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)
  
      d1 = Exponential([1.0])
      d2 = Gamma([1.0], [2.0])
      d3 = Exponential([4.0])
      d = IndependentComponents([d1, d2, d3]).fit(X_masked)
      assert_array_almost_equal(d1.scales, [2.25])
      assert_array_almost_equal(d2.rates, [3.14873], 4)
      assert_array_almost_equal(d2.shapes, [5.667715], 4)
      assert_array_almost_equal(d3.scales, [0.5])
  
imports: import os
  import numpy
  import torch
  import pytest
  from pomegranate.distributions import Exponential
  from pomegranate.distributions import Gamma
  from pomegranate.distributions import Categorical
  from pomegranate.distributions import IndependentComponents
  from ._utils import _test_initialization_raises_one_parameter
  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 30 for calling a global function with example usage: test_masked_fit(X, X_masked) without return types