code: def test_predict_proba(model, X):
      y_hat = model.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,
      )
  
      model2 = GeneralMixtureModel(model.distributions)
      y_hat2 = model2.predict_proba(X)
      assert_array_almost_equal(
          y_hat2,
          [
              [3.2277e-01, 6.7723e-01],
              [1.1481e-02, 9.8852e-01],
              [4.9503e-08, 1.0000e00],
              [2.9498e-09, 1.0000e00],
              [9.3405e-01, 6.5951e-02],
              [5.4255e-16, 1.0000e00],
              [9.2130e-01, 7.8700e-02],
              [1.0050e-06, 1.0000e00],
              [9.0633e-01, 9.3666e-02],
              [2.8176e-05, 9.9997e-01],
              [9.9388e-01, 6.1207e-03],
          ],
          4,
      )
  
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 16 for calling a global function with example usage: test_predict_proba(model, X) without return types