Return the function of interest (kernel or mean) for the expectation
    depending on the type of :obj: and whether any features are given

def get_eval_func(obj, feature, slice=np.s_[...]):
    """
    Return the function of interest (kernel or mean) for the expectation
    depending on the type of :obj: and whether any features are given
    """
    if feature is not None:
        # kernel + feature combination
        if not isinstance(feature, InducingFeature) or not isinstance(obj, kernels.Kernel):
            raise TypeError("If `feature` is supplied, `obj` must be a kernel.")
        return lambda x: tf.transpose(Kuf(feature, obj, x))[slice]
    elif isinstance(obj, mean_functions.MeanFunction):
        return lambda x: obj(x)[slice]
    elif isinstance(obj, kernels.Kernel):
        return lambda x: obj.Kdiag(x)
    else:
        raise NotImplementedError()