def test_set_subset_obsm(adata, subset_func):
    init_hash = joblib.hash(adata)
    orig_obsm_val = adata.obsm["o"].copy()

    while True:
        subset_idx = slice_subset(adata.obs_names)
        if len(adata[subset_idx, :]) > 2:
            break
    subset = adata[subset_idx, :]

    internal_idx = _normalize_index(
        subset_func(np.arange(subset.obsm["o"].shape[0])), subset.obs_names
    )

    assert subset.is_view
    subset.obsm["o"][internal_idx] = 1
    assert not subset.is_view
    assert np.all(adata.obsm["o"] == orig_obsm_val)

    assert init_hash == joblib.hash(adata)
def test_make_gym_venv_nostack(name, num_envs, state_shape, reward_scale):
    seed = 0
    frame_op = None
    frame_op_len = None
    venv = make_gym_venv(name, num_envs, seed, frame_op=frame_op, frame_op_len=frame_op_len, reward_scale=reward_scale)
    venv.reset()
    for i in range(5):
        state, reward, done, info = venv.step([venv.action_space.sample()] * num_envs)

    assert isinstance(state, np.ndarray)
    assert state.shape == (num_envs,) + state_shape
    assert isinstance(reward, np.ndarray)
    assert reward.shape == (num_envs,)
    assert isinstance(done, np.ndarray)
    assert done.shape == (num_envs,)
    assert len(info) == num_envs
    venv.close()