code: def disagreement_measure(
      y: np.array, y_pred1: np.array, y_pred2: np.array
  ) -> np.array:
      """Calculates the disagreement measure between a pair of classifiers. This
      measure is calculated by the frequency that only one classifier makes the
      correct prediction.
  
      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
      -------
      disagreement : The frequency at which both classifiers disagrees
      """
      _, N10, N01, _ = _process_predictions(y, y_pred1, y_pred2)
      disagreement = N10 + N01
      return disagreement
  
file_path: DESlib/deslib/util/diversity_batch.py
imports: import sys
  import numpy
label_desc: Use image node_id 6 for calling a global function with example usage: disagreement_measure(y, y_pred1, y_pred2) and returns: disagreement