code: def test_diversity(diversity_data):
      train_df, reco_df, _ = diversity_data
      assert diversity(
          train_df=train_df,
          reco_df=reco_df,
          item_feature_df=None,
          item_sim_measure="item_cooccurrence_count",
          col_user="UserId",
          col_item="ItemId",
          col_sim="sim",
          col_relevance=None,
      ) == pytest.approx(0.43096, TOL)
  
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 25 for calling a global function with example usage: test_diversity(diversity_data) without return types