code: def test_categorical_chow_liu_weighted(X, w):
      structure = _categorical_chow_liu(X, w)
      assert_tuple_equal(structure, ((), (0,), (0,), (0,)))
  
      structure = _categorical_chow_liu(X, w, root=1)
      assert_tuple_equal(structure, ((1,), (), (1,), (1,)))
  
      structure = _categorical_chow_liu(X, w, root=2)
      assert_tuple_equal(structure, ((2,), (2,), (), (2,)))
  
imports: import numpy
  import torch
  import pytest
  from pomegranate.bayesian_network import BayesianNetwork
  from pomegranate.bayesian_network import _from_structure
  from pomegranate.bayesian_network import _learn_structure
  from pomegranate.bayesian_network import _categorical_exact
  from pomegranate.bayesian_network import _categorical_chow_liu
  from pomegranate.distributions import Categorical
  from pomegranate.distributions import ConditionalCategorical
  from nose.tools import assert_raises
  from numpy.testing import assert_array_almost_equal
label_desc: Use image node_id 6 for calling a global function with example usage: test_categorical_chow_liu_weighted(X, w) without return types