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 = StudentT(3)
      d.fit(X_)
      _test_fit_params(
          d,
          [2.442857, 1.385714, 1.514286],
          [2.402449, 0.418367, 1.666939],
      )
  
      d = StudentT(3)
      d.fit(X_masked)
      _test_fit_params(d, [2.8, 1.46, 1.7], [3.124999, 0.5624, 2.385])
  
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 44 for calling a global function with example usage: test_masked_fit(X, X_masked) without return types