ax/modelbridge/factory.py [549:577]:
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    experiment: Experiment,
    data: Data,
    objective_thresholds: Optional[TRefPoint] = None,
    search_space: Optional[SearchSpace] = None,
    dtype: torch.dtype = torch.double,
    device: torch.device = DEFAULT_TORCH_DEVICE,
) -> TorchModelBridge:
    """Instantiates a multi-objective model that generates points with ParEGO.

    qParEGO optimizes random augmented chebyshev scalarizations of the multiple
    objectives. This allows it to explore non-convex pareto frontiers.
    """
    # pyre-ignore: [16] `Optional` has no attribute `objective`.
    if not isinstance(experiment.optimization_config.objective, MultiObjective):
        raise ValueError("Multi-Objective optimization requires multiple objectives")
    if data.df.empty:
        raise ValueError("MultiObjectiveOptimization requires non-empty data.")
    return checked_cast(
        TorchModelBridge,
        Models.MOO(
            experiment=experiment,
            data=data,
            objective_thresholds=objective_thresholds,
            search_space=search_space or experiment.search_space,
            torch_dtype=dtype,
            torch_device=device,
            acqf_constructor=get_NEI,
            default_model_gen_options={
                "acquisition_function_kwargs": {
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ax/modelbridge/factory.py [587:616]:
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    experiment: Experiment,
    data: Data,
    objective_thresholds: Optional[TRefPoint] = None,
    search_space: Optional[SearchSpace] = None,
    dtype: torch.dtype = torch.double,
    device: torch.device = DEFAULT_TORCH_DEVICE,
) -> TorchModelBridge:
    """Instantiates a Random Scalarization multi-objective model.

    Chooses a different random linear scalarization of the objectives
    for generating each new candidate arm. This will only explore the
    convex hull of the pareto frontier.
    """
    # pyre-ignore: [16] `Optional` has no attribute `objective`.
    if not isinstance(experiment.optimization_config.objective, MultiObjective):
        raise ValueError("Multi-Objective optimization requires multiple objectives")
    if data.df.empty:
        raise ValueError("MultiObjectiveOptimization requires non-empty data.")
    return checked_cast(
        TorchModelBridge,
        Models.MOO(
            experiment=experiment,
            data=data,
            objective_thresholds=objective_thresholds,
            search_space=search_space or experiment.search_space,
            torch_dtype=dtype,
            torch_device=device,
            acqf_constructor=get_NEI,
            default_model_gen_options={
                "acquisition_function_kwargs": {
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