code: def _get_ensemble_data(self):
      ai_info = self.model_storage.json_get("json_ai")
      if ai_info == {}:
          raise Exception(
              "predictor doesn't contain enough data to generate 'ensamble' attribute. Please wait until predictor is complete."
          )
      ai_info_str = json.dumps(ai_info, indent=2)
  
      return pd.DataFrame([[ai_info_str]], columns=["ensemble"])
  
imports: import copy
  import json
  import sys
  from datetime import datetime
  from functools import lru_cache
  from typing import Dict, Optional
  import lightwood
  import numpy
  import pandas
  from type_infer.dtype import dtype
  import mindsdb.interfaces.storage.db
  import mindsdb.utilities.profiler
  from mindsdb.integrations.libs.base import BaseMLEngine
  from mindsdb.interfaces.model.functions import get_model_record
  from mindsdb.interfaces.storage.json import get_json_storage
  from mindsdb.utilities.functions import cast_row_types
  from .functions import run_finetune, run_learn
label_desc: Use image node_id 11 for calling the LightwoodHandler obj's underlying member method code with example usage: obj._get_ensemble_data() and returns: pd