code: def median_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
      -------
      predicted_label : array of shape (n_samples)
          The label of each query sample predicted using the majority voting rule
      """
      _check_predictions(predictions)
      median_predictions = np.median(predictions, axis=1)
      return np.argmax(median_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 18 for calling a global function with example usage: median_rule(predictions) and returns: np