ax/models/torch/botorch_moo_defaults.py [105:146]:
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    model: Model,
    objective_weights: Tensor,
    objective_thresholds: Tensor,
    outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
    X_observed: Optional[Tensor] = None,
    X_pending: Optional[Tensor] = None,
    **kwargs: Any,
) -> AcquisitionFunction:
    r"""Instantiates a qNoisyExpectedHyperVolumeImprovement acquisition function.

    Args:
        model: The underlying model which the acqusition function uses
            to estimate acquisition values of candidates.
        objective_weights: The objective is to maximize a weighted sum of
            the columns of f(x). These are the weights.
        outcome_constraints: A tuple of (A, b). For k outcome constraints
            and m outputs at f(x), A is (k x m) and b is (k x 1) such that
            A f(x) <= b. (Not used by single task models)
        X_observed: A tensor containing points observed for all objective
            outcomes and outcomes that appear in the outcome constraints (if
            there are any).
        X_pending: A tensor containing points whose evaluation is pending (i.e.
            that have been submitted for evaluation) present for all objective
            outcomes and outcomes that appear in the outcome constraints (if
            there are any).
        mc_samples: The number of MC samples to use (default: 512).
        qmc: If True, use qMC instead of MC (default: True).
        prune_baseline: If True, prune the baseline points for NEI (default: True).
        chebyshev_scalarization: Use augmented Chebyshev scalarization.

    Returns:
        qNoisyExpectedHyperVolumeImprovement: The instantiated acquisition function.
    """
    if X_observed is None:
        raise ValueError("There are no feasible observed points.")
    # construct Objective module
    (
        objective,
        objective_thresholds,
    ) = get_weighted_mc_objective_and_objective_thresholds(
        objective_weights=objective_weights, objective_thresholds=objective_thresholds
    )
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ax/models/torch/botorch_moo_defaults.py [177:219]:
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    model: Model,
    objective_weights: Tensor,
    objective_thresholds: Tensor,
    outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
    X_observed: Optional[Tensor] = None,
    X_pending: Optional[Tensor] = None,
    **kwargs: Any,
) -> AcquisitionFunction:
    r"""Instantiates a qExpectedHyperVolumeImprovement acquisition function.

    Args:
        model: The underlying model which the acqusition function uses
            to estimate acquisition values of candidates.
        objective_weights: The objective is to maximize a weighted sum of
            the columns of f(x). These are the weights.
        objective_thresholds:  A tensor containing thresholds forming a reference point
            from which to calculate pareto frontier hypervolume. Points that do not
            dominate the objective_thresholds contribute nothing to hypervolume.
        outcome_constraints: A tuple of (A, b). For k outcome constraints
            and m outputs at f(x), A is (k x m) and b is (k x 1) such that
            A f(x) <= b. (Not used by single task models)
        X_observed: A tensor containing points observed for all objective
            outcomes and outcomes that appear in the outcome constraints (if
            there are any).
        X_pending: A tensor containing points whose evaluation is pending (i.e.
            that have been submitted for evaluation) present for all objective
            outcomes and outcomes that appear in the outcome constraints (if
            there are any).
        mc_samples: The number of MC samples to use (default: 512).
        qmc: If True, use qMC instead of MC (default: True).

    Returns:
        qExpectedHypervolumeImprovement: The instantiated acquisition function.
    """
    if X_observed is None:
        raise ValueError("There are no feasible observed points.")
    # construct Objective module
    (
        objective,
        objective_thresholds,
    ) = get_weighted_mc_objective_and_objective_thresholds(
        objective_weights=objective_weights, objective_thresholds=objective_thresholds
    )
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