code: def make_xor(n_samples, random_state=None):
      """Generate the exclusive-or (XOR) dataset.
  
      Parameters
      ----------
      n_samples : int
                  Number of generated data points.
  
      random_state : int, RandomState instance or None, optional (default=None)
          If int, random_state is the seed used by the random number generator;
          If RandomState instance, random_state is the random number generator;
          If None, the random number generator is the RandomState instance used
          by `np.random`.
  
      Returns
      -------
      X : array of shape = [size_classes, 2]
          The generated data points.
  
      y : array of shape = [size_classes]
          Class labels associated with each class.
  
      """
      rng = check_random_state(random_state)
      X = rng.uniform(low=0, high=1, size=(n_samples, 2))
      y = np.logical_xor(X[:, 0] > 0.5, X[:, 1] > 0.5)
  
      return X, y
  
imports: import numpy
  from sklearn.utils.validation import check_random_state
label_desc: Use image node_id 5 for calling a global function with example usage: make_xor(n_samples, random_state) and returns: X,  y