code: def test_fit_weighted(X, w, probs):
      for param in probs, None:
          d = Categorical(probs)
          d.fit(X[:4], sample_weight=w[:4])
          _test_fit_params(
              d,
              [
                  [0.0, 0.333333, 0.0, 0.666667],
                  [0.666667, 0.0, 0.333333, 0.0],
                  [0.333333, 0.666667, 0.0, 0.0],
              ],
          )
  
          d.fit(X[4:], sample_weight=w[4:])
          _test_fit_params(
              d,
              [
                  [0.625, 0.125, 0.25, 0.0],
                  [0.0, 1.0, 0.0, 0.0],
                  [0.875, 0.0, 0.125, 0.0],
              ],
          )
  
          d = Categorical(probs)
          d.fit(X, sample_weight=w)
          _test_fit_params(
              d,
              [
                  [0.454545, 0.181818, 0.181818, 0.181818],
                  [0.181818, 0.727273, 0.090909, 0.0],
                  [0.727273, 0.181818, 0.090909, 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 35 for calling a global function with example usage: test_fit_weighted(X, w, probs) without return types