code: def test_from_summaries_frozen(X, w, shapes, rates):
      d = Gamma(shapes, rates, 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, [0.0, 0.0, 0.0])
  
      d.from_summaries()
      _test_fit_params(
          d, shapes, rates, [-1.359393, 2.107607, 0.337438]
      )
  
      d.summarize(X[4:])
      assert_array_almost_equal(d._w_sum, [0.0, 0.0, 0.0])
      assert_array_almost_equal(d._xw_sum, [0.0, 0.0, 0.0])
  
      d.from_summaries()
      _test_fit_params(
          d, shapes, rates, [-1.359393, 2.107607, 0.337438]
      )
  
      d = Gamma(shapes, rates, 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, [0.0, 0.0, 0.0])
  
      d.from_summaries()
      _test_fit_params(
          d, shapes, rates, [-1.359393, 2.107607, 0.337438]
      )
  
      d = Gamma(shapes, rates, 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, [0.0, 0.0, 0.0])
  
      d.from_summaries()
      _test_fit_params(
          d, shapes, rates, [-1.359393, 2.107607, 0.337438]
      )
  
imports: import os
  import numpy
  import torch
  import pytest
  from pomegranate.distributions import Gamma
  from ._utils import _test_initialization_raises_two_parameters
  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 33 for calling a global function with example usage: test_from_summaries_frozen(X, w, shapes, rates) without return types