botorch/acquisition/monte_carlo.py [132:157]:
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        **kwargs: Any,
    ) -> None:
        r"""q-Expected Improvement.

        Args:
            model: A fitted model.
            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.
            sampler: The sampler used to draw base samples. Defaults to
                `SobolQMCNormalSampler(num_samples=512, collapse_batch_dims=True)`
            objective: The MCAcquisitionObjective under which the samples are evaluated.
                Defaults to `IdentityMCObjective()`.
            posterior_transform: A PosteriorTransform (optional).
            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. Copied and set to have no
                gradient.
        """
        super().__init__(
            model=model,
            sampler=sampler,
            objective=objective,
            posterior_transform=posterior_transform,
            X_pending=X_pending,
        )
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botorch/acquisition/monte_carlo.py [214:252]:
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        **kwargs: Any,
    ) -> None:
        r"""q-Noisy Expected Improvement.

        Args:
            model: A fitted model.
            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.
            sampler: The sampler used to draw base samples. Defaults to
                `SobolQMCNormalSampler(num_samples=512, collapse_batch_dims=True)`.
            objective: The MCAcquisitionObjective under which the samples are
                evaluated. Defaults to `IdentityMCObjective()`.
            posterior_transform: A PosteriorTransform (optional).
            X_pending: A `batch_shape x m x d`-dim Tensor of `m` design points
                that have points that have been submitted for function evaluation
                but have not yet been evaluated. Concatenated into `X` upon
                forward call. Copied and set to have no gradient.
            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. In order to
                customize pruning parameters, instead manually call
                `botorch.acquisition.utils.prune_inferior_points` on `X_baseline`
                before instantiating the acquisition function.
            cache_root: A boolean indicating whether to cache the root
                decomposition over `X_baseline` and use low-rank updates.

        TODO: similar to qNEHVI, when we are using sequential greedy candidate
        selection, we could incorporate pending points X_baseline and compute
        the incremental qNEI from the new point. This would greatly increase
        efficiency for large batches.
        """
        super().__init__(
            model=model,
            sampler=sampler,
            objective=objective,
            posterior_transform=posterior_transform,
            X_pending=X_pending,
        )
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