code: def like(self, x, y):
          
          # This is the negative log-likelihood
          z = self._lnlfn.interp(x * y)
          return np.exp(-z) * self._nuis_pdf(y) / self._nuis_norm
docs: Evaluate the 2-D 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.