code: def _process_predictions(y, y_pred1, y_pred2):
      """Pre-process the predictions of a pair of base classifiers for the
      computation of the diversity measures
  
      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
      -------
      N00 : Percentage of samples that both classifiers predict the wrong label
  
      N10 : Percentage of samples that only classifier 2 predicts the wrong label
  
      N10 : Percentage of samples that only classifier 1 predicts the wrong label
  
      N11 : Percentage of samples that both classifiers predict the correct label
      """
      size_y = len(y)
      if size_y != len(y_pred1) or size_y != len(y_pred2):
          raise ValueError(
              "The vector with class labels must have the same size."
          )
  
      N00, N10, N01, N11 = 0.0, 0.0, 0.0, 0.0
      for index in range(size_y):
          if y_pred1[index] == y[index] and y_pred2[index] == y[index]:
              N11 += 1.0
          elif (
              y_pred1[index] == y[index] and y_pred2[index] != y[index]
          ):
              N10 += 1.0
          elif (
              y_pred1[index] != y[index] and y_pred2[index] == y[index]
          ):
              N01 += 1.0
          else:
              N00 += 1.0
  
      return N00 / size_y, N10 / size_y, N01 / size_y, N11 / size_y
  
file_path: DESlib/deslib/util/diversity.py
imports: import sys
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: _process_predictions(y, y_pred1, y_pred2) and returns: unknown,  unknown,  unknown,  unknown