code: def rmse(
      rating_true,
      rating_pred,
      col_user=DEFAULT_USER_COL,
      col_item=DEFAULT_ITEM_COL,
      col_rating=DEFAULT_RATING_COL,
      col_prediction=DEFAULT_PREDICTION_COL,
  ):
      """Calculate Root Mean Squared Error
  
      Args:
          rating_true (pandas.DataFrame): True data. There should be no duplicate (userID, itemID) pairs
          rating_pred (pandas.DataFrame): Predicted data. There should be no duplicate (userID, itemID) pairs
          col_user (str): column name for user
          col_item (str): column name for item
          col_rating (str): column name for rating
          col_prediction (str): column name for prediction
  
      Returns:
          float: Root mean squared error
      """
  
      y_true, y_pred = merge_rating_true_pred(
          rating_true=rating_true,
          rating_pred=rating_pred,
          col_user=col_user,
          col_item=col_item,
          col_rating=col_rating,
          col_prediction=col_prediction,
      )
      return np.sqrt(mean_squared_error(y_true, y_pred))
  
imports: import numpy
  import pandas
  from functools import wraps
  from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, explained_variance_score, roc_auc_score, log_loss
  from recommenders.utils.constants import DEFAULT_USER_COL, DEFAULT_ITEM_COL, DEFAULT_RATING_COL, DEFAULT_PREDICTION_COL, DEFAULT_RELEVANCE_COL, DEFAULT_SIMILARITY_COL, DEFAULT_ITEM_FEATURES_COL, DEFAULT_ITEM_SIM_MEASURE, DEFAULT_K, DEFAULT_THRESHOLD
  from recommenders.datasets.pandas_df_utils import has_columns, has_same_base_dtype, lru_cache_df
label_desc: Use image node_id 3 for calling a global function with example usage: rmse(rating_true, rating_pred, col_user, col_item, col_rating, col_prediction) and returns: np