code: def predict(
      self, df: pd.DataFrame, args: Optional[Dict] = None
  ) -> pd.DataFrame:
      with self._anyscale_base_api():
          # load base and fine-tuned models, then hand over
          self._set_models(args.get("using", {}))
          _args = self.model_storage.json_get("args")
          base_models = self.chat_completion_models
          self.chat_completion_models = (
              _args.get("chat_completion_models", base_models)
              if _args
              else base_models
          )
          return super().predict(df, args)
  
imports: import os
  import openai
  import json
  import contextlib
  from typing import Optional, Dict
  import pandas
  from mindsdb.integrations.handlers.openai_handler.openai_handler import OpenAIHandler
  from mindsdb.integrations.handlers.openai_handler.constants import OPENAI_API_BASE
  from mindsdb.integrations.utilities.handler_utils import get_api_key
  from mindsdb.utilities import log
label_desc: Use image node_id 4 for calling the AnyscaleEndpointsHandler obj's underlying member method code with example usage: obj.predict(df, args) and returns: super