code: def test_masked_probability(means, covs, X, X_masked):
      X = torch.tensor(numpy.array(X))
      y = [
          1.540306e-02,
          8.734769e-03,
          3.866756e-02,
          1.825929e-03,
          2.802034e-04,
          2.755452e-05,
          2.244095e-03,
      ]
  
      d = StudentT(3, means, covs)
      mask = torch.ones_like(X).type(torch.bool)
      X_ = torch.masked.MaskedTensor(X, mask=mask)
  
      assert_array_almost_equal(y, d.probability(X_)._masked_data)
  
      y = [
          2.346637e-02,
          3.611182e-02,
          1.000000e00,
          1.825928e-03,
          1.827392e-01,
          2.755453e-05,
          1.133557e-02,
      ]
  
      assert_array_almost_equal(y, d.probability(X_masked)._masked_data)
  
imports: import os
  import numpy
  import torch
  import pytest
  from pomegranate.distributions import StudentT
  from ._utils import _test_initialization_raises_two_parameters
  from ._utils import _test_initialization
  from ._utils import _test_predictions
  from ._utils import _test_efd_from_summaries
  from ._utils import _test_raises
  from nose.tools import assert_raises
  from numpy.testing import assert_array_almost_equal
label_desc: Use image node_id 40 for calling a global function with example usage: test_masked_probability(means, covs, X, X_masked) without return types