code: def bad_cpd(empty_cpd) -> pd.DataFrame:
      """Create a bad CPD table which does not satisfy probability distribution properties"""
      df = empty_cpd
      df.loc[:] = [
          [0.2, 1.0, 0.4, 0.1],
          [0.7, 2.0, 3.0, 0.1],
          [0.3, 1.0, 0.6, 5.8],
      ]
      return df
  
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 19 for calling a global function with example usage: bad_cpd(empty_cpd) and returns: df