code: def test_fit_chain(X, y):
      model = BayesClassifier([Exponential(), Exponential()]).fit(X, y)
  
      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])
      )
  
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 34 for calling a global function with example usage: test_fit_chain(X, y) without return types