code: def test_data_c() -> pd.DataFrame:
      """Test data created so that C should be perfectly predicted based on train_data_cpds.
  
      Given the two independent variables are set randomly (d, e), all other variables are set to be
      from the category with maximum likelihood in train_data_cpds"""
  
      data_arr = [
          ["a", "x", 1, False, False],
          ["b", "x", 2, False, True],
          ["c", "x", 3, True, False],
          ["d", "x", 4, True, True],
          ["d", "y", 1, False, False],
          ["c", "y", 2, False, True],
          ["b", "y", 23, True, False],
          ["a", "y", 64, True, True],
          ["c", "z", 1, False, False],
          ["a", "z", 2, False, True],
          ["d", "z", 3, True, False],
          ["b", "z", 0, True, True],
      ]
  
      data = pd.DataFrame(data_arr, columns=["a", "b", "c", "d", "e"])
      return data
  
imports: import itertools
  from typing import Dict
  import numpy
  import pandas
  import pytest
  from pgmpy.models import BayesianNetwork
  from sklearn.datasets import load_iris
  from causalnex.discretiser import Discretiser
  from causalnex.network import BayesianNetwork
  from causalnex.structure import StructureModel
  from causalnex.structure.notears import from_pandas
label_desc: Use image node_id 13 for calling a global function with example usage: test_data_c() and returns: data