import tensorflow as tf

    A = tf.matrix_triangular_solve(Lm, Kmn, lower=True)

    # compute the covariance due to the conditioning
    if full_cov:
        fvar = Knn - tf.matmul(A, A, transpose_a=True)
        fvar = tf.tile(fvar[None, :, :], [num_func, 1, 1])  # R x N x N
    else:
        fvar = Knn - tf.reduce_sum(tf.square(A), 0)
        fvar = tf.tile(fvar[None, :], [num_func, 1])  # R x N

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

    # construct the conditional mean
    fmean = tf.matmul(A, f, transpose_a=True)
