code: def get_top_k_items(
      dataframe,
      col_user=DEFAULT_USER_COL,
      col_rating=DEFAULT_RATING_COL,
      k=DEFAULT_K,
  ):
      """Get the input customer-item-rating tuple in the format of Pandas
      DataFrame, output a Pandas 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 (pandas.DataFrame): DataFrame of rating data (in the format
          customerID-itemID-rating)
          col_user (str): column name for user
          col_rating (str): column name for rating
          k (int or None): number of items for each user; None means that the input has already been
          filtered out top k items and sorted by ratings and there is no need to do that again.
  
      Returns:
          pandas.DataFrame: DataFrame of top k items for each user, sorted by `col_user` and `rank`
      """
      # Sort dataframe by col_user and (top k) col_rating
      if k is None:
          top_k_items = dataframe
      else:
          top_k_items = (
              dataframe.sort_values(
                  [col_user, col_rating], ascending=[True, False]
              )
              .groupby(col_user, as_index=False)
              .head(k)
              .reset_index(drop=True)
          )
      # Add ranks
      top_k_items["rank"] = (
          top_k_items.groupby(col_user, sort=False).cumcount() + 1
      )
      return top_k_items
  
imports: import numpy
  import pandas
  from functools import wraps
  from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, explained_variance_score, roc_auc_score, log_loss
  from recommenders.utils.constants import DEFAULT_USER_COL, DEFAULT_ITEM_COL, DEFAULT_RATING_COL, DEFAULT_PREDICTION_COL, DEFAULT_RELEVANCE_COL, DEFAULT_SIMILARITY_COL, DEFAULT_ITEM_FEATURES_COL, DEFAULT_ITEM_SIM_MEASURE, DEFAULT_K, DEFAULT_THRESHOLD
  from recommenders.datasets.pandas_df_utils import has_columns, has_same_base_dtype, lru_cache_df
label_desc: Use image node_id 16 for calling a global function with example usage: get_top_k_items(dataframe, col_user, col_rating, k) and returns: top_k_items