code: def test_predict_diamond(X_masked, distributions):
      d1, d2, d3, d4, d12, d22, d32, d13 = distributions
  
      model = BayesianNetwork(
          [d13, d22, d32, d4],
          [(d4, d22), (d4, d32), (d22, d13), (d32, d13)],
      )
      y_hat = model.predict(X_masked)
  
      assert_array_equal(
          y_hat,
          [
              [1, 2, 0, 0],
              [0, 0, 1, 1],
              [1, 0, 0, 0],
              [1, 2, 1, 1],
              [1, 1, 1, 1],
              [0, 1, 0, 1],
              [1, 0, 0, 0],
              [1, 0, 1, 1],
              [1, 1, 0, 1],
              [0, 2, 1, 0],
              [0, 0, 0, 0],
          ],
      )
  
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 24 for calling a global function with example usage: test_predict_diamond(X_masked, distributions) without return types