code: def diversity_data():
      train_df = pd.DataFrame(
          {
              "UserId": [1, 1, 1, 2, 2, 3, 3, 3],
              "ItemId": [1, 2, 4, 3, 4, 3, 4, 5],
          }
      )
  
      reco_df = pd.DataFrame(
          {
              "UserId": [1, 1, 2, 2, 3, 3],
              "ItemId": [3, 5, 2, 5, 1, 2],
              "Relevance": [1, 0, 1, 1, 1, 0],
          }
      )
  
      item_feature_df = pd.DataFrame(
          {
              "ItemId": [1, 2, 3, 4, 5],
              "features": [
                  np.array([0.0, 1.0, 1.0, 0.0, 0.0], dtype=float),
                  np.array([0.0, 1.0, 0.0, 1.0, 0.0], dtype=float),
                  np.array([0.0, 0.0, 1.0, 1.0, 0.0], dtype=float),
                  np.array([0.0, 0.0, 1.0, 0.0, 1.0], dtype=float),
                  np.array([0.0, 0.0, 0.0, 1.0, 1.0], dtype=float),
              ],
          }
      )
  
      return train_df, reco_df, item_feature_df
  
file_path: recommenders/tests/unit/recommenders/evaluation/conftest.py
imports: import numpy
  import pandas
  import pytest
  from recommenders.utils.constants import DEFAULT_USER_COL, DEFAULT_ITEM_COL, DEFAULT_RATING_COL, DEFAULT_PREDICTION_COL
label_desc: Use image node_id 4 for calling a global function with example usage: diversity_data() and returns: train_df,  reco_df,  item_feature_df