ax/modelbridge/factory.py [123:177]:
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) -> TorchModelBridge:
    """Instantiates a Multi-task Gaussian Process (MTGP) model that generates
    points with qNEHVI.

    If the input experiment is a MultiTypeExperiment then a
    Multi-type Multi-task GP model will be instantiated.
    Otherwise, the model will be a Single-type Multi-task GP.
    """
    # pyre-ignore: [16] `Optional` has no attribute `objective`.
    if not isinstance(experiment.optimization_config.objective, MultiObjective):
        raise ValueError("Multi-objective optimization requires multiple objectives.")
    elif data.df.empty:  # pragma: no cover
        raise ValueError("MultiObjectiveOptimization requires non-empty data.")

    if isinstance(experiment, MultiTypeExperiment):
        trial_index_to_type = {
            t.index: t.trial_type for t in experiment.trials.values()
        }
        transforms = MT_MTGP_trans
        transform_configs = {
            "ConvertMetricNames": tconfig_from_mt_experiment(experiment),
            "TrialAsTask": {"trial_level_map": {"trial_type": trial_index_to_type}},
        }
    else:
        # Set transforms for a Single-type MTGP model.
        transforms = ST_MTGP_trans
        transform_configs = None

    # Choose the status quo features for the experiment from the selected trial.
    # If trial_index is None, we will look for a status quo from the last
    # experiment trial to use as a status quo for the experiment.
    if trial_index is None:
        trial_index = len(experiment.trials) - 1
    elif trial_index >= len(experiment.trials):
        raise ValueError("trial_index is bigger than the number of experiment trials")

    # pyre-fixme[16]: `ax.core.base_trial.BaseTrial` has no attribute `status_quo`.
    status_quo = experiment.trials[trial_index].status_quo
    if status_quo is None:
        status_quo_features = None
    else:
        status_quo_features = ObservationFeatures(
            parameters=status_quo.parameters,
            # pyre-fixme[6]: Expected `Optional[numpy.int64]` for 2nd param but got
            #  `int`.
            trial_index=trial_index,
        )

    return checked_cast(
        TorchModelBridge,
        Models.MOO(
            experiment=experiment,
            data=data,
            objective_thresholds=objective_thresholds,
            search_space=search_space or experiment.search_space,
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ax/modelbridge/factory.py [633:684]:
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) -> TorchModelBridge:
    """Instantiates a multi-objective, multi-task model that uses qParEGO.

    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.")
    elif data.df.empty:  # pragma: no cover
        raise ValueError("MultiObjectiveOptimization requires non-empty data.")

    if isinstance(experiment, MultiTypeExperiment):
        trial_index_to_type = {
            t.index: t.trial_type for t in experiment.trials.values()
        }
        transforms = MT_MTGP_trans
        transform_configs = {
            "ConvertMetricNames": tconfig_from_mt_experiment(experiment),
            "TrialAsTask": {"trial_level_map": {"trial_type": trial_index_to_type}},
        }
    else:
        # Set transforms for a Single-type MTGP model.
        transforms = ST_MTGP_trans
        transform_configs = None

    # Choose the status quo features for the experiment from the selected trial.
    # If trial_index is None, we will look for a status quo from the last
    # experiment trial to use as a status quo for the experiment.
    if trial_index is None:
        trial_index = len(experiment.trials) - 1
    elif trial_index >= len(experiment.trials):
        raise ValueError("trial_index is bigger than the number of experiment trials")

    # pyre-fixme[16]: `ax.core.base_trial.BaseTrial` has no attribute `status_quo`.
    status_quo = experiment.trials[trial_index].status_quo
    if status_quo is None:
        status_quo_features = None
    else:
        status_quo_features = ObservationFeatures(
            parameters=status_quo.parameters,
            # pyre-fixme[6]: Expected `Optional[numpy.int64]` for 2nd param but got
            #  `int`.
            trial_index=trial_index,
        )
    return checked_cast(
        TorchModelBridge,
        Models.MOO(
            experiment=experiment,
            data=data,
            objective_thresholds=objective_thresholds,
            search_space=search_space or experiment.search_space,
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