code: def test_learn_structure_chow_liu(X):
      X = numpy.array(X)
      X[:, 1] = X[:, 3]
  
      model = BayesianNetwork(algorithm="chow-liu")
      model.fit(X)
  
      assert_tuple_equal(model._parents, ((), (0,), (1,), (1,)))
  
      assert_array_almost_equal(
          model.distributions[0].probs, [[0.4545, 0.5455]], 4
      )
      assert_array_almost_equal(
          model.distributions[1].probs[0],
          [[0.6000, 0.4000], [0.3333, 0.6667]],
          4,
      )
      assert_array_almost_equal(
          model.distributions[2].probs[0],
          [[0.4000, 0.6000], [0.6667, 0.3333]],
          4,
      )
      assert_array_almost_equal(
          model.distributions[3].probs[0], [[1.0, 0.0], [0.0, 1.0]], 4
      )
  
      assert_array_almost_equal(
          model._factor_graph.factors[0].probs, [[0.4545, 0.5455]], 4
      )
      assert_array_almost_equal(
          model._factor_graph.factors[1].probs,
          [[0.3000, 0.2000], [0.1667, 0.3333]],
          4,
      )
      assert_array_almost_equal(
          model._factor_graph.factors[2].probs,
          [[0.2000, 0.3000], [0.3333, 0.1667]],
          4,
      )
      assert_array_almost_equal(
          model._factor_graph.factors[3].probs,
          [[0.5000, 0.0000], [0.0000, 0.5000]],
          4,
      )
  
imports: import numpy
  import torch
  import pytest
  from pomegranate.bayesian_network import BayesianNetwork
  from pomegranate.distributions import Exponential
  from pomegranate.distributions import Categorical
  from pomegranate.distributions import ConditionalCategorical
  from nose.tools import assert_raises
  from numpy.testing import assert_array_equal
  from numpy.testing import assert_array_almost_equal
label_desc: Use image node_id 31 for calling a global function with example usage: test_learn_structure_chow_liu(X) without return types