code: def test_masked_probability(probs, X, X_masked):
      X = torch.tensor(numpy.array(X))
      y = [0.0420, 0.0075, 0.0010, 0.0030, 0.0070, 0.0010, 0.0140]
  
      d = Categorical(probs)
      mask = torch.ones_like(X).type(torch.bool)
      X_ = torch.masked.MaskedTensor(X, mask=mask)
  
      assert_array_almost_equal(y, d.probability(X_))
  
      y = [
          2.1000e-01,
          1.5000e-01,
          1.0000e00,
          3.0000e-03,
          1.0000e-01,
          1.0000e-03,
          1.4000e-01,
      ]
      assert_array_almost_equal(y, d.probability(X_masked))
  
imports: import os
  import numpy
  import torch
  import pytest
  from pomegranate.distributions import Categorical
  from ._utils import _test_initialization_raises_one_parameter
  from ._utils import _test_predictions
  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(probs, X, X_masked) without return types