code: def _get_most_desired_combination(samples_with_combination):
      """Select the next most desired combination whose evidence should be split among folds
  
      Parameters
      ----------
      samples_with_combination : Dict[Combination, List[int]], :code:`(n_combinations)`
              map from each label combination present in y to list of sample indexes that have this combination assigned
  
      Returns
      -------
      combination: Combination
          the combination to split next
      """
      currently_chosen = None
      best_number_of_combinations, best_support_size = None, None
  
      for combination, evidence in samples_with_combination.items():
          number_of_combinations, support_size = (
              len(set(combination)),
              len(evidence),
          )
          if support_size == 0:
              continue
          if currently_chosen is None or (
              best_number_of_combinations < number_of_combinations
              and best_support_size > support_size
          ):
              currently_chosen = combination
              best_number_of_combinations, best_support_size = (
                  number_of_combinations,
                  support_size,
              )
  
      return currently_chosen
  
file_path: scikit-multilearn/skmultilearn/model_selection/iterative_stratification.py
imports: from sklearn.model_selection._split import _BaseKFold
  import numpy
  import scipy.sparse
  import itertools
  from sklearn.utils import check_random_state
label_desc: Use image node_id 3 for calling a global function with example usage: _get_most_desired_combination(samples_with_combination) and returns: currently_chosen