code: def test_log_probability(X, distributions):
      y = [
          -5.322324,
          -5.43448,
          -3.391639,
          -5.475026,
          -5.391639,
          -6.544341,
          -4.391639,
      ]
  
      d = IndependentComponents(distributions)
      _test_predictions(X, y, d.log_probability(X), torch.float32)
  
      d1 = Exponential([1.2])
      d2 = Exponential([7.5])
      d3 = Exponential([1.2, 7.5])
      d = IndependentComponents([d1, d2])
  
      x = [[1.0, 2.0], [2.0, 2.0], [0.0, 1.0], [0.0, 0.0]]
      y = [-3.297225, -4.130558, -2.330558, -2.197225]
      _test_predictions(x, y, d.log_probability(x), torch.float32)
      _test_predictions(
          x, d.log_probability(x), d3.log_probability(x), torch.float32
      )
  
imports: import os
  import numpy
  import torch
  import pytest
  from pomegranate.distributions import Exponential
  from pomegranate.distributions import Gamma
  from pomegranate.distributions import Categorical
  from pomegranate.distributions import IndependentComponents
  from ._utils import _test_initialization_raises_one_parameter
  from ._utils import _test_initialization
  from ._utils import _test_predictions
  from ._utils import _test_efd_from_summaries
  from ._utils import _test_raises
  from nose.tools import assert_raises
  from numpy.testing import assert_array_almost_equal
label_desc: Use image node_id 12 for calling a global function with example usage: test_log_probability(X, distributions) without return types