b0ddc6d1632f63632583ea70caca678fce3696f3,gpflow/models/model.py,BayesianModel,log_prior,#BayesianModel#,39

Before Change

        return -(self.log_likelihood(*args, **kwargs) + self.log_prior())

    def log_prior(self) -> tf.Tensor:
        if len(self.variables) == 0:
            return tf.convert_to_tensor(0., dtype=default_float())
        return tf.add_n([p.log_prior() for p in self.trainable_parameters])

    @abc.abstractmethod
    def log_likelihood(self, *args, **kwargs) -> tf.Tensor:

After Change

    def log_prior(self) -> tf.Tensor:
        log_priors = [p.log_prior() for p in self.trainable_parameters]
        if log_priors:
            return tf.add_n(log_priors)
        else:
            return tf.convert_to_tensor(0., dtype=default_float())

    @abc.abstractmethod

Instances