class_docstr: Integration with the lightfm Recommender library.
code: def describe(self, attribute=None):
      model_args = self.model_storage.json_get("args")
  
      if attribute == "model":
          return pd.DataFrame(
              {k: [model_args[k]] for k in ["model_params"]}
          )
  
      elif attribute == "features":
          return pd.DataFrame(
              {
                  "n_users_items": [model_args["n_users_items"]],
              }
          )
  
      elif attribute == "info":
          model_metrics = model_args["evaluation_metrics"]
  
          info_dict = {
              k: [model_metrics[k]]
              for k in ["auc", "precision", "recall"]
          }
          info_dict["user_id"] = [model_args["user_id"]]
          info_dict["item_id"] = [model_args["item_id"]]
  
          return pd.DataFrame(info_dict)
  
      else:
          tables = ["info", "features", "model"]
          return pd.DataFrame(tables, columns=["tables"])
  
file_path: mindsdb/mindsdb/integrations/handlers/lightfm_handler/lightfm_handler.py
imports: from typing import Dict, Optional
  import dill
  import pandas
  from dataprep_ml.recommenders import RecommenderPreprocessor
  from lightfm import LightFM
  from lightfm.cross_validation import random_train_test_split
  from lightfm.evaluation import auc_score, precision_at_k, recall_at_k
  from mindsdb.integrations.handlers.lightfm_handler.helpers import get_item_item_recommendations, get_user_item_recommendations
  from mindsdb.integrations.handlers.lightfm_handler.settings import ModelParameters
  from mindsdb.integrations.libs.base import BaseMLEngine
label_desc: Use image node_id 3 for calling the LightFMHandler obj's underlying member method code with example usage: obj.describe(attribute) and returns: pd, pd, pd, pd