ax/models/torch/botorch_kg.py [43:69]:
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    r"""The Knowledge Gradient with one shot optimization.

    Args:
        cost_intercept: The cost intercept for the affine cost of the form
            `cost_intercept + n`, where `n` is the number of generated points.
            Only used for multi-fidelity optimzation (i.e., if fidelity_features
            are present).
        linear_truncated: If `False`, use an alternate downsampling + exponential
            decay Kernel instead of the default `LinearTruncatedFidelityKernel`
            (only relevant for multi-fidelity optimization).
        kwargs: Model-specific kwargs.
    """

    def __init__(
        self,
        cost_intercept: float = 1.0,
        linear_truncated: bool = True,
        use_input_warping: bool = False,
        **kwargs: Any,
    ) -> None:
        super().__init__(
            best_point_recommender=recommend_best_out_of_sample_point,
            linear_truncated=linear_truncated,
            use_input_warping=use_input_warping,
            **kwargs,
        )
        self.cost_intercept = cost_intercept
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ax/models/torch/botorch_mes.py [41:67]:
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    r"""Max-value entropy search.

    Args:
        cost_intercept: The cost intercept for the affine cost of the form
            `cost_intercept + n`, where `n` is the number of generated points.
            Only used for multi-fidelity optimzation (i.e., if fidelity_features
            are present).
        linear_truncated: If `False`, use an alternate downsampling + exponential
            decay Kernel instead of the default `LinearTruncatedFidelityKernel`
            (only relevant for multi-fidelity optimization).
        kwargs: Model-specific kwargs.
    """

    def __init__(
        self,
        cost_intercept: float = 1.0,
        linear_truncated: bool = True,
        use_input_warping: bool = False,
        **kwargs: Any,
    ) -> None:
        super().__init__(
            best_point_recommender=recommend_best_out_of_sample_point,
            linear_truncated=linear_truncated,
            use_input_warping=use_input_warping,
            **kwargs,
        )
        self.cost_intercept = cost_intercept
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