def base_conditional(Kmn, Kmm, Knn, f, *, full_cov=False, q_sqrt=None, white=False):
    r"""
    Given a g1 and g2, and distribution p and q such that
      p(g2) = N(g2;0,Kmm)
      p(g1) = N(g1;0,Knn)
      p(g1|g2) = N(g1;0,Knm)
    And
      q(g2) = N(g2;f,q_sqrt*q_sqrt^T)
    This method computes the mean and (co)variance of
      q(g1) = \int q(g2) p(g1|g2)
    :param Kmn: M x [...] x N
    :param Kmm: M x M
    :param Knn: [...] x N x N  or  N
    :param f: M x R
    :param full_cov: bool
    :param q_sqrt: None or R x M x M (lower triangular)
    :param white: bool
    :return: N x R  or R x N x N
    """
    logger.debug("base conditional")
    # compute kernel stuff
    num_func = tf.shape(f)[-1]  # R
    N = tf.shape(Kmn)[-1]
    M = tf.shape(f)[-2]

    # get the leadings dims in Kmn to the front of the tensor
    # if Kmn has rank two, i.e. [M, N], this is the identity op.
    K = tf.rank(Kmn)
    perm = tf.concat([tf.reshape(tf.range(1, K-1), [K-2]), # leading dims (...)
                      tf.reshape(0, [1]),  # [M]
                      tf.reshape(K-1, [1])], 0)  # [N]
    Kmn = tf.transpose(Kmn, perm)  # ... x M x N

    leading_dims = tf.shape(Kmn)[:-2]
    Lm = tf.cholesky(Kmm)  # [M,M]

    # Compute the projection matrix A
    Lm = tf.broadcast_to(Lm, tf.concat([leading_dims, tf.shape(Lm)], 0))  # [...,M,M]
    A = tf.matrix_triangular_solve(Lm, Kmn, lower=True)  # [...,M,N]
    # compute the covariance due to the conditioning
    if full_cov:
        fvar = Knn - tf.matmul(A, A, transpose_a=True)  # [...,N,N]
        cov_shape = tf.concat([leading_dims, [num_func, N, N]], 0)
        fvar = tf.broadcast_to(tf.expand_dims(fvar, -3), cov_shape)  # [...,R,N,N]
    else:
        fvar = Knn - tf.reduce_sum(tf.square(A), -2)  # [...,N]
        cov_shape = tf.concat([leading_dims, [num_func, N]], 0) # [...,R,N]
        fvar = tf.broadcast_to(tf.expand_dims(fvar, -2), cov_shape)  # [...,R,N]

    # another backsubstitution in the unwhitened case
    if not white:
        A = tf.matrix_triangular_solve(tf.matrix_transpose(Lm), A, lower=False)

    # construct the conditional mean
    f_shape = tf.concat([leading_dims, [M, num_func]], 0)  # [...,M,R]
    f = tf.broadcast_to(f, f_shape)  # [...,M,R]
    fmean = tf.matmul(A, f, transpose_a=True)  # [...,N,R]

    if q_sqrt is not None:
        if q_sqrt.get_shape().ndims == 2:
            LTA = A * tf.expand_dims(tf.transpose(q_sqrt), 2)  # R x M x N
        elif q_sqrt.get_shape().ndims == 3:
            L = q_sqrt
            L = tf.broadcast_to(L, tf.concat([leading_dims, tf.shape(L)], 0))

            shape = tf.concat([leading_dims, [num_func, M, N]], 0)
            A_tiled = tf.broadcast_to(tf.expand_dims(A, -3), shape)
            LTA = tf.matmul(L, A_tiled, transpose_a=True)  # R x M x N
        else:  # pragma: no cover
            raise ValueError("Bad dimension for q_sqrt: %s" %
                             str(q_sqrt.get_shape().ndims))
        if full_cov:
            fvar = fvar + tf.matmul(LTA, LTA, transpose_a=True)  # R x N x N
        else:
            fvar = fvar + tf.reduce_sum(tf.square(LTA), -2)  # R x N

    if not full_cov:
        fvar = tf.matrix_transpose(fvar)  # N x R

    return fmean, fvar
r"""
    Given a g1 and g2, and distribution p and q such that
      p(g2) = N(g2;0,Kmm)
      p(g1) = N(g1;0,Knn)
      p(g1|g2) = N(g1;0,Knm)
    And
      q(g2) = N(g2;f,q_sqrt*q_sqrt^T)
    This method computes the mean and (co)variance of
      q(g1) = \int q(g2) p(g1|g2)
    :param Kmn: M x [...] x N
    :param Kmm: M x M
    :param Knn: [...] x N x N  or  N
    :param f: M x R
    :param full_cov: bool
    :param q_sqrt: None or R x M x M (lower triangular)
    :param white: bool
    :return: N x R  or R x N x N