code: def describe(self, attribute: Optional[str] = None) -> pd.DataFrame:
      if attribute == "info":
          model_description = {}
  
          model_info = self.model_storage.get_info()
          model_data = model_info["data"]
          to_predict = model_info["to_predict"][0]
  
          if model_data.get("accuracies", None) is not None:
              if len(model_data["accuracies"]) > 0:
                  model_data["accuracy"] = float(
                      np.mean(list(model_data["accuracies"].values()))
                  )
  
          model_columns = self.model_storage.columns_get()
  
          model_description["accuracies"] = model_data["accuracies"]
          model_description["column_importances"] = model_data[
              "column_importances"
          ]
          model_description["outputs"] = [to_predict]
          model_description["inputs"] = [
              col
              for col in model_columns
              if col not in model_description["outputs"]
          ]
  
          return pd.DataFrame([model_description])
  
      elif attribute == "features":
          return self._get_features_info()
  
      elif attribute == "model":
          return self._get_model_info()
  
      elif attribute == "jsonai":
          return self._get_ensemble_data()
  
      elif attribute == "progress":
          # todo remove?
          return self._get_progress_data()
  
      else:
          tables = ["info", "features", "model", "jsonai"]
          return pd.DataFrame(tables, columns=["tables"])
  
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 13 for calling the LightwoodHandler obj's underlying member method code with example usage: obj.describe(attribute) and returns: pd, self, self, self, self, pd