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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def predict_probability(self, data):
 """ Predicts probabilities of all states of the missing variables. Parameters data : pandas DataFrame object A DataFrame object with column names same as the variables in the model. Examples -------- B_0 B_1 80 0.439178 0.560822 81 0.581970 0.418030 82 0.488275 0.511725 83 0.581970 0.418030 84 0.510794 0.489206 85 0.439178 0.560822 86 0.439178 0.560822 87 0.417124 0.582876 88 0.407978 0.592022 89 0.429905 0.570095 90 0.581970 0.418030 91 0.407978 0.592022 92 0.429905 0.570095 93 0.429905 0.570095 94 0.439178 0.560822 95 0.407978 0.592022 96 0.559904 0.440096 97 0.417124 0.582876 98 0.488275 0.511725 99 0.407978 0.592022 """

        from pgmpy.inference import VariableElimination

        if set(data.columns) == set(self.nodes()):
            raise ValueError("No variable missing in data. Nothing to predict")

        elif set(data.columns) - set(self.nodes()):
            raise ValueError("Data has variables which are not in the model")

        missing_variables = set(self.nodes()) - set(data.columns)
        pred_values = defaultdict(list)

        model_inference = VariableElimination(self)
        for index, data_point in data.iterrows():
            full_distribution = model_inference.query(variables=missing_variables, evidence=data_point.to_dict())
            states_dict = {}
            for var in missing_variables:
                states_dict[var] = full_distribution.marginalize(missing_variables - {var}, inplace=False)
            for k, v in states_dict.items():
                for l in range(len(v.values)):
                    state = self.get_cpds(k).state_names[k][l]
                    pred_values[k + '_' + str(state)].append(v.values[l])
        return pd.DataFrame(pred_values, index=data.index)