def METHOD_NAME(ray_cluster_2cpu):
    """Tests that checksums are deterministic across different processes (no unordered hash maps)."""
    import ray
    # Generate a lot of features so the probability of a reordering of feature sets is very high.
    config = {
        INPUT_FEATURES: [{"name": f"in{i}", "type": "number"} for i in range(100)],
        OUTPUT_FEATURES: [{"name": "out1", "type": "binary"}],
    }
    config = ModelConfig.from_dict(config)
    mock_dataset = mock.Mock()
    mock_dataset.checksum = uuid.uuid4().hex
    @ray.remote(max_calls=1)
    def calculate_checksum_remote(dataset, config):
        return calculate_checksum(dataset, config)
    # Run each checksum calculation as a remote function so it gets its own Python interpreter, as
    # the hash function in Python is deterministic within a process, but not between different processes.
    # See: https://docs.python.org/3/reference/datamodel.html#object.__hash__
    checksum1 = ray.get(calculate_checksum_remote.remote(mock_dataset, config.to_dict()))
    checksum2 = ray.get(calculate_checksum_remote.remote(mock_dataset, config.to_dict()))
    assert checksum1 == checksum2