code: def _get_top_k_items(
      dataframe,
      col_user=DEFAULT_USER_COL,
      col_item=DEFAULT_ITEM_COL,
      col_rating=DEFAULT_RATING_COL,
      col_prediction=DEFAULT_PREDICTION_COL,
      k=DEFAULT_K,
  ):
      """Get the input customer-item-rating tuple in the format of Spark
      DataFrame, output a Spark DataFrame in the dense format of top k items
      for each user.
  
      .. note::
          if it is implicit rating, just append a column of constants to be ratings.
  
      Args:
          dataframe (pyspark.sql.DataFrame): DataFrame of rating data (in the format of
          customerID-itemID-rating tuple).
          col_user (str): column name for user.
          col_item (str): column name for item.
          col_rating (str): column name for rating.
          col_prediction (str): column name for prediction.
          k (int): number of items for each user.
  
      Return:
          pyspark.sql.DataFrame: DataFrame of top k items for each user.
      """
      window_spec = Window.partitionBy(col_user).orderBy(
          col(col_rating).desc()
      )
  
      # this does not work for rating of the same value.
      items_for_user = (
          dataframe.select(
              col_user,
              col_item,
              col_rating,
              row_number().over(window_spec).alias("rank"),
          )
          .where(col("rank") <= k)
          .groupby(col_user)
          .agg(F.collect_list(col_item).alias(col_prediction))
      )
  
      return items_for_user
  
imports: import numpy
  from recommenders.utils.constants import DEFAULT_PREDICTION_COL, DEFAULT_USER_COL, DEFAULT_ITEM_COL, DEFAULT_RATING_COL, DEFAULT_RELEVANCE_COL, DEFAULT_SIMILARITY_COL, DEFAULT_ITEM_FEATURES_COL, DEFAULT_ITEM_SIM_MEASURE, DEFAULT_TIMESTAMP_COL, DEFAULT_K, DEFAULT_THRESHOLD
label_desc: Use image node_id 1 for calling a global function with example usage: _get_top_k_items(dataframe, col_user, col_item, col_rating, col_prediction, k) and returns: items_for_user