code: def test_categorical_chow_liu_large_pseudocount():
      numpy.random.seed(0)
      X = numpy.random.randint(3, size=(50, 10))
  
      structure = _categorical_chow_liu(X, pseudocount=10)
      assert_tuple_equal(
          structure,
          ((), (2,), (4,), (2,), (0,), (3,), (3,), (2,), (2,), (6,)),
      )
  
      structure = _categorical_chow_liu(X, root=1, pseudocount=10)
      assert_tuple_equal(
          structure,
          ((4,), (), (1,), (2,), (2,), (3,), (3,), (2,), (2,), (6,)),
      )
  
      structure = _categorical_chow_liu(X, root=2, pseudocount=10)
      assert_tuple_equal(
          structure,
          ((4,), (2,), (), (2,), (2,), (3,), (3,), (2,), (2,), (6,)),
      )
  
imports: import numpy
  import torch
  import pytest
  from pomegranate.bayesian_network import BayesianNetwork
  from pomegranate.bayesian_network import _from_structure
  from pomegranate.bayesian_network import _learn_structure
  from pomegranate.bayesian_network import _categorical_exact
  from pomegranate.bayesian_network import _categorical_chow_liu
  from pomegranate.distributions import Categorical
  from pomegranate.distributions import ConditionalCategorical
  from nose.tools import assert_raises
  from numpy.testing import assert_array_almost_equal
label_desc: Use image node_id 8 for calling a global function with example usage: test_categorical_chow_liu_large_pseudocount() without return types