botorch/acquisition/input_constructors.py [444:479]:
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    **kwargs: Any,
) -> Dict[str, Any]:
    r"""Construct kwargs for the `qNoisyExpectedImprovement` constructor.

    Args:
        model: The model to be used in the acquisition function.
        training_data: A TrainingData object contraining the model's
            training data. Used e.g. to extract inputs such as `best_f`
            for expected improvement acquisition functions. Only block-
            design training data currently supported.
        objective: The objective to be used in the acquisition function.
        posterior_transform: The posterior transform to be used in the
            acquisition function.
        X_pending: A `m x d`-dim Tensor of `m` design points that have been
            submitted for function evaluation but have not yet been evaluated.
            Concatenated into X upon forward call.
        sampler: The sampler used to draw base samples. If omitted, uses
            the acquisition functions's default sampler.
        X_baseline: A `batch_shape x r x d`-dim Tensor of `r` design points
            that have already been observed. These points are considered as
            the potential best design point. If omitted, use `training_data.X`.
        prune_baseline: If True, remove points in `X_baseline` that are
            highly unlikely to be the best point. This can significantly
            improve performance and is generally recommended.

    Returns:
        A dict mapping kwarg names of the constructor to values.
    """
    base_inputs = construct_inputs_mc_base(
        model=model,
        training_data=training_data,
        objective=objective,
        posterior_transform=posterior_transform,
        sampler=sampler,
        X_pending=X_pending,
    )
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botorch/acquisition/input_constructors.py [501:535]:
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    **kwargs: Any,
) -> Dict[str, Any]:
    r"""Construct kwargs for the `qProbabilityOfImprovement` constructor.

    Args:
        model: The model to be used in the acquisition function.
        training_data: A TrainingData object contraining the model's
            training data. Used e.g. to extract inputs such as `best_f`
            for expected improvement acquisition functions.
        objective: The objective to be used in the acquisition function.
        posterior_transform: The posterior transform to be used in the
            acquisition function.
        X_pending: A `m x d`-dim Tensor of `m` design points that have been
            submitted for function evaluation but have not yet been evaluated.
            Concatenated into X upon forward call.
        sampler: The sampler used to draw base samples. If omitted, uses
            the acquisition functions's default sampler.
        tau: The temperature parameter used in the sigmoid approximation
            of the step function. Smaller values yield more accurate
            approximations of the function, but result in gradients
            estimates with higher variance.
        best_f: The best objective value observed so far (assumed noiseless). Can
            be a `batch_shape`-shaped tensor, which in case of a batched model
            specifies potentially different values for each element of the batch.
    Returns:
        A dict mapping kwarg names of the constructor to values.
    """
    base_inputs = construct_inputs_mc_base(
        model=model,
        training_data=training_data,
        objective=objective,
        posterior_transform=posterior_transform,
        sampler=sampler,
        X_pending=X_pending,
    )
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