code: def optimizer_EI(
      optimizer: BaseLearner, X: modALinput, tradeoff: float = 0
  ) -> np.ndarray:
      """
      Expected improvement acquisition function for Bayesian optimization.
  
      Args:
          optimizer: The :class:`~modAL.models.BayesianOptimizer` object for which the utility is to be calculated.
          X: The samples for which the expected improvement is to be calculated.
          tradeoff: Value controlling the tradeoff parameter.
  
      Returns:
          Expected improvement utility score.
      """
      try:
          mean, std = optimizer.predict(X, return_std=True)
          mean, std = mean.reshape(
              -1,
          ), std.reshape(
              -1,
          )
      except NotFittedError:
          mean, std = np.zeros(shape=(X.shape[0], 1)), np.ones(
              shape=(X.shape[0], 1)
          )
  
      return EI(mean, std, optimizer.y_max, tradeoff)
  
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 5 for calling a global function with example usage: optimizer_EI(optimizer, X, tradeoff) and returns: EI