code: def test(
      start_date,
      end_date,
      ticker_list,
      data_source,
      time_interval,
      technical_indicator_list,
      drl_lib,
      env,
      model_name,
      if_vix=True,
      **kwargs,
  ):
      # import data processor
      from finrl.meta.data_processor import DataProcessor
  
      # fetch 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": False,
      }
      env_instance = env(config=env_config)
  
      # load elegantrl needs state dim, action dim and net dim
      net_dimension = kwargs.get("net_dimension", 2**7)
      cwd = kwargs.get("cwd", "./" + str(model_name))
      print("price_array: ", len(price_array))
  
      if drl_lib == "elegantrl":
          from finrl.agents.elegantrl.models import (
              DRLAgent as DRLAgent_erl,
          )
  
          episode_total_assets = DRLAgent_erl.DRL_prediction(
              model_name=model_name,
              cwd=cwd,
              net_dimension=net_dimension,
              environment=env_instance,
          )
          return episode_total_assets
      elif drl_lib == "rllib":
          from finrl.agents.rllib.models import (
              DRLAgent as DRLAgent_rllib,
          )
  
          episode_total_assets = DRLAgent_rllib.DRL_prediction(
              model_name=model_name,
              env=env,
              price_array=price_array,
              tech_array=tech_array,
              turbulence_array=turbulence_array,
              agent_path=cwd,
          )
          return episode_total_assets
      elif drl_lib == "stable_baselines3":
          from finrl.agents.stablebaselines3.models import (
              DRLAgent as DRLAgent_sb3,
          )
  
          episode_total_assets = (
              DRLAgent_sb3.DRL_prediction_load_from_file(
                  model_name=model_name,
                  environment=env_instance,
                  cwd=cwd,
              )
          )
          return episode_total_assets
      else:
          raise ValueError(
              "DRL library input is NOT supported. Please check."
          )
  
imports: from __future__ import annotations
  from finrl.config import INDICATORS
  from finrl.config import RLlib_PARAMS
  from finrl.config import TEST_END_DATE
  from finrl.config import TEST_START_DATE
  from finrl.config_tickers import DOW_30_TICKER
  from finrl.meta.env_stock_trading.env_stocktrading import StockTradingEnv
label_desc: Use image node_id 1 for calling a global function with example usage: test(start_date, end_date, ticker_list, data_source, time_interval, technical_indicator_list, drl_lib, env, model_name, if_vix) and returns: episode_total_assets, episode_total_assets, episode_total_assets