code: def max_std_sampling(
      regressor: BaseEstimator,
      X: modALinput,
      n_instances: int = 1,
      random_tie_break=False,
      **predict_kwargs,
  ) -> np.ndarray:
      """
      Regressor standard deviation sampling strategy.
  
      Args:
          regressor: The regressor for which the labels are to be queried.
          X: The pool of samples to query from.
          n_instances: Number of samples to be queried.
          random_tie_break: If True, shuffles utility scores to randomize the order. This
              can be used to break the tie when the highest utility score is not unique.
          **predict_kwargs: Keyword arguments to be passed to :meth:`predict` of the CommiteeRegressor.
  
      Returns:
          The indices of the instances from X chosen to be labelled.
          The standard deviation of the chosen instances.
  
      """
      _, std = regressor.predict(X, return_std=True, **predict_kwargs)
      std = std.reshape(
          X.shape[0],
      )
  
      if not random_tie_break:
          return multi_argmax(std, n_instances=n_instances)
  
      return shuffled_argmax(std, n_instances=n_instances)
  
file_path: modAL/modAL/disagreement.py
imports: from collections import Counter
  import numpy
  from scipy.stats import entropy
  from sklearn.base import BaseEstimator
  from sklearn.exceptions import NotFittedError
  from modAL.models.base import BaseCommittee
  from modAL.utils.data import modALinput
  from modAL.utils.selection import multi_argmax, shuffled_argmax
label_desc: Use image node_id 7 for calling a global function with example usage: max_std_sampling(regressor, X, n_instances, random_tie_break) and returns: shuffled_argmax, multi_argmax