code: def test_probability(X, probs):
      y = [0.04, 0.125, 0.04, 0.04, 0.05, 0.05, 0.053333]
  
      d1 = JointCategorical(probs)
      d2 = JointCategorical(numpy.array(probs, dtype=numpy.float64))
  
      _test_predictions(X, y, d1.probability(X), torch.float32)
      _test_predictions(X, y, d2.probability(X), torch.float64)
  
imports: import numpy
  import torch
  import pytest
  from pomegranate.distributions import JointCategorical
  from ._utils import _test_initialization_raises_one_parameter
  from ._utils import _test_initialization
  from ._utils import _test_predictions
  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 10 for calling a global function with example usage: test_probability(X, probs) without return types