code: def test_masked_log_probability(model, X, X_masked):
      X = torch.tensor(numpy.array(X))
      y = [
          -4.09346,
          -3.9571,
          -5.42764,
          -6.41688,
          -2.32415,
          -9.00339,
          -1.84181,
          -5.10505,
          -1.35824,
          -4.62894,
          2.41058,
      ]
  
      mask = torch.ones_like(X).type(torch.bool)
      X_ = torch.masked.MaskedTensor(X, mask=mask)
  
      assert_array_almost_equal(y, model.log_probability(X_), 5)
  
      y = [
          -2.94181,
          0.1618,
          0.0,
          -6.41688,
          -1.87516,
          -9.00339,
          0.0,
          -3.97365,
          -1.35824,
          -4.62894,
          1.23088,
      ]
  
      assert_array_almost_equal(y, model.log_probability(X_masked), 5)
  
imports: import os
  import numpy
  import torch
  import pytest
  from pomegranate.gmm import GeneralMixtureModel
  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 38 for calling a global function with example usage: test_masked_log_probability(model, X, X_masked) without return types