def __init__(self, model: Model, X_baseline: Tensor, sampler: Optional[MCSampler]=None, objective: Optional[MCAcquisitionObjective]=None, X_pending: Optional[Tensor]=None, prune_baseline: bool=False) -> None:
    'q-Noisy Expected Improvement.\n\n        Args:\n            model: A fitted model.\n            X_baseline: A `r x d`-dim Tensor of `r` design points that have\n                already been observed. These points are considered as the\n                potential best design point.\n            sampler: The sampler used to draw base samples. Defaults to\n                `SobolQMCNormalSampler(num_samples=500, collapse_batch_dims=True)`.\n            objective: The MCAcquisitionObjective under which the samples are\n                evaluated. Defaults to `IdentityMCObjective()`.\n            X_pending:  A `m x d`-dim Tensor of `m` design points that have\n                points that have been submitted for function evaluation\n                but have not yet been evaluated.  Concatenated into X upon\n                forward call.  Copied and set to have no gradient.\n            prune_baseline: If True, remove points in `X_baseline` that are\n                highly unlikely to be the best point. This can significantly\n                improve performance and is generally recommended. In order to\n                customize pruning parameters, instead manually call\n                `botorch.acquisition.utils.prune_inferior_points` on `X_baseline`\n                before instantiating the acquisition function.\n        '
    super().__init__(model=model, sampler=sampler, objective=objective, X_pending=X_pending)
    if prune_baseline:
        X_baseline = prune_inferior_points(model=model, X=X_baseline, objective=objective)
    self.register_buffer('X_baseline', X_baseline)
q-Noisy Expected Improvement.

Args:
    model: A fitted model.
    X_baseline: A `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=500, collapse_batch_dims=True)`.
    objective: The MCAcquisitionObjective under which the samples are
        evaluated. Defaults to `IdentityMCObjective()`.
    X_pending:  A `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.
def __init__(self, model: Model, X_baseline: Tensor, sampler: Optional[MCSampler]=None, objective: Optional[MCAcquisitionObjective]=None, X_pending: Optional[Tensor]=None, prune_baseline: bool=False) -> None:
    'q-Noisy Expected Improvement.\n\n        Args:\n            model: A fitted model.\n            X_baseline: A `r x d`-dim Tensor of `r` design points that have\n                already been observed. These points are considered as the\n                potential best design point.\n            sampler: The sampler used to draw base samples. Defaults to\n                `SobolQMCNormalSampler(num_samples=500, collapse_batch_dims=True)`.\n            objective: The MCAcquisitionObjective under which the samples are\n                evaluated. Defaults to `IdentityMCObjective()`.\n            X_pending:  A `m x d`-dim Tensor of `m` design points that have\n                points that have been submitted for function evaluation\n                but have not yet been evaluated.  Concatenated into X upon\n                forward call.  Copied and set to have no gradient.\n            prune_baseline: If True, remove points in `X_baseline` that are\n                highly unlikely to be the best point. This can significantly\n                improve performance and is generally recommended. In order to\n                customize pruning parameters, instead manually call\n                `botorch.acquisition.utils.prune_inferior_points` on `X_baseline`\n                before instantiating the acquisition function.\n        '
    super().__init__(model=model, sampler=sampler, objective=objective, X_pending=X_pending)
    if prune_baseline:
        X_baseline = prune_inferior_points(model=model, X=X_baseline, objective=objective)
    self.register_buffer('X_baseline', X_baseline)