code: def test_masked_log_probability(means, covs, X, X_masked):
      X = torch.tensor(numpy.array(X))
      y = [
          -4.173189,
          -4.740443,
          -3.252754,
          -6.305667,
          -8.179995,
          -10.499344,
          -6.099453,
      ]
  
      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.log_probability(X_)._masked_data)
  
      y = [
          -3.752187,
          -3.321135,
          0.0,
          -6.305667,
          -1.699695,
          -10.499344,
          -4.47981,
      ]
  
      assert_array_almost_equal(
          y, d.log_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 41 for calling a global function with example usage: test_masked_log_probability(means, covs, X, X_masked) without return types