code: def create_X_y():
      # ex1: The distribution of samples of a test example.
      X = np.array(
          [
              [-1, 1],
              [-0.75, 0.5],
              [-1.5, 1.5],
              [1, 1],
              [0.75, 0.5],
              [1.5, 1.5],
              [1, -1],
              [-0.5, 0.5],
              [0.5, 0.5],
              [0, -1],
              [0.75, -0.5],
              [0.0, 0.0],
              [-1, -1],
              [0, -0.5],
              [1, -1],
          ]
      )
      # Labels associated with the samples. This information is used
      # by techniques based on a posteriori information.
      y = np.array([0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0])
      return X, y
  
file_path: DESlib/deslib/tests/util/test_diversity_batch.py
imports: import numpy
  import pytest
  from deslib.util.diversity_batch import _process_predictions, double_fault, Q_statistic, ratio_errors, agreement_measure, disagreement_measure, correlation_coefficient
label_desc: Use image node_id 1 for calling a global function with example usage: create_X_y() and returns: X,  y