code: def test_log_probability(X, distributions):
      d1, d2, d3, d4, d12, d22, d32, d13 = distributions
  
      model = BayesianNetwork(
          [d1, d22, d13, d4], [(d1, d22), (d22, d13), (d4, d13)]
      )
      logps = model.log_probability(X)
  
      assert_array_almost_equal(
          logps,
          [
              -7.013116,
              -2.700082,
              -5.115996,
              -7.264431,
              -5.184989,
              -4.491842,
              -4.422849,
              -3.709082,
              -5.184989,
              -3.393229,
              -2.140466,
          ],
      )
  
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 13 for calling a global function with example usage: test_log_probability(X, distributions) without return types