How to use the parl.utils.logger.info function in parl

test_flag = 0
    total_steps = 0
    while total_steps < args.train_total_steps:
        trajectories = collect_trajectories(
            env, agent, scaler, episodes=args.episodes_per_batch)
        total_steps += sum([t['obs'].shape[0] for t in trajectories])
        total_train_rewards = sum([np.sum(t['rewards']) for t in trajectories])

        train_obs, train_actions, train_advantages, train_discount_sum_rewards = build_train_data(
            trajectories, agent)

        policy_loss, kl = agent.policy_learn(train_obs, train_actions,
                                             train_advantages)
        value_loss = agent.value_learn(train_obs, train_discount_sum_rewards)

        logger.info(
            'Steps {}, Train reward: {}, Policy loss: {}, KL: {}, Value loss: {}'
            .format(total_steps, total_train_rewards / args.episodes_per_batch,
                    policy_loss, kl, value_loss))
        if total_steps // args.test_every_steps >= test_flag:
            while total_steps // args.test_every_steps >= test_flag:
                test_flag += 1
            eval_reward = run_evaluate_episode(env, agent, scaler)
            logger.info('Steps {}, Evaluate reward: {}'.format(
                total_steps, eval_reward))

def _new_ready_actor(self):
        """ 

        The actor is ready to start new episode,
        but blocking until training thread call actor_ready_event.set()
        """
        actor_ready_event = threading.Event()
        self.ready_actor_queue.put(actor_ready_event)
        logger.info(
            "[new_avaliabe_actor] approximate size of ready actors:{}".format(
                self.ready_actor_queue.qsize()))
        actor_ready_event.wait()

for file, code in pyfiles['python_files'].items():
                file = os.path.join(envdir, file)
                with open(file, 'wb') as code_file:
                    code_file.write(code)

            # save other files to current directory
            for file, content in pyfiles['other_files'].items():
                # create directory (i.e. ./rom_files/)
                if '/' in file:
                    try:
                        os.makedirs(os.path.join(*file.rsplit('/')[:-1]))
                    except OSError as e:
                        pass
                with open(file, 'wb') as f:
                    f.write(content)
            logger.info('[job] reply')
            reply_socket.send_multipart([remote_constants.NORMAL_TAG])
            return envdir
        else:
            logger.error("NotImplementedError:{}, received tag:{}".format(
                job_address, ))
            raise NotImplementedError

Returns:
        gpu_count: int
    """

    gpu_count = 0

    env_cuda_devices = os.environ.get('CUDA_VISIBLE_DEVICES', None)
    if env_cuda_devices is not None:
        assert isinstance(env_cuda_devices, str)
        try:
            if not env_cuda_devices:
                return 0
            gpu_count = len(
                [x for x in env_cuda_devices.split(',') if int(x) >= 0])
            logger.info(
                'CUDA_VISIBLE_DEVICES found gpu count: {}'.format(gpu_count))
        except:
            logger.info('Cannot find available GPU devices, using CPU now.')
            gpu_count = 0
    else:
        try:
            gpu_count = str(subprocess.check_output(["nvidia-smi",
                                                     "-L"])).count('UUID')
            logger.info('nvidia-smi -L found gpu count: {}'.format(gpu_count))
        except:
            logger.info('Cannot find available GPU devices, using CPU now.')
            gpu_count = 0
    return gpu_count

elif stage == 3:
            assert change_num >= 3
            interval = 3.0 / self.discrete_bin
            discrete_id = np.random.randint(self.discrete_bin)
            min_vel = -0.25 + discrete_id * interval
            max_vel = -0.25 + (discrete_id + 1) * interval
            while True:
                target_vels = [1.25]
                for i in range(change_num):
                    target_vels.append(target_vels[-1] +
                                       random.uniform(-0.5, 0.5))
                if target_vels[3] >= min_vel and target_vels[3] <= max_vel:
                    break
        else:
            raise NotImplemented
        logger.info('[CustomR2Env] stage: {}, target_vels: {}'.format(
            stage, target_vels))
        return target_vels

[remote_constants.NORMAL_TAG, status])

        # `xparl status` command line API
        elif tag == remote_constants.STATUS_TAG:
            status_info = self.cluster_monitor.get_status_info()
            self.client_socket.send_multipart(
                [remote_constants.NORMAL_TAG,
                 to_byte(status_info)])

        elif tag == remote_constants.WORKER_INITIALIZED_TAG:
            initialized_worker = cloudpickle.loads(message[1])
            worker_address = initialized_worker.worker_address
            self.job_center.add_worker(initialized_worker)
            hostname = self.job_center.get_hostname(worker_address)
            self.cluster_monitor.add_worker_status(worker_address, hostname)
            logger.info("A new worker {} is added, ".format(worker_address) +
                        "the cluster has {} CPUs.\n".format(self.cpu_num))

            # a thread for sending heartbeat signals to `worker.address`
            thread = threading.Thread(
                target=self._create_worker_monitor,
                args=(initialized_worker.worker_address, ))
            thread.start()

            self.client_socket.send_multipart([remote_constants.NORMAL_TAG])

        # a client connects to the master
        elif tag == remote_constants.CLIENT_CONNECT_TAG:
            client_heartbeat_address = to_str(message[1])
            client_hostname = to_str(message[2])
            self.client_hostname[client_heartbeat_address] = client_hostname
            logger.info(

# start training
        if rpm.size() > MEMORY_WARMUP_SIZE:
            if steps % UPDATE_FREQ == 0:
                batch_all_state, batch_action, batch_reward, batch_isOver = rpm.sample_batch(
                    args.batch_size)
                batch_state = batch_all_state[:, :CONTEXT_LEN, :, :]
                batch_next_state = batch_all_state[:, 1:, :, :]
                cost = agent.learn(batch_state, batch_action, batch_reward,
                                   batch_next_state, batch_isOver)
                all_cost.append(float(cost))
        total_reward += reward
        state = next_state
        if isOver:
            break
    if all_cost:
        logger.info('[Train]total_reward: {}, mean_cost: {}'.format(
            total_reward, np.mean(all_cost)))
    return total_reward, steps, np.mean(all_cost)

def disconnect():
    """Disconnect the global client from the master node."""
    global GLOBAL_CLIENT
    if GLOBAL_CLIENT is not None:
        GLOBAL_CLIENT.client_is_alive = False
        GLOBAL_CLIENT = None
    else:
        logger.info(
            "No client to be released. Please make sure that you have call `parl.connect`"
        )