code: def recommend_k_als(
      model, test, train, top_k=DEFAULT_K, remove_seen=True
  ):
      with Timer() as t:
          # Get the cross join of all user-item pairs and score them.
          users = train.select(DEFAULT_USER_COL).distinct()
          items = train.select(DEFAULT_ITEM_COL).distinct()
          user_item = users.crossJoin(items)
          dfs_pred = model.transform(user_item)
  
          # Remove seen items
          dfs_pred_exclude_train = dfs_pred.alias("pred").join(
              train.alias("train"),
              (dfs_pred[DEFAULT_USER_COL] == train[DEFAULT_USER_COL])
              & (dfs_pred[DEFAULT_ITEM_COL] == train[DEFAULT_ITEM_COL]),
              how="outer",
          )
          topk_scores = dfs_pred_exclude_train.filter(
              dfs_pred_exclude_train[
                  "train." + DEFAULT_RATING_COL
              ].isNull()
          ).select(
              "pred." + DEFAULT_USER_COL,
              "pred." + DEFAULT_ITEM_COL,
              "pred." + DEFAULT_PREDICTION_COL,
          )
      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 5 for calling a global function with example usage: recommend_k_als(model, test, train, top_k, remove_seen) and returns: topk_scores,  t