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)
  
      d = Exponential()
      d.summarize(X, sample_weight=w)
      assert_array_almost_equal(d._w_sum, [11.0, 11.0, 11.0])
      assert_array_almost_equal(d._xw_sum, [25.0, 10.0, 6.0])
  
      d = Exponential()
      d.summarize(X_masked)
      assert_array_almost_equal(d._w_sum, [4.0, 5.0, 4.0])
      assert_array_almost_equal(d._xw_sum, [9.0, 6.0, 6.0])
  
imports: import os
  import numpy
  import torch
  import pytest
  from pomegranate.distributions import Exponential
  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 42 for calling a global function with example usage: test_masked_summarize(X, X_masked, w) without return types