code: def test_from_summaries_inertia(X, w, means, covs):
      d = Normal(means, covs, covariance_type="full", inertia=0.3)
      d.summarize(X[:4])
      d.from_summaries()
      _test_fit_params(
          d,
          [1.4275, 1.5025, 1.715],
          [
              [0.742312, 0.392687, 0.287875],
              [0.392687, 1.068812, 0.149375],
              [0.287875, 0.149375, 0.71175],
          ],
      )
  
      d.summarize(X[4:])
      d.from_summaries()
      _test_fit_params(
          d,
          [2.994917, 1.43075, 1.541167],
          [
              [1.44225, 0.37914, 1.651251],
              [0.37914, 0.418644, 0.481146],
              [1.651251, 0.481146, 2.464414],
          ],
      )
  
      d = Normal(means, covs, covariance_type="full", inertia=0.3)
      d.summarize(X)
      d.from_summaries()
      _test_fit_params(
          d,
          [2.07, 1.51, 1.69],
          [
              [1.771715, 0.358429, 0.804572],
              [0.358429, 0.922857, 0.349143],
              [0.804572, 0.349143, 1.526857],
          ],
      )
  
imports: import os
  import numpy
  import torch
  import pytest
  from pomegranate.distributions import Normal
  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 27 for calling a global function with example usage: test_from_summaries_inertia(X, w, means, covs) without return types