code: def test_fit(X, probs):
      for param in probs, None:
          d = Bernoulli(param)
          d.fit(X[:4])
          _test_efd_from_summaries(
              d, "probs", "_log_probs", [0.5, 0.25, 0.75]
          )
  
          d.fit(X[4:])
          _test_efd_from_summaries(
              d, "probs", "_log_probs", [1.0, 1.0, 0.333333]
          )
  
          d = Bernoulli(param)
          d.fit(X)
          _test_efd_from_summaries(
              d, "probs", "_log_probs", [0.714286, 0.571429, 0.571429]
          )
  
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 32 for calling a global function with example usage: test_fit(X, probs) without return types