Given a Normal distribution for the latent function, and a datum Y, compute the log predictive density of Y,

    i.e. if
        q(F) = N(Fmu, Fvar)

    and this object represents

        p(y|F)

    then this method computes the predictive density

        log ∫ p(y=Y|F)q(F) df

    :param X: input tensor
    :param Fmu: mean function evaluation tensor
    :param Fvar: variance of function evaluation tensor
    :param Y: observation tensor
    :returns: log predictive density

    def _predict_log_density(
      self, X: TensorType, Fmu: TensorType, Fvar: TensorType, Y: TensorType
    ) -> tf.Tensor:
      return tf.reduce_sum(logdensities.gaussian(Y, Fmu, Fvar + self._variance(X)), axis=-1)