code: def _build_likelihood(self):
          r
          K = self.kern.K(self.X)
          L = tf.cholesky(
              K + tf.eye(tf.shape(self.X)[0], dtype=settings.float_type) * settings.numerics.jitter_level)
          F = tf.matmul(L, self.V) + self.mean_function(self.X)
  
          return tf.reduce_sum(self.likelihood.logp(F, self.Y))
docs: r"""
          Construct a tf function to compute the likelihood of a general GP
          model.
  
              \log p(Y, V | theta).