code: def test_hmm_predict_log_proba(hmm, X, priors):
      X = numpy.array([X])
      priors = priors.unsqueeze(0)
  
      y_hat = hmm.predict_log_proba(X)
      assert_array_almost_equal(
          y_hat,
          [
              [
                  [-2.3343e00, -1.0190e-01],
                  [-5.5721e00, -3.8109e-03],
                  [-1.8306e01, 0.0000e00],
                  [-1.9578e01, 0.0000e00],
                  [-2.7591e-01, -1.4225e00],
                  [-3.3558e01, 0.0000e00],
                  [-3.2527e-01, -1.2813e00],
                  [-1.2301e01, -3.8147e-06],
                  [-3.8184e-01, -1.1476e00],
                  [-8.7126e00, -1.6403e-04],
                  [-9.1934e-03, -4.6938e00],
              ]
          ],
          3,
      )
  
      y_hat = hmm.predict_log_proba(X, priors=priors)
      assert_array_almost_equal(
          y_hat,
          [
              [
                  [-2.3343e00, -1.0190e-01],
                  [-5.5721e00, -3.8109e-03],
                  [-1.8306e01, 0.0000e00],
                  [-inf, 0.0000e00],
                  [-2.7591e-01, -1.4225e00],
                  [-3.4329e01, 0.0000e00],
                  [-2.2815e-01, -1.5897e00],
                  [-1.3709e01, 0.0000e00],
                  [-inf, 0.0000e00],
                  [0.0000e00, -inf],
                  [-1.5373e-03, -6.4778e00],
              ]
          ],
          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 39 for calling a global function with example usage: test_hmm_predict_log_proba(hmm, X, priors) without return types