code: def model():
      d0 = Categorical([[0.3, 0.1, 0.6]])
      d1 = ConditionalCategorical(
          [[[0.4, 0.2, 0.4], [0.1, 0.2, 0.7], [0.2, 0.5, 0.3]]]
      )
  
      d2 = ConditionalCategorical(
          [
              [
                  [[0.3, 0.1, 0.6], [0.1, 0.4, 0.5], [0.6, 0.2, 0.2]],
                  [[0.2, 0.3, 0.5], [0.7, 0.1, 0.2], [0.8, 0.1, 0.1]],
                  [[0.6, 0.3, 0.1], [0.4, 0.1, 0.5], [0.6, 0.2, 0.2]],
              ]
          ]
      )
  
      return MarkovChain([d0, d1, d2])
  
imports: import numpy
  import torch
  import pytest
  from pomegranate.markov_chain import MarkovChain
  from pomegranate.distributions import Categorical
  from pomegranate.distributions import ConditionalCategorical
  from .distributions._utils import _test_initialization_raises_one_parameter
  from .distributions._utils import _test_initialization
  from .distributions._utils import _test_predictions
  from .distributions._utils import _test_efd_from_summaries
  from .distributions._utils import _test_raises
  from nose.tools import assert_raises
  from numpy.testing import assert_array_almost_equal
label_desc: Use image node_id 3 for calling a global function with example usage: model() and returns: MarkovChain