code: def _quick_data(A_two_dim=False, n_groups=2):
      # Data are random and do not matter for these tests
      feature_1 = [0, 1, 2, 3, 4, 5, 6, 7]
      feature_2 = [5, 4, 3, 2, 7, 8, 3, 4]
      feature_3 = [9, 2, 4, 2, 9, 3, 1, 8]
      X = np.stack((feature_1, feature_2, feature_3), -1)
      Y = np.array([0, 1, 0, 1, 1, 1, 1, 0])
  
      if n_groups == 2:
          A = np.array([1, 0, 0, 0, 0, 1, 1, 1])
      elif n_groups == 3:
          A = np.array([1, 0, 2, 0, 0, 1, 2, 1])
      elif n_groups == 4:
          A = np.array([3, 0, 2, 0, 3, 1, 2, 1])
      elif n_groups == 5:
          A = np.array([3, 0, 2, 0, 3, 4, 2, 1])
      else:
          raise ValueError(
              "_quick_data only supports 2, 3, or 4 sensitive feature values."
          )
  
      if A_two_dim:
          # Grid Search is still restricted to binary sensitive features.
          # Even though we provide multiple columns of sensitive features,
          # the merged feature comprised of these columns can only have
          # two unique values.
          A = np.stack((A, A), -1)
      return X, Y, A
  
label_desc: Use image node_id 1 for calling a global function with example usage: _quick_data(A_two_dim, n_groups) and returns: X,  Y,  A