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 = Gamma()
      d.fit(X_)
      _test_fit_params(
          d,
          [2.231407, 2.637839, 1.037289],
          [0.913441, 1.903595, 0.685002],
          [-0.316346, 1.31193, -0.372041],
      )
  
      d = Gamma()
      d.fit(X_masked)
      _test_fit_params(
          d,
          [1.8081, 1.9674, 0.8058],
          [0.6457, 1.3475, 0.474],
          [-0.722, 0.6002, -0.7481],
      )
  
imports: import os
  import numpy
  import torch
  import pytest
  from pomegranate.distributions import Gamma
  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 45 for calling a global function with example usage: test_masked_fit(X, X_masked) without return types