def gauss_kl(q_mu, q_sqrt, K=None, *, K_cholesky=None):
    """
    Compute the KL divergence KL[q || p] between

          q(x) = N(q_mu, q_sqrt^2)
    and
          p(x) = N(0, K)

    We assume N multiple independent distributions, given by the columns of
    q_mu and the last dimension of q_sqrt. Returns the sum of the divergences.

    q_mu is a matrix [M, L], each column contains a mean.

    q_sqrt can be a 3D tensor [L, M, M], each matrix within is a lower
        triangular square-root matrix of the covariance of q.
    q_sqrt can be a matrix [M, L], each column represents the diagonal of a
        square-root matrix of the covariance of q.

    K is the covariance of p, [M, M] or [L, M, M]
    K_cholesky is the cholesky of the covariance of p, [M, M] or [L, M, M]
    
    Note: if no K matrix is given (both `K` and `K_cholesky` are None),
    `gauss_kl` computes the KL divergence from p(x) = N(0, I) instead.
    The K matrix can be passed either directly as `K`, or as its Cholesky factor, `K_cholesky`. 
    In either case, it can be a single matrix [M, M], in which case the sum of the L KL divergences 
    is computed by broadcasting, or L different covariances [L, M, M].
    """

    if (K is not None) and (K_cholesky is not None):
        raise ValueError("Ambiguous arguments: gauss_kl() must only "
                         "be passed one of `K` or `K_cholesky`.")

    white = (K is None) and (K_cholesky is None)
    diag = q_sqrt.get_shape().ndims == 2

    M, B = tf.shape(q_mu)[0], tf.shape(q_mu)[1]

    if white:
        alpha = q_mu  # [M, B]
    else:
        if K is not None:
            Lp = tf.cholesky(K)  # [B, M, M] or [M, M]
        elif K_cholesky is not None:
            Lp = K_cholesky  # [B, M, M] or [M, M]

        batched = Lp.get_shape().ndims == 3
        q_mu = tf.transpose(q_mu)[:, :, None] if batched else q_mu  # [B, M, 1] or [M, B]
        alpha = tf.matrix_triangular_solve(Lp, q_mu, lower=True)  # [B, M, 1] or [M, B]

    if diag:
        Lq = Lq_diag = q_sqrt
        Lq_full = tf.matrix_diag(tf.transpose(q_sqrt))  # [B, M, M]
    else:
        Lq = Lq_full = tf.matrix_band_part(q_sqrt, -1, 0)  # force lower triangle # [B, M, M]
        Lq_diag = tf.matrix_diag_part(Lq)  # [M, B]

    # Mahalanobis term: μqᵀ Σp⁻¹ μq
    mahalanobis = tf.reduce_sum(tf.square(alpha))

    # Constant term: - B * M
    constant = - tf.cast(tf.size(q_mu, out_type=tf.int64), dtype=settings.float_type)

    # Log-determinant of the covariance of q(x):
    logdet_qcov = tf.reduce_sum(tf.log(tf.square(Lq_diag)))

    # Trace term: tr(Σp⁻¹ Σq)
    if white:
        trace = tf.reduce_sum(tf.square(Lq))
    else:
        if diag and not batched:
            # K is [M, M] and q_sqrt is [M, B]: fast specialisation
            LpT = tf.transpose(Lp)  # [M, M]
            Lp_inv = tf.matrix_triangular_solve(Lp, tf.eye(M, dtype=settings.float_type),lower=True)  # [M, M]
            K_inv = tf.matrix_diag_part(tf.matrix_triangular_solve(LpT, Lp_inv, lower=False))[:, None]  # [M, M] -> [M, 1]
            trace = tf.reduce_sum(K_inv * tf.square(q_sqrt))
        else:
            # TODO: broadcast instead of tile when tf allows (not implemented in tf <= 1.12)
            Lp_full = Lp if batched else tf.tile(tf.expand_dims(Lp, 0), [B, 1, 1])
            LpiLq = tf.matrix_triangular_solve(Lp_full, Lq_full, lower=True)
            trace = tf.reduce_sum(tf.square(LpiLq))

    twoKL = mahalanobis + constant - logdet_qcov + trace

    # Log-determinant of the covariance of p(x):
    if not white:
        log_sqdiag_Lp = tf.log(tf.square(tf.matrix_diag_part(Lp)))
        sum_log_sqdiag_Lp = tf.reduce_sum(log_sqdiag_Lp)
        # If K is B x M x M, num_latent is no longer implicit, no need to multiply the single kernel logdet
        scale = 1.0 if batched else tf.cast(B, settings.float_type)
        twoKL += scale * sum_log_sqdiag_Lp

    return 0.5 * twoKL
Compute the KL divergence KL[q || p] between

          q(x) = N(q_mu, q_sqrt^2)
    and
          p(x) = N(0, K)

    We assume N multiple independent distributions, given by the columns of
    q_mu and the last dimension of q_sqrt. Returns the sum of the divergences.

    q_mu is a matrix [M, L], each column contains a mean.

    q_sqrt can be a 3D tensor [L, M, M], each matrix within is a lower
        triangular square-root matrix of the covariance of q.
    q_sqrt can be a matrix [M, L], each column represents the diagonal of a
        square-root matrix of the covariance of q.

    K is the covariance of p, [M, M] or [L, M, M]
    K_cholesky is the cholesky of the covariance of p, [M, M] or [L, M, M]
    
    Note: if no K matrix is given (both `K` and `K_cholesky` are None),
    `gauss_kl` computes the KL divergence from p(x) = N(0, I) instead.
    The K matrix can be passed either directly as `K`, or as its Cholesky factor, `K_cholesky`. 
    In either case, it can be a single matrix [M, M], in which case the sum of the L KL divergences 
    is computed by broadcasting, or L different covariances [L, M, M].