`sample_conditional` will return a sample from the conditinoal distribution.
    In most cases this means calculating the conditional mean m and variance v and then
    returning m + sqrt(v) * eps, with eps ~ N(0, 1).
    However, for some combinations of Mok and Mof more efficient sampling routines exists.
    The dispatcher will make sure that we use the most efficent one.

    :return: N x P (full_output_cov = False) or N x P x P (full_output_cov = True)

def _sample_conditional(Xnew, feat, kern, f, *, full_cov=False, full_output_cov=False, q_sqrt=None, white=False, num_samples=None):
    """
    `sample_conditional` will return a sample from the conditinoal distribution.
    In most cases this means calculating the conditional mean m and variance v and then
    returning m + sqrt(v) * eps, with eps ~ N(0, 1).
    However, for some combinations of Mok and Mof more efficient sampling routines exists.
    The dispatcher will make sure that we use the most efficent one.

    :return: N x P (full_output_cov = False) or N x P x P (full_output_cov = True)
    """
    logger.debug("sample conditional: (MixedKernelSharedMof, MixedKernelSeparateMof), SeparateMixedMok")
    if full_cov:
        raise NotImplementedError("full_cov not yet implemented")
    if full_output_cov:
        raise NotImplementedError("full_output_cov not yet implemented")
    independent_cond = conditional.dispatch(object, SeparateIndependentMof, SeparateIndependentMok, object)
    g_mu, g_var = independent_cond(Xnew, feat, kern, f, white=white, q_sqrt=q_sqrt,
                                   full_output_cov=False, full_cov=False)  # [..., N, L], [..., N, L]
    g_sample = _sample_mvn(g_mu, g_var, "diag", num_samples=num_samples)  # [..., (S), N, L]
    with params_as_tensors_for(kern):
        f_mu, f_var = _mix_latent_gp(kern.W, g_mu, g_var, full_cov, full_output_cov)
        f_sample = tf.tensordot(g_sample, kern.W, [[-1], [-1]])  # [..., N, P]
    return f_sample, f_mu, f_var