code: def test_summarize_weighted(X, X2, w, w2, scales):
      d = Exponential(scales)
      d.summarize(X[:4], sample_weight=w[:4])
      assert_array_almost_equal(d._w_sum, [3.0, 3.0, 3.0])
      assert_array_almost_equal(d._xw_sum, [1.0, 2.0, 2.0])
  
      d.summarize(X[4:], sample_weight=w[4:])
      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(scales)
      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(X2, sample_weight=w2)
      assert_array_almost_equal(d._w_sum, [4.6, 4.6, 4.6, 4.6])
      assert_array_almost_equal(d._xw_sum, [23.02, 4.4, 9.61, 5.94])
  
      d = Exponential([0, 0, 0, 0])
      d.summarize(X2, sample_weight=w2)
      assert_array_almost_equal(d._w_sum, [4.6, 4.6, 4.6, 4.6])
      assert_array_almost_equal(d._xw_sum, [23.02, 4.4, 9.61, 5.94])
  
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 21 for calling a global function with example usage: test_summarize_weighted(X, X2, w, w2, scales) without return types