code: def test_fit_weighted(X, w, means, covs):
      for param in means, None:
          d = Normal(means, covs, covariance_type="full")
          d.fit(X, sample_weight=w)
          _test_fit_params(
              d,
              [2.627273, 1.090909, 0.8],
              [
                  [2.038348, 0.360248, 0.533636],
                  [0.360248, 0.308099, 0.120909],
                  [0.533636, 0.120909, 1.187273],
              ],
          )
  
imports: import os
  import numpy
  import torch
  import pytest
  from pomegranate.distributions import Normal
  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 33 for calling a global function with example usage: test_fit_weighted(X, w, means, covs) without return types