code: def test_masked_probability(model, X, X_masked):
      X = torch.tensor(numpy.array(X))
      y = [
          1.668138e-02,
          1.911842e-02,
          4.393471e-03,
          1.633741e-03,
          9.786682e-02,
          1.229918e-04,
          1.585297e-01,
          6.066021e-03,
          2.571130e-01,
          9.765117e-03,
          1.114044e01,
      ]
  
      mask = torch.ones_like(X).type(torch.bool)
      X_ = torch.masked.MaskedTensor(X, mask=mask)
  
      assert_array_almost_equal(y, model.probability(X_), 5)
  
      y = [
          5.277007e-02,
          1.175627e00,
          1.000000e00,
          1.633741e-03,
          1.533307e-01,
          1.229918e-04,
          1.000000e00,
          1.880462e-02,
          2.571130e-01,
          9.765117e-03,
          3.424242e00,
      ]
  
      assert_array_almost_equal(y, model.probability(X_masked), 5)
  
imports: import os
  import numpy
  import torch
  import pytest
  from pomegranate.bayes_classifier import BayesClassifier
  from pomegranate.distributions import Exponential
  from .distributions._utils import _test_initialization_raises_one_parameter
  from .distributions._utils import _test_initialization
  from .distributions._utils import _test_predictions
  from .distributions._utils import _test_efd_from_summaries
  from .distributions._utils import _test_raises
  from nose.tools import assert_raises
  from numpy.testing import assert_array_almost_equal
label_desc: Use image node_id 37 for calling a global function with example usage: test_masked_probability(model, X, X_masked) without return types