code: def test_diversity_item_feature_vector(spark_diversity_data):
      train_df, reco_df, item_feature_df = spark_diversity_data
      evaluator = SparkDiversityEvaluation(
          train_df=train_df,
          reco_df=reco_df,
          item_feature_df=item_feature_df,
          item_sim_measure="item_feature_vector",
          col_user="UserId",
          col_item="ItemId",
      )
      assert evaluator.diversity() == pytest.approx(0.5000, TOL)
  
imports: import pytest
  import numpy
  import pandas
  from pandas.util.testing import assert_frame_equal
  from recommenders.evaluation.python_evaluation import precision_at_k, recall_at_k, ndcg_at_k, map_at_k, map
label_desc: Use image node_id 25 for calling a global function with example usage: test_diversity_item_feature_vector(spark_diversity_data) without return types