code: def generate_data_combinations(n=10):
      """
      Generate datasets with appropriate (and random) distribution type.
  
      Yields
      ------
      ((X, Y, Z), (X_type, Y_type, Z_type)) : tuple
          (X, Y, Z) is data, and (X_type, Y_type, Z_type) are their respective
          distribution types.
      """
      datas = [Bin1d, Cat, Cont2d, Cont1d]
      dist_type = [
          Keyword_BINARY,
          Keyword_CATEGORY,
          Keyword_CONTINUOUS,
          Keyword_CONTINUOUS,
      ]
      K = len(datas)
      total_combinations = K**3
      combinations = np.random.choice(
          total_combinations, size=n
      ).tolist()
      for c in combinations:
          c_orig = c
          X = c % K
          c = (c - X) // K
          Y = c % K
          c = (c - Y) // K
          Z = c % K
          assert X + Y * K + Z * K * K == c_orig
          X_type = dist_type[X]
          X = datas[X]
          Y_type = dist_type[Y]
          Y = datas[Y]
          Z_type = dist_type[Z]
          Z = datas[Z]
          yield (X, Y, Z), (X_type, Y_type, Z_type)
  
imports: import sys
  import numpy
  from fairlearn.adversarial._adversarial_mitigation import _AdversarialFairness
  from fairlearn.adversarial._pytorch_engine import PytorchEngine
  from fairlearn.adversarial._tensorflow_engine import TensorflowEngine
  from fairlearn.adversarial._backend_engine import BackendEngine
label_desc: Use image node_id 2 for calling a global function with example usage: generate_data_combinations(n) without return types