code: def test_get_top_k_items_largek(rating_true):
      top_6_items_df = get_top_k_items(
          dataframe=rating_true,
          col_user=DEFAULT_USER_COL,
          col_rating=DEFAULT_RATING_COL,
          k=6,
      )
      top_6_user_true = pd.Series(
          [1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3]
      )
      top_6_rating_true = pd.Series(
          [5, 4, 3, 5, 5, 3, 3, 1, 5, 5, 5, 4, 4, 3]
      )
      top_6_rank_true = pd.Series(
          [1, 2, 3, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 6]
      )
      assert top_6_items_df[DEFAULT_USER_COL].equals(top_6_user_true)
      assert top_6_items_df[DEFAULT_RATING_COL].equals(
          top_6_rating_true
      )
      assert top_6_items_df["rank"].equals(top_6_rank_true)
      assert top_6_items_df[DEFAULT_ITEM_COL][:3].equals(
          pd.Series([1, 2, 3])
      )
      # First two itemIDs of user 2. The scores are both 5, so any order is OK.
      assert set(top_6_items_df[DEFAULT_ITEM_COL][3:5]) == set([1, 4])
      # Third and fourth itemID of user 2. The scores are both 3, so any order is OK.
      assert set(top_6_items_df[DEFAULT_ITEM_COL][5:7]) == set([5, 6])
      assert top_6_items_df[DEFAULT_ITEM_COL][7] == 7
      # First three itemIDs of user 3. The scores are both 5, so any order is OK.
      assert set(top_6_items_df[DEFAULT_ITEM_COL][8:11]) == set(
          [2, 5, 6]
      )
      # Fourth and fifth itemID of user 3. The scores are both 4, so any order is OK.
      assert set(top_6_items_df[DEFAULT_ITEM_COL][11:13]) == set([8, 9])
      # Sixth itemID of user 3. Item 10,11,12 have a score of 3, so either one is OK.
      assert top_6_items_df[DEFAULT_ITEM_COL][13] in [10, 11, 12]
  
imports: import numpy
  import pandas
  import pytest
  from unittest.mock import Mock
  from sklearn.preprocessing import minmax_scale
  from pandas.testing import assert_frame_equal
  from recommenders.utils.constants import DEFAULT_USER_COL, DEFAULT_ITEM_COL, DEFAULT_RATING_COL, DEFAULT_PREDICTION_COL
  from recommenders.evaluation.python_evaluation import _check_column_dtypes, merge_rating_true_pred, merge_ranking_true_pred, rmse, mae, rsquared, exp_var, get_top_k_items, precision_at_k, recall_at_k, ndcg_at_k, map_at_k, map, auc, logloss, user_diversity, diversity, historical_item_novelty, novelty, user_item_serendipity, user_serendipity, serendipity, catalog_coverage, distributional_coverage, ColumnMismatchError, ColumnTypeMismatchError
label_desc: Use image node_id 11 for calling a global function with example usage: test_get_top_k_items_largek(rating_true) without return types