code: def test_from_summaries_weighted(X, w, mins, maxs):
      for param in mins, None:
          d = Uniform(mins, maxs)
          d.summarize(X[:4], sample_weight=w[:4])
          d.from_summaries()
          _test_fit_params(d, [0.5, 0.2, 0.5], [3.1, 2.1, 2.2])
  
          d.summarize(X[4:], sample_weight=w[4:])
          d.from_summaries()
          _test_fit_params(d, [2.2, 1.0, 0.1], [5.4, 1.9, 4.0])
  
          d = Uniform(mins, maxs)
          d.summarize(X, sample_weight=w)
          d.from_summaries()
          _test_fit_params(d, [0.5, 0.2, 0.1], [5.4, 2.1, 4.0])
  
imports: import os
  import numpy
  import torch
  import pytest
  from pomegranate.distributions import Uniform
  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_weighted(X, w, mins, maxs) without return types