code: def make_circle_square(size_classes, random_state=None):
      """Generate the circle square dataset.
  
      Parameters
      ----------
      size_classes : list with the number of samples for each class.
  
      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.
  
      References
      ----------
      P. Henniges, E. Granger, R. Sabourin, Factors of overtraining
      with fuzzy artmap neural networks, International Joint Conference
      on Neural Networks (2005) 1075–1080.
  
      """
      rng = check_random_state(random_state)
      n_samples = sum(size_classes)
      class_1 = np.zeros((size_classes[0], 2))
      class_2 = np.zeros((size_classes[1], 2))
      size_class1 = 0
      size_class2 = 0
      data = rng.rand(n_samples * 10, 2)
      r = 0.398942
      for x in data:
          test_class = ((x[0] - 0.5) ** 2) + ((x[1] - 0.5) ** 2)
          if test_class < (r**2):
              if size_class1 < size_classes[0]:
                  class_1[size_class1] = x
                  size_class1 += 1
  
          elif size_class2 < size_classes[1]:
              class_2[size_class2] = x
              size_class2 += 1
  
          if size_class2 + size_class1 >= n_samples:
              break
  
      y = np.hstack((np.zeros(size_class1), np.ones(size_class2)))
      X = np.vstack((class_1, class_2))
  
      return X, y
  
imports: import numpy
  from sklearn.utils.validation import check_random_state
label_desc: Use image node_id 2 for calling a global function with example usage: make_circle_square(size_classes, random_state) and returns: X,  y