code: def test_sample(distributions):
      torch.manual_seed(0)
  
      d1, d2, d3, d4, d12, d22, d32, d13 = distributions
      model = BayesianNetwork(
          [d1, d22, d13, d4], [(d1, d22), (d22, d13), (d4, d13)]
      )
  
      X = model.sample(1)
      assert_array_equal(X, [[1, 1, 1, 1]])
  
      X = model.sample(5)
      assert_array_equal(
          X,
          [
              [0, 0, 1, 1],
              [0, 0, 0, 1],
              [0, 2, 1, 0],
              [0, 1, 1, 1],
              [0, 0, 1, 1],
          ],
      )
  
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 12 for calling a global function with example usage: test_sample(distributions) without return types