code: def test_fit_chain(X):
      d = Categorical().fit(X[:4])
      _test_fit_params(
          d,
          [
              [0.0, 0.5, 0.25, 0.25],
              [0.25, 0.25, 0.5, 0.0],
              [0.25, 0.25, 0.5, 0.0],
          ],
      )
  
      d.fit(X[4:])
      _test_fit_params(
          d,
          [
              [0.333333, 0.333333, 0.333333, 0.0],
              [0.0, 1.0, 0.0, 0.0],
              [0.666667, 0.0, 0.333333, 0.0],
          ],
      )
  
      d = Categorical().fit(X)
      _test_fit_params(
          d,
          [
              [0.142857, 0.428571, 0.285714, 0.142857],
              [0.142857, 0.571429, 0.285714, 0.0],
              [0.428571, 0.142857, 0.428571, 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 36 for calling a global function with example usage: test_fit_chain(X) without return types