code: def aom(
      scores,
      n_buckets=5,
      method="static",
      bootstrap_estimators=False,
      random_state=None,
  ):
      """Average of Maximum - An ensemble method for combining multiple
      estimators. See :cite:`aggarwal2015theoretical` for details.
  
      First dividing estimators into subgroups, take the maximum score as the
      subgroup score. Finally, take the average of all subgroup scores.
  
      Parameters
      ----------
      scores : numpy array of shape (n_samples, n_estimators)
          The score matrix outputted from various estimators
  
      n_buckets : int, optional (default=5)
          The number of subgroups to build
  
      method : str, optional (default='static')
          {'static', 'dynamic'}, if 'dynamic', build subgroups
          randomly with dynamic bucket size.
  
      bootstrap_estimators : bool, optional (default=False)
          Whether estimators are drawn with replacement.
  
      random_state : int, RandomState instance or None, optional (default=None)
          If int, random_state is the seed used by the
          random number generator; If RandomState instance, random_state is
          the random number generator; If None, the random number generator
          is the RandomState instance used by `np.random`.
  
      Returns
      -------
      combined_scores : Numpy array of shape (n_samples,)
          The combined scores.
  
      """
      return _aom_moa_helper(
          "AOM",
          scores,
          n_buckets,
          method,
          bootstrap_estimators,
          random_state,
      )
  
file_path: combo/combo/models/score_comb.py
imports: import numpy
  from numpy.random import RandomState
  from sklearn.utils import check_array
  from sklearn.utils import column_or_1d
  from sklearn.utils import shuffle
  from sklearn.utils.extmath import weighted_mode
  from sklearn.utils.random import sample_without_replacement
  from numpy.testing import assert_equal
  from sklearn.utils.multiclass import check_classification_targets
  from pyod.utils.utility import check_parameter
label_desc: Use image node_id 2 for calling a global function with example usage: aom(scores, n_buckets, method, bootstrap_estimators, random_state) and returns: _aom_moa_helper