code: def test_from_summaries_frozen(X, w, probs):
      d = JointCategorical(probs, frozen=True)
      d.summarize(X[:4])
      assert_array_almost_equal(d._w_sum, [0.0, 0.0, 0.0])
      assert_array_almost_equal(d._xw_sum, numpy.zeros_like(probs))
  
      d.from_summaries()
      _test_efd_from_summaries(d, "probs", "_log_probs", probs)
  
      d.summarize(X[4:])
      assert_array_almost_equal(d._w_sum, [0.0, 0.0, 0.0])
      assert_array_almost_equal(d._xw_sum, numpy.zeros_like(probs))
  
      d.from_summaries()
      _test_efd_from_summaries(d, "probs", "_log_probs", probs)
  
      d = JointCategorical(probs, frozen=True)
      d.summarize(X)
      assert_array_almost_equal(d._w_sum, [0.0, 0.0, 0.0])
      assert_array_almost_equal(d._xw_sum, numpy.zeros_like(probs))
  
      d.from_summaries()
      _test_efd_from_summaries(d, "probs", "_log_probs", probs)
  
      d = JointCategorical(probs, frozen=True)
      d.summarize(X, sample_weight=w)
      assert_array_almost_equal(d._w_sum, [0.0, 0.0, 0.0])
      assert_array_almost_equal(d._xw_sum, numpy.zeros_like(probs))
  
      d.from_summaries()
      _test_efd_from_summaries(d, "probs", "_log_probs", probs)
  
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 27 for calling a global function with example usage: test_from_summaries_frozen(X, w, probs) without return types