code: def max_PI(
      optimizer: BaseLearner,
      X: modALinput,
      tradeoff: float = 0,
      n_instances: int = 1,
  ) -> np.ndarray:
      """
      Maximum PI query strategy. Selects the instance with highest probability of improvement.
  
      Args:
          optimizer: The :class:`~modAL.models.BayesianOptimizer` object for which the utility is to be calculated.
          X: The samples for which the probability of improvement is to be calculated.
          tradeoff: Value controlling the tradeoff parameter.
          n_instances: Number of samples to be queried.
  
      Returns:
          The indices of the instances from X chosen to be labelled.
          The pi metric of the chosen instances.
  
      """
      pi = optimizer_PI(optimizer, X, tradeoff=tradeoff)
      return multi_argmax(pi, 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 7 for calling a global function with example usage: max_PI(optimizer, X, tradeoff, n_instances) and returns: multi_argmax