def test_set_obs(adata, subset_func):
    init_hash = joblib.hash(adata)

    subset = adata[subset_func(adata.obs_names), :]

    new_obs = pd.DataFrame(
        dict(a=np.ones(subset.n_obs), b=np.ones(subset.n_obs)),
        index=subset.obs_names,
    )

    assert subset.is_view
    subset.obs = new_obs
    assert not subset.is_view
    assert np.all(subset.obs == new_obs)

    assert joblib.hash(adata) == init_hash
def update_model(model, disc_grads, gen_grads, disc_opt, gen_opt,
                 global_step, update_scale):
  """Update model with gradients."""

  disc_vars, gen_vars = model.get_variables()

  with tf.control_dependencies(gen_grads + disc_grads):
    disc_update_op = disc_opt.apply_gradients(
        zip(_scale_vars(update_scale, disc_grads),
            disc_vars))

    gen_update_op = gen_opt.apply_gradients(
        zip(_scale_vars(update_scale, gen_grads),
            gen_vars),
        global_step=global_step)

    update_op = tf.group([disc_update_op, gen_update_op])

  return update_op