code: def test_from_summaries_weighted_inertia(X, w, probs):
      d = JointCategorical(probs, inertia=0.3)
      d.summarize(X, sample_weight=w)
      d.from_summaries()
      _test_efd_from_summaries(
          d,
          "probs",
          "_log_probs",
          [
              [[0.0125, 0.1827], [0.1353, 0.0340], [0.0250, 0.0250]],
              [[0.0050, 0.0450], [0.3935, 0.0350], [0.0690, 0.0380]],
          ],
      )
  
      d = JointCategorical(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 = JointCategorical(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 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 26 for calling a global function with example usage: test_from_summaries_weighted_inertia(X, w, probs) without return types