code: def rating_true():
      return pd.DataFrame(
          {
              DEFAULT_USER_COL: np.random.choice(
                  range(0, DATA_USER_NUM), DATA_SAMPLE_NUM
              ),
              DEFAULT_ITEM_COL: np.random.choice(
                  range(0, DATA_ITEM_NUM), DATA_SAMPLE_NUM
              ),
              DEFAULT_RATING_COL: np.random.choice(
                  range(1, DATA_RATING_MAX + 1), DATA_SAMPLE_NUM
              ),
          }
      )
  
imports: import numpy
  import pandas
  import pytest
  import random
  from sklearn.preprocessing import minmax_scale
  from recommenders.utils.timer import Timer
  from recommenders.utils.constants import DEFAULT_USER_COL, DEFAULT_ITEM_COL, DEFAULT_RATING_COL, DEFAULT_PREDICTION_COL, SEED
  from recommenders.evaluation.python_evaluation import 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, auc, logloss
label_desc: Use image node_id 1 for calling a global function with example usage: rating_true() and returns: pd