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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 estimate(self, start=None, tabu_length=0, max_indegree=None):
 """ Performs local hill climb search to estimates the `DAG` structure that has optimal score, according to the scoring method supplied in the constructor. Starts at model `start` and proceeds by step-by-step network modifications until a local maximum is reached. Only estimates network structure, no parametrization. Parameters start: DAG instance The starting point for the local search. By default a completely disconnected network is used. tabu_length: int If provided, the last `tabu_length` graph modifications cannot be reversed during the search procedure. This serves to enforce a wider exploration of the search space. Default value: 100. max_indegree: int or None If provided and unequal None, the procedure only searches among models where all nodes have at most `max_indegree` parents. Defaults to None. Returns ------- model: `DAG` instance A `DAG` at a (local) score maximum. Examples -------- ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J'] [('B', 'J'), ('A', 'J')] [('J', 'A'), ('B', 'J')] """

        epsilon = 1e-8
        nodes = self.state_names.keys()
        if start is None:
            start = DAG()
            start.add_nodes_from(nodes)
        elif not isinstance(start, DAG) or not set(start.nodes()) == set(nodes):
            raise ValueError("'start' should be a DAG with the same variables as the data set, or 'None'.")

        tabu_list = []
        current_model = start

        while True:
            best_score_delta = 0
            best_operation = None

            for operation, score_delta in self._legal_operations(current_model, tabu_list, max_indegree):
                if score_delta > best_score_delta:
                    best_operation = operation
                    best_score_delta = score_delta

            if best_operation is None or best_score_delta < epsilon:
                break
            elif best_operation[0] == '+':
                current_model.add_edge(*best_operation[1])
                tabu_list = ([('-', best_operation[1])] + tabu_list)[:tabu_length]
            elif best_operation[0] == '-':
                current_model.remove_edge(*best_operation[1])
                tabu_list = ([('+', best_operation[1])] + tabu_list)[:tabu_length]
            elif best_operation[0] == 'flip':
                X, Y = best_operation[1]
                current_model.remove_edge(X, Y)
                current_model.add_edge(Y, X)
                tabu_list = ([best_operation] + tabu_list)[:tabu_length]

        return current_model