How to use the identify.Identification function in identify

def write(self, message):
            self.terminal.write(message)
            self.log += message

        def flush(self):
            self.terminal.flush()

    sys.stdout = Logger()  # type: ignore
    logger = sys.stdout

    #for ipython, reset this with
    #import sys
    #sys.stdout = sys.__stdout__

    idf = Identification(config, args.model, args.model_real, args.measurements, args.regressor, args.validation)

    if idf.opt['selectBlocksFromMeasurements']:
        idf.opt['selectingBlocks'] = 1
        old_essential_option = idf.opt['useEssentialParams']
        idf.opt['useEssentialParams'] = 0

        old_feasible_option = idf.opt['constrainToConsistent']
        idf.opt['constrainToConsistent'] = 0

        # loop over input blocks and select good ones
        while 1:
            idf.estimateParameters()
            idf.data.getBlockStats(idf.model)
            idf.estimateRegressorTorques()
            oc = OutputConsole(idf)
            oc.render(summary_only=True)

def main():
    # save either optimized or random trajectory parameters to filename
    if args.filename:
        traj_file = args.filename
    else:
        traj_file = config['urdf'] + '.trajectory.npz'

    if config['optimizeTrajectory']:
        # find trajectory params by optimization
        old_sim = config['simulateTorques']
        config['simulateTorques'] = True
        model = Model(config, config['urdf'])
        if config['useStaticTrajectories']:
            old_gravity = config['identifyGravityParamsOnly']
            idf = Identification(config, config['urdf'], config['urdf_real'], measurements_files=None,
                                 regressor_file=None, validation_file=None)
            trajectoryOptimizer = PostureOptimizer(config, idf, model, simulation_func=simulateTrajectory, world=args.world)
            config['identifyGravityParamsOnly'] = old_gravity
        else:
            idf = Identification(config, config['urdf'], urdf_file_real=None, measurements_files=None,
                                 regressor_file=None, validation_file=None)
            trajectoryOptimizer = TrajectoryOptimizer(config, idf, model, simulation_func=simulateTrajectory, world=args.world)

        trajectory = trajectoryOptimizer.optimizeTrajectory()
        config['simulateTorques'] = old_sim
    else:
        # use some random params
        print("no optimized trajectory found, generating random one")
        trajectory = PulsedTrajectory(config['num_dofs'], use_deg=config['useDeg']).initWithRandomParams()
        print("a {}".format([t_a.tolist() for t_a in trajectory.a]))
        print("b {}".format([t_b.tolist() for t_b in trajectory.b]))

else:
        traj_file = config['urdf'] + '.trajectory.npz'

    if config['optimizeTrajectory']:
        # find trajectory params by optimization
        old_sim = config['simulateTorques']
        config['simulateTorques'] = True
        model = Model(config, config['urdf'])
        if config['useStaticTrajectories']:
            old_gravity = config['identifyGravityParamsOnly']
            idf = Identification(config, config['urdf'], config['urdf_real'], measurements_files=None,
                                 regressor_file=None, validation_file=None)
            trajectoryOptimizer = PostureOptimizer(config, idf, model, simulation_func=simulateTrajectory, world=args.world)
            config['identifyGravityParamsOnly'] = old_gravity
        else:
            idf = Identification(config, config['urdf'], urdf_file_real=None, measurements_files=None,
                                 regressor_file=None, validation_file=None)
            trajectoryOptimizer = TrajectoryOptimizer(config, idf, model, simulation_func=simulateTrajectory, world=args.world)

        trajectory = trajectoryOptimizer.optimizeTrajectory()
        config['simulateTorques'] = old_sim
    else:
        # use some random params
        print("no optimized trajectory found, generating random one")
        trajectory = PulsedTrajectory(config['num_dofs'], use_deg=config['useDeg']).initWithRandomParams()
        print("a {}".format([t_a.tolist() for t_a in trajectory.a]))
        print("b {}".format([t_b.tolist() for t_b in trajectory.b]))
        print("q {}".format(trajectory.q.tolist()))
        print("nf {}".format(trajectory.nf.tolist()))
        print("wf {}".format(trajectory.w_f_global))

    print("Saving found trajectory to {}".format(traj_file))