class_docstr: Provides implementations for DRL algorithms
  
  Attributes
  ----------
      env: gym environment class
          user-defined class
  
  Methods
  -------
      get_model()
          setup DRL algorithms
      train_model()
          train DRL algorithms in a train dataset
          and output the trained model
      DRL_prediction()
          make a prediction in a test dataset and get results
class_docstr_tok: ["Provides","implementations","for","DRL","algorithms","Attributes","--","--","--","--","--","env",":","gym","environment","class","user-defined","class","Methods","--","--","--","-","get_model","(",")","setup","DRL","algorithms","train_model","(",")","train","DRL","algorithms","in","a","train","dataset","and","output","the","trained","model","DRL_prediction","(",")","make","a","prediction","in","a","test","dataset","and","get","results"]
code: def train_model(
      model, tb_log_name, total_timesteps=5000
  ):  # this function is static method, so it can be called without creating an instance of the class
      model = model.learn(
          total_timesteps=total_timesteps,
          tb_log_name=tb_log_name,
          callback=TensorboardCallback(),
      )
      return model
  
imports: from __future__ import annotations
  import time
  import numpy
  import pandas
  from stable_baselines3 import A2C
  from stable_baselines3 import DDPG
  from stable_baselines3 import PPO
  from stable_baselines3 import SAC
  from stable_baselines3 import TD3
  from stable_baselines3.common.callbacks import BaseCallback
  from stable_baselines3.common.noise import NormalActionNoise
  from stable_baselines3.common.noise import OrnsteinUhlenbeckActionNoise
  from stable_baselines3.common.vec_env import DummyVecEnv
  from finrl import config
  from finrl.meta.env_stock_trading.env_stocktrading import StockTradingEnv
  from finrl.meta.preprocessor.preprocessors import data_split
label_desc: Use image node_id 3 for calling the DRLAgent obj's underlying member method code with example usage: obj.train_model(model, tb_log_name, total_timesteps) and returns: model