botorch/acquisition/knowledge_gradient.py [57:77]:
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    r"""Batch Knowledge Gradient using one-shot optimization.

    This computes the batch Knowledge Gradient using fantasies for the outer
    expectation and either the model posterior mean or MC-sampling for the inner
    expectation.

    In addition to the design variables, the input `X` also includes variables
    for the optimal designs for each of the fantasy models. For a fixed number
    of fantasies, all parts of `X` can be optimized in a "one-shot" fashion.
    """

    def __init__(
        self,
        model: Model,
        num_fantasies: Optional[int] = 64,
        sampler: Optional[MCSampler] = None,
        objective: Optional[MCAcquisitionObjective] = None,
        posterior_transform: Optional[PosteriorTransform] = None,
        inner_sampler: Optional[MCSampler] = None,
        X_pending: Optional[Tensor] = None,
        current_value: Optional[Tensor] = None,
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botorch/acquisition/knowledge_gradient.py [320:339]:
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    r"""Batch Knowledge Gradient for multi-fidelity optimization.

    A version of `qKnowledgeGradient` that supports multi-fidelity optimization
    via a `CostAwareUtility` and the `project` and `expand` operators. If none
    of these are set, this acquisition function reduces to `qKnowledgeGradient`.
    Through `valfunc_cls` and `valfunc_argfac`, this can be changed into a custom
    multi-fidelity acquisition function (it is only KG if the terminal value is
    computed using a posterior mean).
    """

    def __init__(
        self,
        model: Model,
        num_fantasies: Optional[int] = 64,
        sampler: Optional[MCSampler] = None,
        objective: Optional[MCAcquisitionObjective] = None,
        posterior_transform: Optional[PosteriorTransform] = None,
        inner_sampler: Optional[MCSampler] = None,
        X_pending: Optional[Tensor] = None,
        current_value: Optional[Tensor] = None,
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