code: def test_summarize_weighted_flat(X, w, means, covs):
      w = numpy.array(w)[:, 0]
  
      d = StudentT(3, means, covs)
      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, [2.1, 2.5, 3.1])
      assert_array_almost_equal(d._xxw_sum, [1.71, 4.489999, 3.63])
  
      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, [28.9, 12.0, 8.799999])
      assert_array_almost_equal(d._xxw_sum, [98.350006, 16.48, 20.1])
  
      d = StudentT(3, means, covs)
      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, [28.9, 12.0, 8.799999])
      assert_array_almost_equal(d._xxw_sum, [98.350006, 16.48, 20.1])
  
imports: import os
  import numpy
  import torch
  import pytest
  from pomegranate.distributions import StudentT
  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 21 for calling a global function with example usage: test_summarize_weighted_flat(X, w, means, covs) without return types