def METHOD_NAME(space: Space, n: int, base_seed: int):
    """Tests the batched spaces are deterministic by using a copied version"""
    # Copy the spaces and check that the np_random are not reference equal
    space_a = space
    space_a.seed(base_seed)
    space_b = copy.deepcopy(space_a)
    assert_rng_equal(space_a.np_random, space_b.np_random)
    assert space_a.np_random is not space_b.np_random
    # Batch the spaces and check that the np_random are not reference equal
    space_a_batched = batch_space(space_a, n)
    space_b_batched = batch_space(space_b, n)
    assert_rng_equal(space_a_batched.np_random, space_b_batched.np_random)
    assert space_a_batched.np_random is not space_b_batched.np_random
    # Create that the batched space is not reference equal to the origin spaces
    assert space_a.np_random is not space_a_batched.np_random
    # Check that batched space a and b random number generator are not effected by the original space
    space_a.sample()
    space_a_batched_sample = space_a_batched.sample()
    space_b_batched_sample = space_b_batched.sample()
    for a_sample, b_sample in zip(
        iterate(space_a_batched, space_a_batched_sample),
        iterate(space_b_batched, space_b_batched_sample),
    ):
        if isinstance(a_sample, tuple):
            assert len(a_sample) == len(b_sample)
            for a_subsample, b_subsample in zip(a_sample, b_sample):
                assert_array_equal(a_subsample, b_subsample)
        else:
            assert_array_equal(a_sample, b_sample)