code: def test_initialization():
      d = [Exponential(), Exponential()]
      model = BayesClassifier(d)
  
      _test_initialization(model, None, "priors", 0.0, False, None)
      assert_raises(AttributeError, getattr, model, "_w_sum")
      assert_raises(AttributeError, getattr, model, "_log_priors")
  
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 6 for calling a global function with example usage: test_initialization() without return types