code: def test_fit(X, distributions):
      d1, d2, d3, d4, d12, d22, d32, d13 = distributions
      model = BayesianNetwork(
          [d13, d22, d32, d4],
          [(d4, d22), (d4, d32), (d22, d13), (d32, d13)],
      )
  
      model.fit(X)
      assert_array_almost_equal(
          model.distributions[0].probs[0],
          [
              [[1.0000, 0.0000], [0.5000, 0.5000]],
              [[0.5000, 0.5000], [0.0000, 1.0000]],
              [[0.0000, 1.0000], [0.5000, 0.5000]],
          ],
      )
      assert_array_almost_equal(
          model.distributions[1].probs[0],
          [[0.2000, 0.4000, 0.4000], [0.3333, 0.5000, 0.1667]],
          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, [[0.4545, 0.5455]], 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 25 for calling a global function with example usage: test_fit(X, distributions) without return types