code: def __init__(self, args):
      self.model_lock = threading.Lock()
      self.rpm_lock = threading.Lock()
      self.log_lock = threading.Lock()
  
      self.args = args
  
      obs_dim = OBS_DIM
      action_dim = ACT_DIM
  
      # Initialize model, algorithm, agent, replay_memory
      model = GridModel(obs_dim, action_dim)
      algorithm = SAC(
          model,
          gamma=GAMMA,
          tau=TAU,
          alpha=args.alpha,
          actor_lr=ACTOR_LR,
          critic_lr=CRITIC_LR,
      )
      self.agent = GridAgent(algorithm)
  
      self.agent.restore("./paddle_pretrain_model")
  
      self.rpm = ReplayMemory(
          max_size=MEMORY_SIZE, obs_dim=obs_dim, act_dim=action_dim
      )
  
      self.total_steps = 0
      self.total_MDP_steps = 0
      self.save_cnt = 0
  
      parl.connect(
          args.xparl_addr,
          distributed_files=[
              "lib64/*",
              "model_jm/*",
              "Agent/*",
              "Environment/*",
              "Observation/*",
              "Reward/*",
              "utilize/*",
          ],
      )
      for _ in range(args.actor_num):
          th = threading.Thread(target=self.run_sampling)
          th.setDaemon(True)
          th.start()
  
imports: import os
  import numpy
  import argparse
  import threading
  import time
  import parl
  from parl.utils import logger, summary, ReplayMemory
  from grid_model import GridModel
  from grid_agent import GridAgent
  from parl.algorithms import SAC
  from env_wrapper import get_env
label_desc: Use image node_id 1 to create a new Learner object from inherited base classes: object with example: obj = Learner(args)