code: def trade(self):
      state = self.get_state()
  
      if self.drl_lib == "elegantrl":
          with torch.no_grad():
              s_tensor = torch.as_tensor((state,), device=self.device)
              a_tensor = self.act(s_tensor)
              action = a_tensor.detach().cpu().numpy()[0]
  
          action = (action * self.max_stock).astype(int)
  
      elif self.drl_lib == "rllib":
          action = self.agent.compute_single_action(state)
  
      elif self.drl_lib == "stable_baselines3":
          action = self.model.predict(state)[0]
  
      else:
          raise ValueError(
              "The DRL library input is NOT supported yet. Please check your input."
          )
  
      self.stocks_cd += 1
      if self.turbulence_bool == 0:
          min_action = 10  # stock_cd
          for index in np.where(action < -min_action)[0]:  # sell_index:
              sell_num_shares = min(self.stocks[index], -action[index])
              qty = abs(int(sell_num_shares))
              respSO = []
              tSubmitOrder = threading.Thread(
                  target=self.submitOrder(
                      qty, self.stockUniverse[index], "sell", respSO
                  )
              )
              tSubmitOrder.start()
              tSubmitOrder.join()
              self.cash = float(self.alpaca.get_account().cash)
              self.stocks_cd[index] = 0
  
          for index in np.where(action > min_action)[0]:  # buy_index:
              if self.cash < 0:
                  tmp_cash = 0
              else:
                  tmp_cash = self.cash
              buy_num_shares = min(
                  tmp_cash // self.price[index], abs(int(action[index]))
              )
              qty = abs(int(buy_num_shares))
              respSO = []
              tSubmitOrder = threading.Thread(
                  target=self.submitOrder(
                      qty, self.stockUniverse[index], "buy", respSO
                  )
              )
              tSubmitOrder.start()
              tSubmitOrder.join()
              self.cash = float(self.alpaca.get_account().cash)
              self.stocks_cd[index] = 0
  
      else:  # sell all when turbulence
          positions = self.alpaca.list_positions()
          for position in positions:
              if position.side == "long":
                  orderSide = "sell"
              else:
                  orderSide = "buy"
              qty = abs(int(float(position.qty)))
              respSO = []
              tSubmitOrder = threading.Thread(
                  target=self.submitOrder(
                      qty, position.symbol, orderSide, respSO
                  )
              )
              tSubmitOrder.start()
              tSubmitOrder.join()
  
          self.stocks_cd[:] = 0
  
imports: from __future__ import annotations
  import datetime
  import threading
  import time
  import alpaca_trade_api
  import gymnasium
  import numpy
  import pandas
  import torch
  from finrl.meta.data_processors.processor_alpaca import AlpacaProcessor
label_desc: Use image node_id 5 for calling the AlpacaPaperTrading obj's underlying member method code with example usage: obj.trade() without return types