How to use the pomegranate.BayesianNetwork function in pomegranate

[['1','4',0.5],
            ['1','5',0.5],
            ['1','6',0],
            ['2','4',0],
            ['2','5',0.5],
            ['2','6',0.5],
            ['3','4',0.5],
            ['3','5',0],
            ['3','6',0.5],
            ],[A])

        s1 = Node(A, name="A")
        s2 = Node(B, name="B")
        s3 = Node(C, name="C")

        model = BayesianNetwork("tree")
        model.add_states(s1, s2, s3)
        model.add_edge(s1, s2)
        model.add_edge(s1, s3)
        model.bake()
        self.model = model

        meta = []
        for i in range(self.model.node_count()-1):
            meta.append({
                "name": chr(ord('A') + i),
                "type": "categorical",
                "size": 3,
                "i2s": ['1', '2', '3']
        })
        meta.append({
                "name": "C",

Wet = ConditionalProbabilityTable(
            [['F','F','T',0.01],
            ['F','F','F',0.99],
            ['F','T','T',0.8],
            ['F','T','F',0.2],
            ['T','F','T',0.9],
            ['T','F','F',0.1],
            ['T','T','T',0.99],
            ['T','T','F',0.01],
            ],[Sprinkler,Rain])

        s1 = Node(Rain, name="Rain")
        s2 = Node(Sprinkler, name="Sprinkler")
        s3 = Node(Wet, name="Wet")

        model = BayesianNetwork("Simple fully connected")
        model.add_states(s1, s2, s3)
        model.add_edge(s1, s2)
        model.add_edge(s1, s3)
        model.add_edge(s2, s3)
        model.bake()
        self.model = model

        meta = []
        for i in range(self.model.node_count()):
            meta.append({
                "name": None,
                "type": "categorical",
                "size": 2,
                "i2s": ['T', 'F']
        })
        meta[0]['name'] = 'Rain'

[['1','1',0.5],
            ['1','2',0.5],
            ['1','3',0],
            ['2','1',0],
            ['2','2',0.5],
            ['2','3',0.5],
            ['3','1',0.5],
            ['3','2',0],
            ['3','3',0.5],
            ],[B])

        s1 = Node(A, name="A")
        s2 = Node(B, name="B")
        s3 = Node(C, name="C")

        model = BayesianNetwork("ChainSampler")
        model.add_states(s1, s2, s3)
        model.add_edge(s1, s2)
        model.add_edge(s2, s3)
        model.bake()
        self.model = model

        meta = []
        for i in range(self.model.node_count()):
            meta.append({
                "name": chr(ord('A') + i),
                "type": "categorical",
                "size": 3,
                "i2s": ['1', '2', '3']
        })
        self.meta = meta

['F','T',0.2],
            ['F','F',0.8],
            ],[Cancer])
        Dyspnoea = ConditionalProbabilityTable(
            [['T','T',0.65],
            ['T','F',0.35],
            ['F','T',0.3],
            ['F','F',0.7],
            ],[Cancer])
        s1 = Node(Pollution, name="Pollution")
        s2 = Node(Smoker, name="Smoker")
        s3 = Node(Cancer, name="Cancer")
        s4 = Node(XRay, name="XRay")
        s5 = Node(Dyspnoea, name="Dyspnoea")

        model = BayesianNetwork("Lung Cancer")
        model.add_states(s1, s2, s3, s4, s5)
        model.add_edge(s1, s3)
        model.add_edge(s2, s3)
        model.add_edge(s3, s4)
        model.add_edge(s3, s5)
        model.bake()
        self.model = model

        meta = []
        name_mapper = ["Pollution", "Smoker", "Cancer", "XRay", "Dyspnoea"]
        for i in range(self.model.node_count()):
            meta.append({
                "name": name_mapper[i],
                "type": "categorical",
                "size": 2,
                "i2s": ['T', 'F']