code: def Q_statistic(
      y: np.array, y_pred1: np.array, y_pred2: np.array
  ) -> np.array:
      """Calculates the Q-statistics diversity measure between a pair of
      classifiers. The Q value is in a range [-1, 1]. Classifiers that tend to
      classify the same object correctly will have positive values of Q, and
      Q = 0 for two independent classifiers.
  
      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
      -------
      Q : The q-statistic measure between two classifiers
      """
      N00, N10, N01, N11 = _process_predictions(y, y_pred1, y_pred2)
      a = (N11 * N00) - (N01 * N10)
      b = (N11 * N00) + (N01 * N10)
      Q = np.divide(a, b, out=np.zeros_like(a), where=b != 0)
      return Q
  
file_path: DESlib/deslib/util/diversity_batch.py
imports: import sys
  import numpy
label_desc: Use image node_id 4 for calling a global function with example usage: Q_statistic(y, y_pred1, y_pred2) and returns: Q