code: def test_summarize_weighted_flat(model, X, y, w):
      w = numpy.array(w)[:, 0]
  
      model.summarize(X, y, sample_weight=w)
      assert_array_almost_equal(model._w_sum, [7.0, 8.0])
  
      model = BayesClassifier([Exponential(), Exponential()])
      model.summarize(X, y, sample_weight=w)
      assert_array_almost_equal(model._w_sum, [7.0, 8.0])
  
imports: import os
  import numpy
  import torch
  import pytest
  from pomegranate.bayes_classifier import BayesClassifier
  from pomegranate.distributions import Exponential
  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 23 for calling a global function with example usage: test_summarize_weighted_flat(model, X, y, w) without return types