code: def pdf_link(self, link_f, y, Y_metadata=None):
          
          assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
          c = np.zeros((link_f.shape[0],))
  
          # log_objective = np.log(self.r) + (self.r - 1) * np.log(y) - link_f - (np.exp(-link_f) * (y ** self.r))
          # log_objective = stats.weibull_min.pdf(y,c=self.beta,loc=link_f,scale=1.)
          log_objective = self.logpdf_link(link_f, y, Y_metadata)
          return np.exp(log_objective)
docs: Likelihood function given link(f)
  
  
          :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 weibull distribution
          :returns: likelihood evaluated for this point
          :rtype: float