code: def minimum_rule(predictions):
      """Apply the product fusion rule to the predicted vector of class supports
      (predictions).
  
      Parameters
      ----------
      predictions : np array of shape (n_samples, n_classifiers, n_classes)
          Vector of class supports predicted by each base classifier for sample
  
      Returns
      -------
      list_proba : array of shape = [n_classifiers, n_samples, n_classes]
          Probabilities predicted by each base classifier in the ensemble for all
          samples in X.
      """
      _check_predictions(predictions)
      min_predictions = np.min(predictions, axis=1)
      return np.argmax(min_predictions, axis=1)
  
imports: import numpy
  from scipy.stats.mstats import mode
  from sklearn.utils.validation import check_array
  from deslib.util.prob_functions import softmax
label_desc: Use image node_id 20 for calling a global function with example usage: minimum_rule(predictions) and returns: np