code: def test_masked_summarize(X, X_masked, w):
      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])
      d.summarize(X_masked)
      assert_array_almost_equal(d1._w_sum, [4.0])
      assert_array_almost_equal(d1._xw_sum, [9.0])
      assert_array_almost_equal(d2._w_sum, [5.0])
      assert_array_almost_equal(d2._xw_sum, [9.0])
      assert_array_almost_equal(d3._w_sum, [4.0])
      assert_array_almost_equal(d3._xw_sum, [2.0])
  
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 28 for calling a global function with example usage: test_masked_summarize(X, X_masked, w) without return types