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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 next_hyperparameter_lowest_mu(fun_prediction,
                                  fun_prediction_args,
                                  x_bounds, x_types,
                                  minimize_starting_points,
                                  minimize_constraints_fun=None):
    '''
    "Lowest Mu" acquisition function
    '''
    best_x = None
    best_acquisition_value = None
    x_bounds_minmax = [[i[0], i[-1]] for i in x_bounds]
    x_bounds_minmax = numpy.array(x_bounds_minmax)

    for starting_point in numpy.array(minimize_starting_points):
        res = minimize(fun=_lowest_mu,
                       x0=starting_point.reshape(1, -1),
                       bounds=x_bounds_minmax,
                       method="L-BFGS-B",
                       args=(fun_prediction, fun_prediction_args, \
                             x_bounds, x_types, minimize_constraints_fun))

        if (best_acquisition_value is None) or (res.fun < best_acquisition_value):
            res.x = numpy.ndarray.tolist(res.x)
            res.x = lib_data.match_val_type(res.x, x_bounds, x_types)
            if (minimize_constraints_fun is None) or (minimize_constraints_fun(res.x) is True):
                best_acquisition_value = res.fun
                best_x = res.x

    outputs = None
    if best_x is not None:
        mu, sigma = fun_prediction(best_x, *fun_prediction_args)
        outputs = {'hyperparameter': best_x, 'expected_mu': mu,
                   'expected_sigma': sigma, 'acquisition_func': "lm"}
    return outputs