code: def make_banana2(size_classes, sigma=1, random_state=None):
      """Generate the Banana dataset similar to the Matlab PRTools toolbox.
  
      Parameters
      ----------
      size_classes : list with the number of samples for each class.
  
      sigma : float (Default = 1),
          variance of the normal distribution
  
      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
      ----------
      R.P.W. Duin, P. Juszczak, D.de Ridder, P. Paclik, E. Pekalska, D.M.Tax,
      Prtools, a matlab toolbox for
      pattern recognition, 2004. URL 〈http://www.prtools.org〉.
  
      """
      rng = check_random_state(random_state)
      banana_size = 5
      region_class_1 = (
          0.125 * np.pi + rng.rand(size_classes[0]) * 1.25 * np.pi
      )
      data_class_1 = (
          banana_size
          * np.transpose(
              [np.sin(region_class_1), np.cos(region_class_1)]
          )
          + rng.randn(size_classes[0], 2) * sigma
      )
  
      region_class_2 = (
          0.375 * np.pi - rng.rand(size_classes[1]) * 1.25 * np.pi
      )
  
      tmp = (
          np.transpose([np.sin(region_class_2), np.cos(region_class_2)])
          * banana_size
      )
  
      data_class_2 = (tmp + rng.randn(size_classes[1], 2) * sigma) + (
          np.ones((size_classes[1], 2)) * (-0.75 * banana_size)
      )
  
      X = np.vstack((data_class_1, data_class_2))
      y = np.hstack(
          (np.zeros(size_classes[0]), np.ones(size_classes[1]))
      )
  
      return X, y
  
imports: import numpy
  from sklearn.utils.validation import check_random_state
label_desc: Use image node_id 4 for calling a global function with example usage: make_banana2(size_classes, sigma, random_state) and returns: X,  y