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
  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 2 for calling a global function with example usage: rating_pred_binary(rating_pred) and returns: rating_pred