class_docstr: This integration seamlessly combines MindsDB and Leonardo AI to create a powerful
  AI-driven solution for creative content generation.
  
  Content Generation with Leonardo AI: Harness the power of advanced generative
  models for creative content production. From realistic images to artistic text, Leonardo
  AI opens up new possibilities for content creators.
class_docstr_tok: ["This","integration","seamlessly","combines","MindsDB","and","Leonardo","AI","to","create","a","powerful","AI-driven","solution","for","creative","content","generation",".","Content","Generation","with","Leonardo","AI",":","Harness","the","power","of","advanced","generative","models","for","creative","content","production",".","From","realistic","images","to","artistic","text",",","Leonardo","AI","opens","up","new","possibilities","for","content","creators","."]
code: def describe(self, attribute: Optional[str] = None) -> pd.DataFrame:
      args = self.model_storage.json_get("args")
      model, target = args["using"]["model"], args["target"]
      prompt_template = args.get(
          "prompt_template", "Generate a picture of {{{{text}}}}"
      )
  
      if attribute == "features":
          return pd.DataFrame(
              [[target, prompt_template]],
              columns=["target_column", "mindsdb_prompt_template"],
          )
      elif attribute == "metadata":
          api_key = self._get_leonardo_api_key(
              args, self.engine_storage
          )
          return pd.DataFrame(
              [[target, api_key, model]],
              columns=["target", "api_key", "model_name"],
          )
      else:
          tables = ["args", "api_key"]
          return pd.DataFrame(tables, columns=["tables"])
  
file_path: mindsdb/mindsdb/integrations/handlers/leonardoai_handler/leonardo_ai_handler.py
imports: import os
  import requests
  import time
  from typing import Dict, Optional
  from mindsdb.integrations.libs.llm_utils import get_completed_prompts
  import pandas
  from mindsdb.integrations.libs.base import BaseMLEngine
  from mindsdb.utilities import log
  from mindsdb.interfaces.storage.model_fs import HandlerStorage
  from mindsdb.utilities.config import Config
label_desc: Use image node_id 7 for calling the LeonardoAIHandler obj's underlying member method code with example usage: obj.describe(attribute) and returns: pd, pd, pd