code: def rating_pred_binary(rating_pred):
      # Normalize the predictions
      rating_pred[DEFAULT_PREDICTION_COL] = minmax_scale(
          rating_pred[DEFAULT_PREDICTION_COL].astype(float)
      )
      return rating_pred
  
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 4 for calling a global function with example usage: rating_pred_binary(rating_pred) and returns: rating_pred