code: def test_predict_log_proba(model, X):
      y_hat = model.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,
      )
  
      model2 = GeneralMixtureModel(model.distributions)
      y_hat2 = model2.predict_log_proba(X)
      assert_array_almost_equal(
          y_hat2,
          [
              [-1.1308e00, -3.8974e-01],
              [-4.4671e00, -1.1548e-02],
              [-1.6821e01, 0.0000e00],
              [-1.9642e01, 0.0000e00],
              [-6.8226e-02, -2.7188e00],
              [-3.5150e01, 0.0000e00],
              [-8.1969e-02, -2.5421e00],
              [-1.3810e01, -9.5367e-07],
              [-9.8348e-02, -2.3680e00],
              [-1.0477e01, -2.8133e-05],
              [-6.1395e-03, -5.0961e00],
          ],
          3,
      )
  
imports: import os
  import numpy
  import torch
  import pytest
  from pomegranate.gmm import GeneralMixtureModel
  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 18 for calling a global function with example usage: test_predict_log_proba(model, X) without return types