code: def main():
      # replace print output to stderr
      sys.stdout = sys.stderr
  
      params = get_input()
  
      method = BYOM_METHOD(params["method"])
      code = params["code"]
  
      module = import_string(code)
  
      model_class = find_model_class(module)
  
      if method == BYOM_METHOD.CHECK:
          model = model_class()
  
          if not hasattr(model, "train"):
              raise RuntimeError(
                  'Model class has to have "train" method'
              )
  
          if not hasattr(model, "predict"):
              raise RuntimeError(
                  'Model class has to have "predict" method'
              )
  
          return_output(True)
  
      if method == BYOM_METHOD.TRAIN:
          df = pd_decode(params["df"])
          to_predict = params["to_predict"]
          args = params["args"]
          model = model_class()
  
          call_args = [df, to_predict]
          if args:
              call_args.append(args)
          model.train(*call_args)
  
          # return model
          data = model.__dict__
  
          model_state = encode(data)
          return_output(model_state)
  
      elif method == BYOM_METHOD.PREDICT:
          model_state = params["model_state"]
          df = pd_decode(params["df"])
          args = params["args"]
  
          model = model_class()
          model.__dict__ = decode(model_state)
  
          call_args = [df]
          if args:
              call_args.append(args)
          res = model.predict(*call_args)
          return_output(pd_encode(res))
  
      elif method == BYOM_METHOD.FINETUNE:
          model_state = params["model_state"]
          df = pd_decode(params["df"])
          args = params["args"]
  
          model = model_class()
          model.__dict__ = decode(model_state)
  
          call_args = [df]
          if args:
              call_args.append(args)
  
          model.finetune(*call_args)
  
          # return model
          data = model.__dict__
          model_state = encode(data)
          return_output(model_state)
  
      elif method == BYOM_METHOD.DESCRIBE:
          model_state = params["model_state"]
          model = model_class()
          model.__dict__ = decode(model_state)
          try:
              df = model.describe(params.get("attribute"))
          except Exception:
              return_output(pd_encode(pd.DataFrame()))
          return_output(pd_encode(df))
  
      raise NotImplementedError(method)
  
file_path: mindsdb/mindsdb/integrations/handlers/byom_handler/proc_wrapper.py
imports: import io
  import sys
  import pickle
  import inspect
  from enum import Enum
  import pandas
label_desc: Use image node_id 9 for calling a global function with example usage: main() without return types