code: def test_gmm_predict_proba(gmm, X, priors):
      y_hat = gmm.predict_proba(X)
      assert_array_almost_equal(
          y_hat,
          [
              [5.2653e-01, 4.7347e-01],
              [2.6385e-02, 9.7361e-01],
              [1.1551e-07, 1.0000e00],
              [6.8830e-09, 1.0000e00],
              [9.7063e-01, 2.9372e-02],
              [1.2660e-15, 1.0000e00],
              [9.6468e-01, 3.5317e-02],
              [2.3451e-06, 1.0000e00],
              [9.5759e-01, 4.2413e-02],
              [6.5741e-05, 9.9993e-01],
              [9.9737e-01, 2.6323e-03],
          ],
          4,
      )
  
      y_hat = gmm.predict_proba(X, priors=priors)
      assert_array_almost_equal(
          y_hat,
          [
              [5.2653e-01, 4.7347e-01],
              [2.6385e-02, 9.7361e-01],
              [1.1551e-07, 1.0000e00],
              [0.0000e00, 1.0000e00],
              [9.7063e-01, 2.9372e-02],
              [5.4256e-16, 1.0000e00],
              [9.7618e-01, 2.3825e-02],
              [2.3451e-06, 1.0000e00],
              [0.0000e00, 1.0000e00],
              [1.0000e00, 0.0000e00],
              [9.9737e-01, 2.6323e-03],
          ],
          4,
      )
  
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 14 for calling a global function with example usage: test_gmm_predict_proba(gmm, X, priors) without return types