code: def _get_features_info(self):
      ai_info = self.model_storage.json_get("json_ai")
      if ai_info == {}:
          raise Exception(
              "predictor doesn't contain enough data to generate 'feature' attribute."
          )
      data = []
      dtype_dict = ai_info["dtype_dict"]
      for column in dtype_dict:
          c_data = []
          c_data.append(column)
          c_data.append(dtype_dict[column])
          c_data.append(ai_info["encoders"][column]["module"])
          if (
              ai_info["encoders"][column]["args"].get(
                  "is_target", "False"
              )
              == "True"
          ):
              c_data.append("target")
          else:
              c_data.append("feature")
          data.append(c_data)
  
      return pd.DataFrame(
          data, columns=["column", "type", "encoder", "role"]
      )
  
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 9 for calling the LightwoodHandler obj's underlying member method code with example usage: obj._get_features_info() and returns: pd