code: def test_masked_summarize(X, X_masked, w):
      X = torch.tensor(numpy.array(X))
      mask = torch.ones_like(X).type(torch.bool)
      X_ = torch.masked.MaskedTensor(X, mask=mask)
  
      d = Categorical()
      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],
          ],
      )
  
      d = Categorical()
      d.summarize(X_masked)
      assert_array_almost_equal(d._w_sum, [4.0, 5.0, 4.0])
      assert_array_almost_equal(
          d._xw_sum,
          [
              [0.0, 1.0, 2.0, 1.0],
              [1.0, 2.0, 2.0, 0.0],
              [2.0, 0.0, 2.0, 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 42 for calling a global function with example usage: test_masked_summarize(X, X_masked, w) without return types