def __init__(self, model: Model, beta: float, sampler: Optional[MCSampler]=None, objective: Optional[MCAcquisitionObjective]=None, X_pending: Optional[Tensor]=None) -> None:
    'q-Upper Confidence Bound.\n\n        Args:\n            model: A fitted model.\n            beta: Controls tradeoff between mean and standard deviation in UCB.\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        '
    super().__init__(model=model, sampler=sampler, objective=objective, X_pending=X_pending)
    self.beta_prime = math.sqrt(((beta * math.pi) / 2))
q-Upper Confidence Bound.

Args:
    model: A fitted model.
    beta: Controls tradeoff between mean and standard deviation in UCB.
    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.
def __init__(self, model: Model, beta: float, sampler: Optional[MCSampler]=None, objective: Optional[MCAcquisitionObjective]=None, X_pending: Optional[Tensor]=None) -> None:
    'q-Upper Confidence Bound.\n\n        Args:\n            model: A fitted model.\n            beta: Controls tradeoff between mean and standard deviation in UCB.\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        '
    super().__init__(model=model, sampler=sampler, objective=objective, X_pending=X_pending)
    self.beta_prime = math.sqrt(((beta * math.pi) / 2))