code: def test_fit(X, probs):
      d = Categorical(probs)
      d.fit(X)
      assert_raises(
          AssertionError, assert_array_almost_equal, probs, d.probs
      )
  
      for param in probs, None:
          d = Categorical(param)
          d.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(param)
          d.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 34 for calling a global function with example usage: test_fit(X, probs) without return types