code: def test_masked_summarize(model, 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)
  
      model.summarize(X_, sample_weight=w)
      assert_array_almost_equal(
          model._w_sum, [[7.0, 7.0, 7.0], [8.0, 8.0, 8.0]]
      )
      assert_array_almost_equal(
          model._xw_sum, [[7.0, 6.0, 10.0], [20.0, 9.0, 0.0]]
      )
  
      model = KMeans(2)
      model.summarize(X_masked, sample_weight=w)
      assert_array_almost_equal(
          model._w_sum, [[4.0, 10.0, 5.0], [3.0, 2.0, 1.0]]
      )
      assert_array_almost_equal(
          model._xw_sum, [[6.0, 13.0, 6.0], [1.0, 0.0, 2.0]]
      )
  
imports: import os
  import numpy
  import torch
  import pytest
  from pomegranate.kmeans import KMeans
  from .distributions._utils import _test_initialization_raises_one_parameter
  from .distributions._utils import _test_initialization
  from .distributions._utils import _test_predictions
  from .distributions._utils import _test_efd_from_summaries
  from .distributions._utils import _test_raises
  from nose.tools import assert_raises
  from numpy.testing import assert_array_almost_equal
label_desc: Use image node_id 31 for calling a global function with example usage: test_masked_summarize(model, X, X_masked, w) without return types