How to use the lagom.utils.pickle_dump function in lagom

Rs, Hs = zip(*out)
            es.tell(solutions, [-R for R in Rs])
            logger = Logger()
            logger('generation', generation+1)
            logger('num_seconds', round(time.perf_counter() - t0, 1))
            logger('Returns', describe(Rs, axis=-1, repr_indent=1, repr_prefix='\n'))
            logger('Horizons', describe(Hs, axis=-1, repr_indent=1, repr_prefix='\n'))
            logger('fbest', es.result.fbest)
            train_logs.append(logger.logs)
            if generation == 0 or (generation+1) % config['log.freq'] == 0:
                logger.dump(keys=None, index=0, indent=0, border='-'*50)
            if (generation+1) >= int(config['train.generations']*(checkpoint_count/(config['checkpoint.num'] - 1))):
                agent.from_vec(tensorify(es.result.xbest, 'cpu'))
                agent.checkpoint(logdir, generation+1)
                checkpoint_count += 1
    pickle_dump(obj=train_logs, f=logdir/'train_logs', ext='.pkl')
    return None

Rs, Hs = zip(*out)
            es.tell(solutions, [-R for R in Rs])
            logger = Logger()
            logger('generation', generation+1)
            logger('num_seconds', round(time.perf_counter() - t0, 1))
            logger('Returns', describe(Rs, axis=-1, repr_indent=1, repr_prefix='\n'))
            logger('Horizons', describe(Hs, axis=-1, repr_indent=1, repr_prefix='\n'))
            logger('fbest', es.result.fbest)
            train_logs.append(logger.logs)
            if generation == 0 or (generation+1) % config['log.freq'] == 0:
                logger.dump(keys=None, index=0, indent=0, border='-'*50)
            if (generation+1) >= int(config['train.generations']*(checkpoint_count/(config['checkpoint.num'] - 1))):
                agent.from_vec(tensorify(es.result.xbest, 'cpu'))
                agent.checkpoint(logdir, generation+1)
                checkpoint_count += 1
    pickle_dump(obj=train_logs, f=logdir/'train_logs', ext='.pkl')
    return None

agent = Agent(config, env, device)
    runner = StepRunner(reset_on_call=False)
    engine = Engine(config, agent=agent, env=env, runner=runner)
    train_logs = []
    checkpoint_count = 0
    for i in count():
        if agent.total_timestep >= config['train.timestep']:
            break
        train_logger = engine.train(i)
        train_logs.append(train_logger.logs)
        if i == 0 or (i+1) % config['log.freq'] == 0:
            train_logger.dump(keys=None, index=0, indent=0, border='-'*50)
        if agent.total_timestep >= int(config['train.timestep']*(checkpoint_count/(config['checkpoint.num'] - 1))):
            agent.checkpoint(logdir, i + 1)
            checkpoint_count += 1
    pickle_dump(obj=train_logs, f=logdir/'train_logs', ext='.pkl')
    return None

agent = Agent(config, env, device)
    runner = StepRunner(reset_on_call=False)
    engine = Engine(config, agent=agent, env=env, runner=runner)
    train_logs = []
    checkpoint_count = 0
    for i in count():
        if agent.total_timestep >= config['train.timestep']:
            break
        train_logger = engine.train(i)
        train_logs.append(train_logger.logs)
        if i == 0 or (i+1) % config['log.freq'] == 0:
            train_logger.dump(keys=None, index=0, indent=0, border='-'*50)
        if agent.total_timestep >= int(config['train.timestep']*(checkpoint_count/(config['checkpoint.num'] - 1))):
            agent.checkpoint(logdir, i + 1)
            checkpoint_count += 1
    pickle_dump(obj=train_logs, f=logdir/'train_logs', ext='.pkl')
    return None

agent = Agent(config, env, device)
    runner = EpisodeRunner(reset_on_call=False)
    engine = Engine(config, agent=agent, env=env, runner=runner)
    train_logs = []
    checkpoint_count = 0
    for i in count():
        if agent.total_timestep >= config['train.timestep']:
            break
        train_logger = engine.train(i)
        train_logs.append(train_logger.logs)
        if i == 0 or (i+1) % config['log.freq'] == 0:
            train_logger.dump(keys=None, index=0, indent=0, border='-'*50)
        if agent.total_timestep >= int(config['train.timestep']*(checkpoint_count/(config['checkpoint.num'] - 1))):
            agent.checkpoint(logdir, i + 1)
            checkpoint_count += 1
    pickle_dump(obj=train_logs, f=logdir/'train_logs', ext='.pkl')
    return None

train_output = engine.train(i)
            
            if i == 0 or (i+1) % config['log.record_interval'] == 0 or (i+1) % config['log.print_interval'] == 0:
                train_log = engine.log_train(train_output)
                
                with torch.no_grad():  # disable grad, save memory
                    eval_output = engine.eval(n=i)
                eval_log = engine.log_eval(eval_output)
                
                if i == 0 or (i+1) % config['log.record_interval'] == 0:
                    train_logs.append(train_log)
                    eval_logs.append(eval_log)
        
        pickle_dump(obj=train_logs, f=logdir/'train_logs', ext='.pkl')
        pickle_dump(obj=eval_logs, f=logdir/'eval_logs', ext='.pkl')
        
        return None

elif 'train.timestep' in config and agent.total_T >= config['train.timestep']:  # enough timesteps
                break
            
            train_output = engine.train(i)
            
            if i == 0 or (i+1) % config['log.interval'] == 0:
                train_log = engine.log_train(train_output)
                train_logs.append(train_log)
                
                if config['eval.independent']:
                    with torch.no_grad():  # disable grad, save memory
                        eval_output = engine.eval(n=i)
                    eval_log = engine.log_eval(eval_output)
                    eval_logs.append(eval_log)

        pickle_dump(obj=train_logs, f=logdir/'train_logs', ext='.pkl')
        pickle_dump(obj=eval_logs, f=logdir/'eval_logs', ext='.pkl')
        
        return None

optimizer = optim.Adam(model.parameters(), lr=config['lr'])
    
    engine = Engine(config, 
                    model=model, 
                    optimizer=optimizer,
                    train_loader=train_loader, 
                    test_loader=test_loader)
    
    train_logs = []
    eval_logs = []
    for epoch in range(config['train.num_epoch']):
        train_logger = engine.train(epoch, logdir=logdir)
        train_logs.append(train_logger.logs)
        eval_logger = engine.eval(epoch, logdir=logdir)
        eval_logs.append(eval_logger.logs)
    pickle_dump(obj=train_logs, f=logdir/'train_logs', ext='.pkl')
    pickle_dump(obj=eval_logs, f=logdir/'eval_logs', ext='.pkl')
    return None

def checkpoint(self, logdir, num_iter):
        self.save(logdir/f'agent_{num_iter}.pth')
        if 'env.normalize_obs' in self.config and self.config['env.normalize_obs']:
            moments = (self.env.obs_moments.mean, self.env.obs_moments.var)
            pickle_dump(obj=moments, f=logdir/f'obs_moments_{num_iter}', ext='.pth')

def checkpoint(self, logdir, num_iter):
        self.save(logdir/f'agent_{num_iter}.pth')
        obs_env = get_wrapper(self.env, 'VecStandardizeObservation')
        if obs_env is not None:
            pickle_dump(obj=(obs_env.mean, obs_env.var), f=logdir/f'obs_moments_{num_iter}', ext='.pth')