code: def test_from_summaries(model, X, y):
      model.summarize(X, y)
      model.from_summaries()
  
      assert_array_almost_equal(model._w_sum, [0.0, 0.0])
      assert_array_almost_equal(model.priors, [6.0 / 11, 5.0 / 11])
      assert_array_almost_equal(
          model._log_priors, numpy.log([6.0 / 11, 5.0 / 11])
      )
  
      X_ = numpy.array(X)[numpy.array(y) == 0]
      d = Exponential().fit(X_)
  
      assert_array_almost_equal(d.scales, model.distributions[0].scales)
  
      model = BayesClassifier([Exponential(), Exponential()])
      model.summarize(X, y)
      model.from_summaries()
  
      assert_array_almost_equal(model._w_sum, [0.0, 0.0])
      assert_array_almost_equal(model.priors, [6.0 / 11, 5.0 / 11])
      assert_array_almost_equal(
          model._log_priors, numpy.log([6.0 / 11, 5.0 / 11])
      )
  
      assert_array_almost_equal(d.scales, model.distributions[0].scales)
  
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 26 for calling a global function with example usage: test_from_summaries(model, X, y) without return types