code: def make_banana(size_classes, na=0.1, random_state=None):
      """Generate the Banana dataset.
  
      Parameters
      ----------
      size_classes : list with the number of samples for each class.
  
      na : float (Default = 0.2),
          Noise amplitude. It must be < 1.0
  
      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
      ----------
      Kuncheva, Ludmila I. Combining pattern classifiers: methods and algorithms.
      John Wiley & Sons, 2004.
  
      """
      rng = check_random_state(random_state)
      if not isinstance(na, float) or na > 1.0:
          raise ValueError(
              "Parameter na must be a float lower than 1. na = {}".format(
                  na
              )
          )
  
      t1 = np.transpose(
          -np.linspace(-np.pi / 4, np.pi, size_classes[0])
      )
      z1 = np.transpose((np.sin(t1), np.cos(t1)))
      class_1 = 1.5 * z1 + rng.randn(size_classes[0], 2) * na
  
      t2 = np.transpose(
          -np.linspace(-np.pi / 4, np.pi, size_classes[1])
      )
      z2 = np.transpose((np.sin(t2), np.cos(t2)))
      class_2 = z2 - rng.randn(size_classes[1], 2) * na
      y = np.hstack(
          (np.zeros(size_classes[0]), np.ones(size_classes[1]))
      )
      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 3 for calling a global function with example usage: make_banana(size_classes, na, random_state) and returns: X,  y