code: def recommend_k_fastai(
      model, test, train, top_k=DEFAULT_K, remove_seen=True
  ):
      with Timer() as t:
          (
              total_users,
              total_items,
          ) = model.data.train_ds.x.classes.values()
          total_items = total_items[1:]
          total_users = total_users[1:]
          test_users = test[DEFAULT_USER_COL].unique()
          test_users = np.intersect1d(test_users, total_users)
          users_items = cartesian_product(test_users, total_items)
          users_items = pd.DataFrame(
              users_items, columns=[DEFAULT_USER_COL, DEFAULT_ITEM_COL]
          )
          training_removed = pd.merge(
              users_items,
              train.astype(str),
              on=[DEFAULT_USER_COL, DEFAULT_ITEM_COL],
              how="left",
          )
          training_removed = training_removed[
              training_removed[DEFAULT_RATING_COL].isna()
          ][[DEFAULT_USER_COL, DEFAULT_ITEM_COL]]
          topk_scores = score(
              model,
              test_df=training_removed,
              user_col=DEFAULT_USER_COL,
              item_col=DEFAULT_ITEM_COL,
              prediction_col=DEFAULT_PREDICTION_COL,
              top_k=top_k,
          )
      return topk_scores, t
  
imports: import os
  import numpy
  import pandas
  from tempfile import TemporaryDirectory
  import surprise
  import cornac
  from recommenders.utils.timer import Timer
  from recommenders.utils.constants import COL_DICT, DEFAULT_K, DEFAULT_USER_COL, DEFAULT_ITEM_COL, DEFAULT_RATING_COL, DEFAULT_PREDICTION_COL, DEFAULT_TIMESTAMP_COL, SEED
  from recommenders.models.sar import SAR
  from recommenders.models.surprise.surprise_utils import predict, compute_ranking_predictions
  from recommenders.models.cornac.cornac_utils import predict_ranking
  from recommenders.evaluation.python_evaluation import map, ndcg_at_k, precision_at_k, recall_at_k
  from recommenders.evaluation.python_evaluation import rmse, mae, rsquared, exp_var
label_desc: Use image node_id 14 for calling a global function with example usage: recommend_k_fastai(model, test, train, top_k, remove_seen) and returns: topk_scores,  t