code: def double_fault(y, y_pred1, y_pred2):
      """Calculates the double fault (df) measure. This measure represents the
      probability that both classifiers makes the wrong prediction. A lower value
      of df means the base classifiers are less likely to make the same error.
      This measure must be minimized to increase diversity.
  
      Parameters
      ----------
      y : array of shape (n_samples):
          class labels of each sample.
  
      y_pred1 : array of shape (n_samples):
                predicted class labels by the classifier 1 for each sample.
  
      y_pred2 : array of shape (n_samples):
                predicted class labels by the classifier 2 for each sample.
  
      Returns
      -------
      df : The double fault measure between two classifiers
  
      References
      ----------
      Giacinto, Giorgio, and Fabio Roli. "Design of effective neural network
      ensembles for image classification purposes."
      Image and Vision Computing 19.9 (2001): 699-707.
      """
      N00, _, _, _ = _process_predictions(y, y_pred1, y_pred2)
      df = N00
      return df
  
file_path: DESlib/deslib/util/diversity.py
imports: import sys
  import numpy
label_desc: Use image node_id 2 for calling a global function with example usage: double_fault(y, y_pred1, y_pred2) and returns: df