code: def model():
      f1 = Categorical([[0.23, 0.77]])
      f2 = JointCategorical(
          [
              [[0.10, 0.05, 0.05], [0.15, 0.05, 0.04]],
              [[0.20, 0.10, 0.05], [0.05, 0.10, 0.06]],
          ]
      )
      f3 = Categorical([[0.61, 0.39]])
      f4 = JointCategorical([[0.17, 0.15], [0.4, 0.28]])
  
      m1 = Categorical([[0.5, 0.5]])
      m2 = Categorical([[1.0 / 3, 1.0 / 3, 1.0 / 3]])
      m3 = Categorical([[0.5, 0.5]])
      m4 = Categorical([[0.5, 0.5]])
  
      model = FactorGraph()
      model.add_factor(f1)
      model.add_factor(f2)
      model.add_factor(f3)
      model.add_factor(f4)
  
      model.add_marginal(m1)
      model.add_marginal(m2)
      model.add_marginal(m3)
      model.add_marginal(m4)
  
      model.add_edge(m1, f1)
      model.add_edge(m1, f2)
      model.add_edge(m3, f2)
      model.add_edge(m2, f2)
      model.add_edge(m3, f3)
      model.add_edge(m3, f4)
      model.add_edge(m4, f4)
      return model
  
imports: import numpy
  import torch
  import pytest
  from pomegranate.factor_graph import FactorGraph
  from pomegranate.distributions import Exponential
  from pomegranate.distributions import Categorical
  from pomegranate.distributions import JointCategorical
  from nose.tools import assert_raises
  from numpy.testing import assert_array_almost_equal
label_desc: Use image node_id 4 for calling a global function with example usage: model() and returns: model