code: def pdf_link(self, link_f, y, Y_metadata=None):
          
          assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
          return np.exp(self.logpdf_link(link_f, y, Y_metadata))
docs: Likelihood function given link(f)
  
          .. math::
              p(y_{i}|\\lambda(f_{i})) = \\frac{\\lambda(f_{i})^{y_{i}}}{y_{i}!}e^{-\\lambda(f_{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 poisson distribution
          :returns: likelihood evaluated for this point
          :rtype: float