-likelihood in the x/y parameter space.
  
          The dimension of the two input arrays should be the same.
  
          Parameters
          ----------
          x : array_like
              Array of coordinates in the `x` parameter.
  
          y : array_like       
              Array of coordinates in the `y` nuisance parameter.