code: def test_gmm_predict_log_proba(gmm, X, priors):
      y_hat = gmm.predict_log_proba(X)
      assert_array_almost_equal(
          y_hat,
          [
              [-6.4145e-01, -7.4766e-01],
              [-3.6350e00, -2.6740e-02],
              [-1.5974e01, 0.0000e00],
              [-1.8794e01, 0.0000e00],
              [-2.9812e-02, -3.5277e00],
              [-3.4303e01, 0.0000e00],
              [-3.5955e-02, -3.3434e00],
              [-1.2963e01, -2.3842e-06],
              [-4.3338e-02, -3.1603e00],
              [-9.6298e00, -6.5804e-05],
              [-2.6357e-03, -5.9399e00],
          ],
          3,
      )
  
      y_hat = gmm.predict_log_proba(X, priors=priors)
      assert_array_almost_equal(
          y_hat,
          [
              [-6.4145e-01, -7.4766e-01],
              [-3.6350e00, -2.6740e-02],
              [-1.5974e01, 0.0000e00],
              [-inf, 0.0000e00],
              [-2.9812e-02, -3.5277e00],
              [-3.5150e01, 0.0000e00],
              [-2.4113e-02, -3.7370e00],
              [-1.2963e01, -2.3842e-06],
              [-inf, 0.0000e00],
              [0.0000e00, -inf],
              [-2.6358e-03, -5.9399e00],
          ],
          3,
      )
  
imports: import os
  import numpy
  import torch
  import pytest
  from pomegranate.gmm import GeneralMixtureModel
  from pomegranate.hmm import DenseHMM
  from pomegranate.distributions import Exponential
  from nose.tools import assert_raises
  from numpy.testing import assert_array_equal
  from numpy.testing import assert_array_almost_equal
label_desc: Use image node_id 16 for calling a global function with example usage: test_gmm_predict_log_proba(gmm, X, priors) without return types