code: def test_summarize_weighted_flat(X, w, probs):
      w = numpy.array(w)[:, 0]
  
      for param in probs, None:
          d = Categorical(param)
          d.summarize(X[:4], sample_weight=w[:4])
          assert_array_almost_equal(d._w_sum, [3.0, 3.0, 3.0])
          assert_array_almost_equal(
              d._xw_sum,
              [
                  [0.0, 1.0, 0.0, 2.0],
                  [2.0, 0.0, 1.0, 0.0],
                  [1.0, 2.0, 0.0, 0.0],
              ],
          )
  
          d.summarize(X[4:], sample_weight=w[4:])
          assert_array_almost_equal(d._w_sum, [11.0, 11.0, 11.0])
          assert_array_almost_equal(
              d._xw_sum,
              [
                  [5.0, 2.0, 2.0, 2.0],
                  [2.0, 8.0, 1.0, 0.0],
                  [8.0, 2.0, 1.0, 0.0],
              ],
          )
  
          d = Categorical(param)
          d.summarize(X, sample_weight=w)
          assert_array_almost_equal(d._w_sum, [11.0, 11.0, 11.0])
          assert_array_almost_equal(
              d._xw_sum,
              [
                  [5.0, 2.0, 2.0, 2.0],
                  [2.0, 8.0, 1.0, 0.0],
                  [8.0, 2.0, 1.0, 0.0],
              ],
          )
  
imports: import os
  import numpy
  import torch
  import pytest
  from pomegranate.distributions import Categorical
  from ._utils import _test_initialization_raises_one_parameter
  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 21 for calling a global function with example usage: test_summarize_weighted_flat(X, w, probs) without return types