code: def X_masked(X):
      mask = torch.tensor(
          numpy.array(
              [
                  [False, True, True, True],
                  [True, True, False, False],
                  [False, False, False, True],
                  [True, True, True, True],
                  [False, True, False, False],
                  [True, True, True, False],
                  [False, False, False, True],
                  [True, False, True, True],
                  [True, True, True, False],
                  [True, True, True, True],
                  [True, False, True, False],
              ]
          )
      )
  
      X = torch.tensor(numpy.array(X))
      return torch.masked.MaskedTensor(X, mask=mask)
  
imports: import numpy
  import torch
  import pytest
  from pomegranate.bayesian_network import BayesianNetwork
  from pomegranate.bayesian_network import _from_structure
  from pomegranate.bayesian_network import _learn_structure
  from pomegranate.bayesian_network import _categorical_exact
  from pomegranate.bayesian_network import _categorical_chow_liu
  from pomegranate.distributions import Categorical
  from pomegranate.distributions import ConditionalCategorical
  from nose.tools import assert_raises
  from numpy.testing import assert_array_almost_equal
label_desc: Use image node_id 3 for calling a global function with example usage: X_masked(X) and returns: torch