code: def max_UCB(
      optimizer: BaseLearner,
      X: modALinput,
      beta: float = 1,
      n_instances: int = 1,
  ) -> np.ndarray:
      """
      Maximum UCB query strategy. Selects the instance with highest upper confidence bound.
  
      Args:
          optimizer: The :class:`~modAL.models.BayesianOptimizer` object for which the utility is to be calculated.
          X: The samples for which the maximum upper confidence bound is to be calculated.
          beta: Value controlling the beta parameter.
          n_instances: Number of samples to be queried.
  
      Returns:
          The indices of the instances from X chosen to be labelled.
          The ucb metric of the chosen instances.
  
      """
      ucb = optimizer_UCB(optimizer, X, beta=beta)
      return multi_argmax(ucb, n_instances=n_instances)
  
file_path: modAL/modAL/acquisition.py
imports: import numpy
  from scipy.special import ndtr
  from scipy.stats import norm
  from sklearn.exceptions import NotFittedError
  from modAL.models.base import BaseLearner
  from modAL.utils.data import modALinput
  from modAL.utils.selection import multi_argmax
label_desc: Use image node_id 9 for calling a global function with example usage: max_UCB(optimizer, X, beta, n_instances) and returns: multi_argmax