def METHOD_NAME(self):
    space = gyms.MultiDiscrete([3, 3])
    actions = su.torch_point(space, space.sample())  # single sampler
    actions = actions.unsqueeze(0).unsqueeze(0)  # add [step, sampler]
    flat_actions = su.flatten(space, actions)
    al = su.action_list(space, flat_actions)
    assert len(al) == 1
    assert len(al[0]) == 2
    space = gyms.Tuple([gyms.MultiDiscrete([3, 3]), gyms.Discrete(2)])
    actions = su.torch_point(space, space.sample())  # single sampler
    actions = (
        actions[0].unsqueeze(0).unsqueeze(0),
        torch.tensor(actions[1]).unsqueeze(0).unsqueeze(0),
    )  # add [step, sampler]
    flat_actions = su.flatten(space, actions)
    al = su.action_list(space, flat_actions)
    assert len(al) == 1
    assert len(al[0][0]) == 2
    assert isinstance(al[0][1], int)
    space = gyms.Dict(
        {"tuple": gyms.MultiDiscrete([3, 3]), "scalar": gyms.Discrete(2)}
    )
    actions = su.torch_point(space, space.sample())  # single sampler
    actions = OrderedDict(
        [
            ("tuple", actions["tuple"].unsqueeze(0).unsqueeze(0)),
            ("scalar", torch.tensor(actions["scalar"]).unsqueeze(0).unsqueeze(0)),
        ]
    )
    flat_actions = su.flatten(space, actions)
    al = su.action_list(space, flat_actions)
    assert len(al) == 1
    assert len(al[0]["tuple"]) == 2
    assert isinstance(al[0]["scalar"], int)