code: def test_predict(X_masked, model, model2):
      y_hat = model.predict(X_masked)
      assert_array_almost_equal(
          y_hat,
          [
              [1, 2, 0, 0],
              [0, 0, 1, 0],
              [1, 0, 1, 0],
              [1, 2, 1, 1],
              [1, 1, 1, 0],
              [0, 1, 0, 0],
              [1, 0, 1, 0],
              [1, 1, 1, 1],
              [1, 1, 0, 0],
              [0, 2, 1, 0],
              [0, 0, 0, 0],
          ],
      )
  
      y_hat = model2.predict(X_masked)
      assert_array_almost_equal(
          y_hat,
          [
              [1, 2, 0, 0],
              [0, 0, 1, 0],
              [1, 0, 1, 0],
              [1, 2, 1, 1],
              [1, 1, 1, 1],
              [0, 1, 0, 0],
              [1, 0, 1, 0],
              [1, 1, 1, 1],
              [1, 1, 0, 1],
              [0, 2, 1, 0],
              [0, 0, 0, 0],
          ],
      )
  
imports: import numpy
  import torch
  import pytest
  from pomegranate.factor_graph import FactorGraph
  from pomegranate.distributions import Exponential
  from pomegranate.distributions import Categorical
  from pomegranate.distributions import JointCategorical
  from nose.tools import assert_raises
  from numpy.testing import assert_array_almost_equal
label_desc: Use image node_id 16 for calling a global function with example usage: test_predict(X_masked, model, model2) without return types