code: def test_probability(X, probs):
      y = [0.08512, 0.00768, 0.18088, 0.02688, 0.0784, 0.0204, 0.02625]
  
      d1 = ConditionalCategorical(probs)
      d2 = ConditionalCategorical(
          [numpy.array(prob, dtype=numpy.float64) for prob in probs]
      )
  
      _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 ConditionalCategorical
  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 11 for calling a global function with example usage: test_probability(X, probs) without return types