code: def test_fit_fg(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._factor_graph.factors[0].probs,
          [
              [[0.1667, 0.0000], [0.0833, 0.0833]],
              [[0.0833, 0.0833], [0.0000, 0.1667]],
              [[0.0000, 0.1667], [0.0833, 0.0833]],
          ],
          4,
      )
      assert_array_almost_equal(
          model._factor_graph.factors[1].probs,
          [[0.1000, 0.2000, 0.2000], [0.1667, 0.2500, 0.0833]],
          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, [[0.4545, 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 28 for calling a global function with example usage: test_fit_fg(X, distributions) without return types