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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 maxcut_qaoa(graph, steps=1, rand_seed=None, connection=None, samples=None, initial_beta=None, initial_gamma=None, minimizer_kwargs=None, vqe_option=None):
 """ Max cut set up method :param graph: Graph definition. Either networkx or list of tuples :param steps: (Optional. Default=1) Trotterization order for the QAOA algorithm. :param rand_seed: (Optional. Default=None) random seed when beta and gamma angles are not provided. :param connection: (Optional) connection to the QVM. Default is None. :param samples: (Optional. Default=None) VQE option. Number of samples (circuit preparation and measurement) to use in operator averaging. :param initial_beta: (Optional. Default=None) Initial guess for beta parameters. :param initial_gamma: (Optional. Default=None) Initial guess for gamma parameters. :param minimizer_kwargs: (Optional. Default=None). Minimizer optional arguments. If None set to ``{'method': 'Nelder-Mead', 'options': {'ftol': 1.0e-2, 'xtol': 1.0e-2, 'disp': False}`` :param vqe_option: (Optional. Default=None). VQE optional arguments. If None set to ``vqe_option = {'disp': print_fun, 'return_all': True, 'samples': samples}`` """

    if not isinstance(graph, nx.Graph) and isinstance(graph, list):
        maxcut_graph = nx.Graph()
        for edge in graph:
            maxcut_graph.add_edge(*edge)
        graph = maxcut_graph.copy()

    cost_operators = []
    driver_operators = []
    for i, j in graph.edges():
        cost_operators.append(PauliTerm("Z", i, 0.5)*PauliTerm("Z", j) + PauliTerm("I", 0, -0.5))
    for i in graph.nodes():
        driver_operators.append(PauliSum([PauliTerm("X", i, -1.0)]))

    if connection is None:
        connection = get_qc(f"{len(graph.nodes)}q-qvm")

    if minimizer_kwargs is None:
        minimizer_kwargs = {'method': 'Nelder-Mead',
                            'options': {'ftol': 1.0e-2, 'xtol': 1.0e-2,
                                        'disp': False}}
    if vqe_option is None:
        vqe_option = {'disp': print, 'return_all': True,
                      'samples': samples}

    qaoa_inst = QAOA(connection, list(graph.nodes()), steps=steps, cost_ham=cost_operators,
                     ref_ham=driver_operators, store_basis=True,
                     rand_seed=rand_seed,
                     init_betas=initial_beta,
                     init_gammas=initial_gamma,
                     minimizer=minimize,
                     minimizer_kwargs=minimizer_kwargs,
                     vqe_options=vqe_option)

    return qaoa_inst