def test_negative_scalar_index(adata, index: int, obs: bool):
    pos_index = index + (adata.n_obs if obs else adata.n_vars)

    if obs:
        adata_pos_subset = adata[pos_index]
        adata_neg_subset = adata[index]
    else:
        adata_pos_subset = adata[:, pos_index]
        adata_neg_subset = adata[:, index]

    np.testing.assert_array_equal(
        adata_pos_subset.obs_names, adata_neg_subset.obs_names
    )
    np.testing.assert_array_equal(
        adata_pos_subset.var_names, adata_neg_subset.var_names
    )
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