code: def test_from_summaries_weighted_inertia(X, w, probs):
      d = ConditionalCategorical(probs, inertia=0.3)
      d.summarize(X, sample_weight=w)
      d.from_summaries()
      _test_efd_from_summaries(
          d,
          "probs",
          "_log_probs",
          [
              [[0.7750, 0.2250], [0.6835, 0.3165]],
              [
                  [0.38, 0.62],
                  [0.3889583, 0.61104167],
                  [0.5745641, 0.4254359],
              ],
              [[0.805, 0.195], [0.270, 0.730]],
          ],
      )
  
      d = ConditionalCategorical(probs, inertia=1.0)
      d.summarize(X[:4])
      d.from_summaries()
      _test_efd_from_summaries(d, "probs", "_log_probs", probs)
  
      d.summarize(X[4:])
      d.from_summaries()
      _test_efd_from_summaries(d, "probs", "_log_probs", probs)
  
      d = ConditionalCategorical(probs, inertia=1.0)
      d.summarize(X)
      d.from_summaries()
      _test_efd_from_summaries(d, "probs", "_log_probs", probs)
  
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 27 for calling a global function with example usage: test_from_summaries_weighted_inertia(X, w, probs) without return types