code: def test_reset_cache(X, y):
      d = [Exponential(), Exponential()]
  
      model = BayesClassifier(d)
      model.summarize(X, y)
  
      assert_array_almost_equal(model._w_sum, [6.0, 5.0])
      assert_array_almost_equal(
          model._log_priors, [-0.693147, -0.693147]
      )
  
      model._reset_cache()
      assert_array_almost_equal(model._w_sum, [0.0, 0.0])
      assert_array_almost_equal(
          model._log_priors, [-0.693147, -0.693147]
      )
  
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 8 for calling a global function with example usage: test_reset_cache(X, y) without return types