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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _build_likelihood(self):
 """ This gives a variational bound on the model likelihood. """


        # Get prior KL.
        KL = self.build_prior_KL()

        # Get conditionals
        fmean, fvar = self._build_predict(self.X, full_cov=False, full_output_cov=False)

        # Get variational expectations.
        var_exp = self.likelihood.variational_expectations(fmean, fvar, self.Y)

        # re-scale for minibatch size
        scale = tf.cast(self.num_data, settings.float_type) / tf.cast(tf.shape(self.X)[0], settings.float_type)

        return tf.reduce_sum(var_exp) * scale - KL