code: def test_fit_weighted(X, w, probs):
      for param in probs, None:
          d = Bernoulli(param)
          d.fit(X[:4], sample_weight=w[:4])
          _test_efd_from_summaries(
              d, "probs", "_log_probs", [0.333333, 0.0, 0.666667]
          )
  
          d.fit(X[4:], sample_weight=w[4:])
          _test_efd_from_summaries(
              d, "probs", "_log_probs", [1.0, 1.0, 0.25]
          )
  
          d = Bernoulli(param)
          d.fit(X, sample_weight=w)
          _test_efd_from_summaries(
              d, "probs", "_log_probs", [0.818182, 0.727273, 0.363636]
          )
  
      d = Bernoulli()
      d.fit(X, sample_weight=w)
      _test_efd_from_summaries(
          d, "probs", "_log_probs", [0.818182, 0.727273, 0.363636]
      )
  
imports: import os
  import numpy
  import torch
  import pytest
  from pomegranate.distributions import Bernoulli
  from ._utils import _test_initialization_raises_one_parameter
  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, probs) without return types