code: def train(
      start_date,
      end_date,
      ticker_list,
      data_source,
      time_interval,
      technical_indicator_list,
      drl_lib,
      env,
      model_name,
      if_vix=True,
      **kwargs,
  ):
      # download data
      dp = DataProcessor(data_source, **kwargs)
      data = dp.download_data(
          ticker_list, start_date, end_date, time_interval
      )
      data = dp.clean_data(data)
      data = dp.add_technical_indicator(data, technical_indicator_list)
      if if_vix:
          data = dp.add_vix(data)
      price_array, tech_array, turbulence_array = dp.df_to_array(
          data, if_vix
      )
      env_config = {
          "price_array": price_array,
          "tech_array": tech_array,
          "turbulence_array": turbulence_array,
          "if_train": True,
      }
      env_instance = env(config=env_config)
  
      # read parameters
      cwd = kwargs.get("cwd", "./" + str(model_name))
  
      if drl_lib == "elegantrl":
          from finrl.agents.elegantrl.models import (
              DRLAgent as DRLAgent_erl,
          )
  
          break_step = kwargs.get("break_step", 1e6)
          erl_params = kwargs.get("erl_params")
          agent = DRLAgent_erl(
              env=env,
              price_array=price_array,
              tech_array=tech_array,
              turbulence_array=turbulence_array,
          )
          model = agent.get_model(model_name, model_kwargs=erl_params)
          trained_model = agent.train_model(
              model=model, cwd=cwd, total_timesteps=break_step
          )
      elif drl_lib == "rllib":
          total_episodes = kwargs.get("total_episodes", 100)
          rllib_params = kwargs.get("rllib_params")
          from finrl.agents.rllib.models import (
              DRLAgent as DRLAgent_rllib,
          )
  
          agent_rllib = DRLAgent_rllib(
              env=env,
              price_array=price_array,
              tech_array=tech_array,
              turbulence_array=turbulence_array,
          )
          model, model_config = agent_rllib.get_model(model_name)
          model_config["lr"] = rllib_params["lr"]
          model_config["train_batch_size"] = rllib_params[
              "train_batch_size"
          ]
          model_config["gamma"] = rllib_params["gamma"]
          # ray.shutdown()
          trained_model = agent_rllib.train_model(
              model=model,
              model_name=model_name,
              model_config=model_config,
              total_episodes=total_episodes,
          )
          trained_model.save(cwd)
      elif drl_lib == "stable_baselines3":
          total_timesteps = kwargs.get("total_timesteps", 1e6)
          agent_params = kwargs.get("agent_params")
          from finrl.agents.stablebaselines3.models import (
              DRLAgent as DRLAgent_sb3,
          )
  
          agent = DRLAgent_sb3(env=env_instance)
          model = agent.get_model(model_name, model_kwargs=agent_params)
          trained_model = agent.train_model(
              model=model,
              tb_log_name=model_name,
              total_timesteps=total_timesteps,
          )
          print("Training is finished!")
          trained_model.save(cwd)
          print("Trained model is saved in " + str(cwd))
      else:
          raise ValueError(
              "DRL library input is NOT supported. Please check."
          )
  
file_path: FinRL/finrl/train.py
imports: from __future__ import annotations
  from finrl.config import ERL_PARAMS
  from finrl.config import INDICATORS
  from finrl.config import RLlib_PARAMS
  from finrl.config import SAC_PARAMS
  from finrl.config import TRAIN_END_DATE
  from finrl.config import TRAIN_START_DATE
  from finrl.config_tickers import DOW_30_TICKER
  from finrl.meta.data_processor import DataProcessor
  from finrl.meta.env_stock_trading.env_stocktrading_np import StockTradingEnv
label_desc: Use image node_id 1 for calling a global function with example usage: train(start_date, end_date, ticker_list, data_source, time_interval, technical_indicator_list, drl_lib, env, model_name, if_vix) without return types