code: def test_masked_fit(X, X_masked):
      X = torch.tensor(numpy.array(X))
      mask = torch.ones_like(X).type(torch.bool)
      X_ = torch.masked.MaskedTensor(X, mask=mask)
  
      d = Categorical()
      d.fit(X_)
      _test_efd_from_summaries(
          d,
          "probs",
          "_log_probs",
          [
              [0.142857, 0.428571, 0.285714, 0.142857],
              [0.142857, 0.571429, 0.285714, 0.0],
              [0.428571, 0.142857, 0.428571, 0.0],
          ],
      )
  
      d = Categorical()
      d.fit(X_masked)
      _test_efd_from_summaries(
          d,
          "probs",
          "_log_probs",
          [
              [0.0, 0.25, 0.5, 0.25],
              [0.2, 0.4, 0.4, 0.0],
              [0.5, 0.0, 0.5, 0.0],
          ],
      )
  
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 44 for calling a global function with example usage: test_masked_fit(X, X_masked) without return types