code: def negative_double_fault(
      y: np.array, y_pred1: np.array, y_pred2: np.array
  ) -> np.array:
      """The negative of the double fault measure. This measure should be
      maximized for a higher 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_classifiers, n_samples):
                predicted class labels by the classifier 2 for each sample.
  
      Returns
      -------
      df : The negative 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.
      """
      return -double_fault(y, y_pred1, y_pred2)
  
file_path: DESlib/deslib/util/diversity_batch.py
imports: import sys
  import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: negative_double_fault(y, y_pred1, y_pred2) and returns: unknown